subject:(Statistical Image Reconstruction)

University of Michigan

1. Long, Yong. Statistical Image Reconstruction and Motion Estimation for Image-Guided Radiotherapy.

Degree: PhD, Electrical Engineering: Systems, 2011, University of Michigan

URL: http://hdl.handle.net/2027.42/86254

► Image reconstruction and motion estimation are very important for image-guided radiotherapy (IGRT). Three-dimensional reconstruction of patient anatomy using X-ray computed tomography (CT) allows identification of… (more)

▼ Image reconstruction and motion estimation are very important for image-guided radiotherapy (IGRT). Three-dimensional reconstruction of patient anatomy using X-ray computed tomography (CT) allows identification of the location of a tumor prior to treatment. The locations of tumorsmay change during actual treatment due to movement such as respiratory motion. Motion estimation helps optimize the accuracy and precision of radiotherapy so that more of the normal surrounding tissue can be spared. This dissertation addresses several important issues related to these two core components of IGRT. Firstly, we developed two new separable footprint (SF) projector methods for X-ray conebeam CT. The SF projectors approximate the voxel footprint functions as 2D separable functions. The SF-TR projector uses trapezoid functions in the transaxial direction and rectangular functions in the axial direction, whereas the SF-TT projector uses trapezoid functions in both directions. Both SF projector methods are more accurate than the distance-driven (DD) projector, which is a current state-of-the-art method in the field. The SF-TT projector is more accurate than the SF-TR projector for rays associated with large cone angles. In addition, the SF-TR projector has similar computation speed with the DD projector and the SF-TT projector is about two times slower. Secondly, we proposed a statistical penalized weighted least-squares (PWLS) method with edge-preserving regularization to reconstruct two basis materials from a single-energy CT scan acquired with differential filtration, such as a split filter or a bow-tie filter. It requires only the use of suitable filters between the X-ray tube and the patient. For both filtration methods, the proposed PWLS method reconstructed soft tissue and bone images with lower RMS errors, reduced the beam-hardening artifacts much more effectively and produced lower noise, as compared with the traditional non-iterative Joseph and Spital method. Thirdly, we conducted an objective characterization of the influence of rotational arc length on accuracy of motion estimation for projection-to-volume targeting during rotational therapy. Simulations illustrate the potential accuracy of limited-angle projection-to-volume alignment. Registration accuracy can be sensitive to angular center, tends to be lower along direction of the projection set, and tends to decrease away from the rotation center. Advisors/Committee Members: Balter, James M. (committee member), Fessler, Jeffrey A. (committee member), Clinthorne, Neal H. (committee member), Hero Iii, Alfred O. (committee member), Meyer, Charles R. (committee member).

Subjects/Keywords: X-Ray CT; Statistical Image Reconstruction; Image-Guided Radiotherapy; Image Registration; Forward and Back-projection; Dual-Energy CT; Electrical Engineering; Engineering

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Long, Y. (2011). Statistical Image Reconstruction and Motion Estimation for Image-Guided Radiotherapy. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/86254

Chicago Manual of Style (16^th Edition):

Long, Yong. “Statistical Image Reconstruction and Motion Estimation for Image-Guided Radiotherapy.” 2011. Doctoral Dissertation, University of Michigan. Accessed January 20, 2021. http://hdl.handle.net/2027.42/86254.

MLA Handbook (7^th Edition):

Long, Yong. “Statistical Image Reconstruction and Motion Estimation for Image-Guided Radiotherapy.” 2011. Web. 20 Jan 2021.

Vancouver:

Long Y. Statistical Image Reconstruction and Motion Estimation for Image-Guided Radiotherapy. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2027.42/86254.

Council of Science Editors:

Long Y. Statistical Image Reconstruction and Motion Estimation for Image-Guided Radiotherapy. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/86254

University of Florida

2. Posirca, Iulia M. Variational Models for Simultaneous Image Segmentation and Noise Removal.

Degree: PhD, Mathematics, 2012, University of Florida

URL: https://ufdc.ufl.edu/UFE0044953

► We present two projects for simultaneous image segmentation and noise removal. The first project concerns the images corrupted with Gaussian noise and the second one… (more)

Subjects/Keywords: Data smoothing; Image processing; Image reconstruction; Imaging; Mathematics; Modeling; Pixels; Statistical models; Supernova remnants; Ultrasonography; image – noise – segmentation – variational

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Posirca, I. M. (2012). Variational Models for Simultaneous Image Segmentation and Noise Removal. (Doctoral Dissertation). University of Florida. Retrieved from https://ufdc.ufl.edu/UFE0044953

Chicago Manual of Style (16^th Edition):

Posirca, Iulia M. “Variational Models for Simultaneous Image Segmentation and Noise Removal.” 2012. Doctoral Dissertation, University of Florida. Accessed January 20, 2021. https://ufdc.ufl.edu/UFE0044953.

MLA Handbook (7^th Edition):

Posirca, Iulia M. “Variational Models for Simultaneous Image Segmentation and Noise Removal.” 2012. Web. 20 Jan 2021.

Vancouver:

Posirca IM. Variational Models for Simultaneous Image Segmentation and Noise Removal. [Internet] [Doctoral dissertation]. University of Florida; 2012. [cited 2021 Jan 20]. Available from: https://ufdc.ufl.edu/UFE0044953.

Council of Science Editors:

Posirca IM. Variational Models for Simultaneous Image Segmentation and Noise Removal. [Doctoral Dissertation]. University of Florida; 2012. Available from: https://ufdc.ufl.edu/UFE0044953

University of Manitoba

3. Teimoorisichani, Mohammadreza. Geometry optimization and evaluation of PET inserts for simultaneous PET/MR neuroimaging.

Degree: Physics and Astronomy, 2019, University of Manitoba

URL: http://hdl.handle.net/1993/34107

► A positron emission tomography (PET) insert with a compact ring diameter is currently under design by our group. The PET insert is a brain-dedicated scanner… (more)

▼ A positron emission tomography (PET) insert with a compact ring diameter is currently under design by our group. The PET insert is a brain-dedicated scanner which is designed to retrofit into the Siemens Magnetom 7T brain Magnetic Resonance (MR) scanner. A dual-layer offset (DLO) design is considered for the detectors of the PET insert to provide depth-of-interaction information and reduce resolution degradation due to parallax error. A wide range of detector geometries was evaluated through Monte Carlo simulations to characterize the performance of each detector and study the optimum detector geometry for the PET insert. More than 200 DLO detectors with a scintillation material of LYSO and detector thicknesses between 10 to 30 mm, different layer thickness ratios and crystal sizes of 0.5 to 4 mm were evaluated in this study. The effects of each detector geometry on radial mispositioning of the incident photons, coincidence response functions, scanner sensitivity and scanner resolution were studied. The effects of layer-misassignment in DLO detectors due to inter-crystal scatter were also studied. The count rate performance of several scanners with different detector sizes and thicknesses were evaluated to provide a comprehensive model for the count rate performance of each scanner with a wide range of deadtime properties. A scalable dual-GPU list-mode image reconstruction algorithm has also been developed, which was used in the evaluation of different brain PET scanners. Based on the results of this work, a single-ended readout DLO detector with an area of between 1.5 to 9.6 cm2, a total/front/back layer thickness of 15/6/9 or 20/8/12 mm, and a crystal size of 2 to 3 mm is recommended. Advisors/Committee Members: Goertzen, Andrew (Physics and Astronomy) (supervisor), Pistorius, Stephen (Physics and Astronomy, University of Manitoba) (examiningcommittee), Ko, Ji Hyuan (Human Anatomy and Cell Science, University of Manitoba) (examiningcommittee), Fiege, Jason (Physics and Astronomy, University of Manitoba) (examiningcommittee), Badawi, Ramsey (Biomedical Engineering, University of California, Davis) (examiningcommittee).

Subjects/Keywords: PET; Positron emission tomography; Statistical image reconstruction; Scintillation detector; Depth-of-interaction; Dual-layer offset

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Teimoorisichani, M. (2019). Geometry optimization and evaluation of PET inserts for simultaneous PET/MR neuroimaging. (Thesis). University of Manitoba. Retrieved from http://hdl.handle.net/1993/34107

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Teimoorisichani, Mohammadreza. “Geometry optimization and evaluation of PET inserts for simultaneous PET/MR neuroimaging.” 2019. Thesis, University of Manitoba. Accessed January 20, 2021. http://hdl.handle.net/1993/34107.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Teimoorisichani, Mohammadreza. “Geometry optimization and evaluation of PET inserts for simultaneous PET/MR neuroimaging.” 2019. Web. 20 Jan 2021.

Vancouver:

Teimoorisichani M. Geometry optimization and evaluation of PET inserts for simultaneous PET/MR neuroimaging. [Internet] [Thesis]. University of Manitoba; 2019. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/1993/34107.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Teimoorisichani M. Geometry optimization and evaluation of PET inserts for simultaneous PET/MR neuroimaging. [Thesis]. University of Manitoba; 2019. Available from: http://hdl.handle.net/1993/34107

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Notre Dame

4. Zhiqian Chang. Statistical Reconstruction and Simultaneous Parameter Estimation for Iterative X-ray Computed Tomography</h1>.

Degree: Electrical Engineering, 2017, University of Notre Dame

URL: https://curate.nd.edu/show/41687h16c89

► Statistical iterative image reconstruction methods for X-ray computed tomography (CT) have, over the past few years, shown promise in maintaining diagnostic image quality across… (more)

▼ Statistical iterative image reconstruction methods for X-ray computed tomography (CT) have, over the past few years, shown promise in maintaining diagnostic image quality across a wider range of dosage than has been routinely practical with standard deterministic methods. In this dissertation, iterative reconstruction techniques are modified to attack three limited-information problems: (i) photon starvation due to low signals; (ii) artifacts induced by high attenuation objects and (iii) spatially non-uniform sampling geometry. Dose reduction in clinical X-ray CT causes low signal-to-noise ratio (SNR) in photon-sparse situations. All techniques meet their limits of practicality when significant portions of the sinogram are near photon starvation. The corruption of electronic noise leads to measured photon counts’ taking on negative values, posing a problem for the log() operation in pre-processing of data. We propose two categories of methods for extremely low-count sinogram pretreatment: an adaptive denoising filter and a pointwise Bayesian inference method. The denoising filter is easy to implement and preserves local statistics, but it introduces correlation between channels and may affect image resolution. The Bayesian inference is a pointwise estimate incorporating a prior model for Poisson rates. Both approaches achieve significant improvements in diagnostic image quality at dramatically reduced dosage. High-attenuation materials pose significant challenges to CT imaging. Formed of high mass density and high atomic number elements, bones and metals, for example, have high resistance to transmission of photons. Scatter and beam hardening effects are more prominent, raising substantially the importance of the nonlinear relation between line-integral projection estimates and path lengths. As a result, streaking artifacts often appear in reconstructed images along high density directions. In this dissertation, two novel iterative approaches are proposed to reduce such artifacts. One method parameterizes scatter and beam hardening as a locally varying additive Poisson noise, and attempts to estimate the offset as part of the iterative reconstruction loop. The other method uses a prior image to guide both reconstruction and sinogram correction. Artifacts are significantly reduced at little cost in resolution loss. Cone-beam geometry creates non-uniform spatial sampling rates, which becomes a more pronounced issue as scanners are extended to wider cone angles. While in new, wider-coverage detectors, noise non-uniformity can be mitigated by spatially adaptive regularization design, some data dependent systematic errors are difficult to model deterministically. The detector array is adjusted to accommodate detection efficiency issue at large cone angles. Array modules are compromised to be tilted towards the source, which causes systematic inconsistencies between two module boundaries. To combat the challenge of increased sampling non-uniformity, we propose a joint system parameter estimation with… Advisors/Committee Members: Robert Stevenson, Committee Member, Jean-Baptiste Thibault, Committee Member, Ken Sauer, Research Director, Charles Bouman, Committee Member.

Subjects/Keywords: Adaptive Regularization; Joint Estimation; Statistical Modeling; Computed Tomography; Image Reconstruction; Bayesian Inference

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chang, Z. (2017). Statistical Reconstruction and Simultaneous Parameter Estimation for Iterative X-ray Computed Tomography</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/41687h16c89

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chang, Zhiqian. “Statistical Reconstruction and Simultaneous Parameter Estimation for Iterative X-ray Computed Tomography</h1>.” 2017. Thesis, University of Notre Dame. Accessed January 20, 2021. https://curate.nd.edu/show/41687h16c89.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chang, Zhiqian. “Statistical Reconstruction and Simultaneous Parameter Estimation for Iterative X-ray Computed Tomography</h1>.” 2017. Web. 20 Jan 2021.

Vancouver:

Chang Z. Statistical Reconstruction and Simultaneous Parameter Estimation for Iterative X-ray Computed Tomography</h1>. [Internet] [Thesis]. University of Notre Dame; 2017. [cited 2021 Jan 20]. Available from: https://curate.nd.edu/show/41687h16c89.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chang Z. Statistical Reconstruction and Simultaneous Parameter Estimation for Iterative X-ray Computed Tomography</h1>. [Thesis]. University of Notre Dame; 2017. Available from: https://curate.nd.edu/show/41687h16c89

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Michigan

5. Ahn, Sangtae. Convergent algorithms for statistical image reconstruction in emission tomography.

Degree: PhD, Electrical engineering, 2004, University of Michigan

URL: http://hdl.handle.net/2027.42/124609

► Emission computed tomography (ECT), including positron emission tomography (PET) and single photon emission computed tomography (SPECT), is a medical imaging technique that provides functional information… (more)

▼ Emission computed tomography (ECT), including positron emission tomography (PET) and single photon emission computed tomography (SPECT), is a medical imaging technique that provides functional information about physiological processes. The goal of ECT is to reconstruct the distribution of the radioisotopes in the body by measuring the emitted photons. Statistical image reconstruction methods have shown improved image quality over conventional nonstatistical methods by using accurate physical models and appropriate noise models. However, statistical methods require huge computation and complex modeling. So computationally efficient algorithms and simple yet accurate statistical models are essential. First, we develop fast and convergent algorithms for statistical image reconstruction. Ordered subsets or incremental gradient type algorithms have been popular due to their fast initial convergence rates. However, they do not converge to a solution in general. We achieve global convergence by two methods: relaxation and incremental optimization transfer principles. Those two families of algorithms are provably convergent yet converge fast. We apply the algorithms to emission and transmission tomography and to simulation and real PET data. Secondly, we develop statistical models for randoms-precorrected PET. Accidental coincidence (AC) events, or randoms, are one of primary sources of background noise in PET. Most PET scanners are corrected for AC events by real-time subtraction of delayed window coincidences. Although the randoms-precorrection compensates in mean for AC events but destroys the usual Poisson statistics, complicating statistical reconstruction. We propose new likelihood approximations that allow negative sinogram values without requiring zero-thresholding. Analysis and simulation results show that the new statistical model is nearly free of systematic bias yet keeps low variance. Finally, we analyze the parameterization of time activity curves (TACs) in dynamic imaging. We provide approximate expressions for the covariance matrix of kinetic parameter estimators based on TAC reconstructions when TACs are modeled as a linear combination of temporal basis functions such as B-splines. The approximations are useful tools for assessing and optimizing the temporal basis functions for TACs. Advisors/Committee Members: Fessler, Jeffrey A. (advisor).

Subjects/Keywords: Convergent Algorithms; Emission Tomography; Medical Imaging; Statistical Image Reconstruction

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ahn, S. (2004). Convergent algorithms for statistical image reconstruction in emission tomography. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/124609

Chicago Manual of Style (16^th Edition):

Ahn, Sangtae. “Convergent algorithms for statistical image reconstruction in emission tomography.” 2004. Doctoral Dissertation, University of Michigan. Accessed January 20, 2021. http://hdl.handle.net/2027.42/124609.

MLA Handbook (7^th Edition):

Ahn, Sangtae. “Convergent algorithms for statistical image reconstruction in emission tomography.” 2004. Web. 20 Jan 2021.

Vancouver:

Ahn S. Convergent algorithms for statistical image reconstruction in emission tomography. [Internet] [Doctoral dissertation]. University of Michigan; 2004. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2027.42/124609.

Council of Science Editors:

Ahn S. Convergent algorithms for statistical image reconstruction in emission tomography. [Doctoral Dissertation]. University of Michigan; 2004. Available from: http://hdl.handle.net/2027.42/124609

Washington University in St. Louis

6. Zhang, Shuangyue. Basis Vector Model Method for Proton Stopping Power Estimation using Dual-Energy Computed Tomography.

Degree: PhD, Electrical & Systems Engineering, 2018, Washington University in St. Louis

URL: https://openscholarship.wustl.edu/eng_etds/395

► Accurate estimation of the proton stopping power ratio (SPR) is important for treatment planning and dose prediction for proton beam therapy. The state-of-the-art clinical… (more)

▼ Accurate estimation of the proton stopping power ratio (SPR) is important for treatment planning and dose prediction for proton beam therapy. The state-of-the-art clinical practice for estimating patient-specific SPR distributions is the stoichiometric calibration method using single-energy computed tomography (SECT) images, which in principle may introduce large intrinsic uncertainties into estimation results. One major factor that limits the performance of SECT-based methods is the Hounsfield unit (HU) degeneracy in the presence of tissue composition variations. Dual-energy computed tomography (DECT) has shown the potential of reducing uncertainties in proton SPR prediction via scanning the patient with two different source energy spectra. Numerous methods have been studied to estimate the SPR by dual-energy CT DECT techniques using either image-domain or sinogram-domain decomposition approaches. In this work, we implement and evaluate a novel DECT approach for proton SPR mapping, which integrates image reconstruction and material characterization using a joint statistical image reconstruction (JSIR) method based on a linear basis vector model (BVM). This method reconstructs two images of material parameters simultaneously from the DECT measurement data and then uses them to predict the electron densities and the mean excitation energies, which are required by the Bethe equation for computing proton SPR. The proposed JSIR-BVM method is first compared with image-domain and sinogram-domain decomposition approaches based on three available SPR models including the BVM in a well controlled simulation framework that is representative of major uncertainty sources existing in practice. The intrinsic SPR modeling accuracy of the three DECT-SPR models is validated via theoretical computed radiological quantities for various reference human tissues. The achievable performances of the investigated methods in the presence of image formation uncertainties are evaluated using synthetic DECT transmission sinograms of virtual cylindrical phantoms and virtual patients, which consist of reference human tissues with known densities and compositions. The JSIR-BVM method is then experimentally commissioned using the DECT measurement data acquired on a Philips Brilliance Big Bore CT scanner at 90 kVp and 140 kVp for two phantoms of different sizes, each of which contains 12 different soft and bony tissue surrogates. An image-domain decomposition method that utilizes the two HU images reconstructed via the scanner's software is implemented for comparison The JSIR-BVM method outperforms the other investigated methods in both the simulation and experimental settings. Although all investigated DECT-SPR models support low intrinsic modeling errors (i.e., less than 0.2% RMS errors for reference human tissues), the achievable accuracy of the image- and sinogram-domain methods is limited by the image formation uncertainties introduced by the reconstruction and decomposition processes. In contrast, by taking advantage of an… Advisors/Committee Members: Joseph A. O'Sullivan, Mark A. Anastasio, R. M. Arthur, David G. Politte, Tianyu Zhao.

Subjects/Keywords: dual-energy computed tomography; proton stopping power; statistical image reconstruction; Bioimaging and Biomedical Optics; Electrical and Electronics

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zhang, S. (2018). Basis Vector Model Method for Proton Stopping Power Estimation using Dual-Energy Computed Tomography. (Doctoral Dissertation). Washington University in St. Louis. Retrieved from https://openscholarship.wustl.edu/eng_etds/395

Chicago Manual of Style (16^th Edition):

Zhang, Shuangyue. “Basis Vector Model Method for Proton Stopping Power Estimation using Dual-Energy Computed Tomography.” 2018. Doctoral Dissertation, Washington University in St. Louis. Accessed January 20, 2021. https://openscholarship.wustl.edu/eng_etds/395.

MLA Handbook (7^th Edition):

Zhang, Shuangyue. “Basis Vector Model Method for Proton Stopping Power Estimation using Dual-Energy Computed Tomography.” 2018. Web. 20 Jan 2021.

Vancouver:

Zhang S. Basis Vector Model Method for Proton Stopping Power Estimation using Dual-Energy Computed Tomography. [Internet] [Doctoral dissertation]. Washington University in St. Louis; 2018. [cited 2021 Jan 20]. Available from: https://openscholarship.wustl.edu/eng_etds/395.

Council of Science Editors:

Zhang S. Basis Vector Model Method for Proton Stopping Power Estimation using Dual-Energy Computed Tomography. [Doctoral Dissertation]. Washington University in St. Louis; 2018. Available from: https://openscholarship.wustl.edu/eng_etds/395

University of Waterloo

7. Mohebi, Azadeh. Statistical Fusion of Scientific Images.

Degree: 2009, University of Waterloo

URL: http://hdl.handle.net/10012/4582

► A practical and important class of scientific images are the 2D/3D images obtained from porous materials such as concretes, bone, active carbon, and glass. These… (more)

▼ A practical and important class of scientific images are the 2D/3D images obtained from porous materials such as concretes, bone, active carbon, and glass. These materials constitute an important class of heterogeneous media possessing complicated microstructure that is difficult to describe qualitatively. However, they are not totally random and there is a mixture of organization and randomness that makes them difficult to characterize and study. In order to study different properties of porous materials, 2D/3D high resolution samples are required. But obtaining high resolution samples usually requires cutting, polishing and exposure to air, all of which affect the properties of the sample. Moreover, 3D samples obtained by Magnetic Resonance Imaging (MRI) are very low resolution and noisy. Therefore, artificial samples of porous media are required to be generated through a porous media reconstruction process. The recent contributions in the reconstruction task are either only based on a prior model, learned from statistical features of real high resolution training data, and generating samples from that model, or based on a prior model and the measurements. The main objective of this thesis is to some up with a statistical data fusion framework by which different images of porous materials at different resolutions and modalities are combined in order to generate artificial samples of porous media with enhanced resolution. The current super-resolution, multi-resolution and registration methods in image processing fail to provide a general framework for the porous media reconstruction purpose since they are usually based on finding an estimate rather than a typical sample, and also based on having the images from the same scene – the case which is not true for porous media images. The statistical fusion approach that we propose here is based on a Bayesian framework by which a prior model learned from high resolution samples are combined with a measurement model defined based on the low resolution, coarse-scale information, to come up with a posterior model. We define a measurement model, in the non-hierachical and hierarchical image modeling framework, which describes how the low resolution information is asserted in the posterior model. Then, we propose a posterior sampling approach by which 2D posterior samples of porous media are generated from the posterior model. A more general framework that we propose here is asserting other constraints rather than the measurement in the model and then propose a constrained sampling strategy based on simulated annealing to generate artificial samples.

Subjects/Keywords: Statistical Fusion; Porous Media; Image Reconstruction; Sampling; Resolution Enhancement

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Mohebi, A. (2009). Statistical Fusion of Scientific Images. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/4582

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Mohebi, Azadeh. “Statistical Fusion of Scientific Images.” 2009. Thesis, University of Waterloo. Accessed January 20, 2021. http://hdl.handle.net/10012/4582.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Mohebi, Azadeh. “Statistical Fusion of Scientific Images.” 2009. Web. 20 Jan 2021.

Vancouver:

Mohebi A. Statistical Fusion of Scientific Images. [Internet] [Thesis]. University of Waterloo; 2009. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/10012/4582.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mohebi A. Statistical Fusion of Scientific Images. [Thesis]. University of Waterloo; 2009. Available from: http://hdl.handle.net/10012/4582

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Freie Universität Berlin

8. Huizing-Kusitzky, Lea Heleen. Adaptive statistical iterative reconstruction (ASIR) in head CT-improvement of image quality und reduction of radiation dose.

Degree: 2018, Freie Universität Berlin

URL: http://dx.doi.org/10.17169/refubium-8550

► Worldwide, the number of CT scans has risen sharply in recent years, especially in emergency settings. This has resulted in a rapidly increasing exposure of… (more)

▼ Worldwide, the number of CT scans has risen sharply in recent years, especially in emergency settings. This has resulted in a rapidly increasing exposure of humans and the environment to radiation. It is therefore important to explore ways to reduce the amount of radiation required to perform these exams. This study investigated adaptive statistical iterative reconstruction (ASIR) as one option for reducing the radiation dosage while ensuring good image quality in native, cranial CTs. Methodology: 157 patients were divided into 2 protocol groups of 120 kV and modular mAs: Group A (control group) with 100% FBP (filtered back projection), N = 71. Group B1 (case group) with 20% ASIR and 80% FBP, N = 86. The raw data from 74 patients in the case group was post-processed with 40% ASIR and 60% FBP (Group B2). Quantitative and qualitative image parameters of all groups were evaluated and statistically analysed for significant differences. In addition, all patients were divided into groups by diagnosis and also checked for statistical significance. Results: All of the calculated values of the case groups were compared with those of the control group which indicated a reduction in the effective dose of radiation of more than 40% over that received by the control group (p < 0.0001). The image quality for Group B1 was significantly reduced both quantitatively and qualitatively. Group B2 achieved similar quantitative results, with the qualitative analysis worse than the control group, but still better than group B1. For clinical use, the quality was high enough to allow diagnosis of the case groups. The comparative calculations within the diagnostic groups appeared heterogeneous. For more than 50% of the patients, the imaging was unremarkable. Conclusion: • A protocol with 20% ASIR and 80% FBP allows a significant reduction in the ra-diation dosage of almost 40% while offering adequate image quality. • Post-processing with 40% ASIR and 60% FBP improves image quality. • Iterative reconstruction techniques should be applied in clinical practise. Advisors/Committee Members: [email protected] (contact), w (gender), N.N. (firstReferee), N.N. (furtherReferee).

Subjects/Keywords: Iterative reconstruction (IR); adaptive statistical iterative reconstruction (ASIR); effective dose (ED); image quality; 600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Huizing-Kusitzky, L. H. (2018). Adaptive statistical iterative reconstruction (ASIR) in head CT-improvement of image quality und reduction of radiation dose. (Thesis). Freie Universität Berlin. Retrieved from http://dx.doi.org/10.17169/refubium-8550

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Huizing-Kusitzky, Lea Heleen. “Adaptive statistical iterative reconstruction (ASIR) in head CT-improvement of image quality und reduction of radiation dose.” 2018. Thesis, Freie Universität Berlin. Accessed January 20, 2021. http://dx.doi.org/10.17169/refubium-8550.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Huizing-Kusitzky, Lea Heleen. “Adaptive statistical iterative reconstruction (ASIR) in head CT-improvement of image quality und reduction of radiation dose.” 2018. Web. 20 Jan 2021.

Vancouver:

Huizing-Kusitzky LH. Adaptive statistical iterative reconstruction (ASIR) in head CT-improvement of image quality und reduction of radiation dose. [Internet] [Thesis]. Freie Universität Berlin; 2018. [cited 2021 Jan 20]. Available from: http://dx.doi.org/10.17169/refubium-8550.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Huizing-Kusitzky LH. Adaptive statistical iterative reconstruction (ASIR) in head CT-improvement of image quality und reduction of radiation dose. [Thesis]. Freie Universität Berlin; 2018. Available from: http://dx.doi.org/10.17169/refubium-8550

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Universiteit Utrecht

9. Zbijewski, Wojciech Bartosz. Model-based image reconstruction in X-ray computed tomography.

Degree: 2006, Universiteit Utrecht

URL: http://dspace.library.uu.nl:8080/handle/1874/9678

► The thesis investigates the applications of iterative, statistical reconstruction (SR) algorithms in X-ray Computed Tomography. Emphasis is put on various aspects of system modeling in… (more)

▼ The thesis investigates the applications of iterative, statistical reconstruction (SR) algorithms in X-ray Computed Tomography. Emphasis is put on various aspects of system modeling in statistical reconstruction. Fundamental issues such as effects of object discretization and algorithm initialization on reconstructed images are thoroughly investigated. We show that artifacts may emerge in reconstructions if overly coarse image grids are used. Only by reconstructing on a finer grid than would be used in analytical algorithms one can retain the main advantage of SR over analytical methods: the improved resolution-noise trade-off. Furthermore, we demonstrate that the emergence of the abovementioned artifacts is more related to the density of object discretization than to the selection of image basis function: for coarse grids, artifacts emerge both for voxel- and blob-based image representations. Another important finding is that the use of an analytically reconstructed image as initial guess for SR may result in significant acceleration of SR's convergence around small, high-contrast structures. Speed-ups of up to one order of magnitude are achievable. The noise injected with this analytically computed initial estimate is promptly removed by SR, so the acceleration is obtained without any penalty in terms of resolution-noise trade-off. The thesis demonstrates also how the use of system modeling during reconstruction improves the quality of X-ray CT images. We introduce an efficient and accurate Monte Carlo-based scheme for the correction of scatter-induced image artifacts. To this end, a dedicated Monte Carlo (MC) simulator of X-ray CT scanners is developed. With the simulator, the scale of scatter-induced artifacts is investigated for the case of micro-CT imaging. It is found that scatter is responsible for as much as 50% of the strength of cupping artifacts present in the reconstructions of rat-sized objects. The proposed MC simulator uses an advanced fitting scheme to significantly reduce the computational time needed to obtain an accurate estimate of object scatter. Acceleration factors of three-four orders of magnitude are attainable. Such a rapid scatter simulation is no longer a computational bottleneck during image reconstruction. When the accelerated MC simulator is combined with poly-energetic statistical reconstruction algorithm, micro-CT images almost free of any scatter or beam hardening artifacts are achieved. Strong reduction of artifacts is attained both for real and simulated micro-CT data. Furthermore, we investigate a micro-CT configuration based on arrays of modular detectors. In such a design, appearance of gaps between the modules seems inevitable. We show that by using SR one may obtain volumetric reconstructions free of any gap-induced artifacts even for systems where discontinuities cover as much as 10% of detector area. It has only to be ascertained that the projection data collected with the modular detector is complete in the central imaging plane. An important advantage of SR is that no…

Subjects/Keywords: Geneeskunde; imaging; computed tomography; micro-CT; image reconstruction; maximum likelihood; statistical reconstruction; Monte Carlo simulation; scatter; image artifacts; image quality

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zbijewski, W. B. (2006). Model-based image reconstruction in X-ray computed tomography. (Doctoral Dissertation). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/9678

Chicago Manual of Style (16^th Edition):

Zbijewski, Wojciech Bartosz. “Model-based image reconstruction in X-ray computed tomography.” 2006. Doctoral Dissertation, Universiteit Utrecht. Accessed January 20, 2021. http://dspace.library.uu.nl:8080/handle/1874/9678.

MLA Handbook (7^th Edition):

Zbijewski, Wojciech Bartosz. “Model-based image reconstruction in X-ray computed tomography.” 2006. Web. 20 Jan 2021.

Vancouver:

Zbijewski WB. Model-based image reconstruction in X-ray computed tomography. [Internet] [Doctoral dissertation]. Universiteit Utrecht; 2006. [cited 2021 Jan 20]. Available from: http://dspace.library.uu.nl:8080/handle/1874/9678.

Council of Science Editors:

Zbijewski WB. Model-based image reconstruction in X-ray computed tomography. [Doctoral Dissertation]. Universiteit Utrecht; 2006. Available from: http://dspace.library.uu.nl:8080/handle/1874/9678

10. Zbijewski, Wojciech Bartosz. Model-based image reconstruction in X-ray computed tomography.

Degree: 2006, University Utrecht

URL: https://dspace.library.uu.nl/handle/1874/9678 ; URN:NBN:NL:UI:10-1874-9678 ; urn:isbn:90-393-4264-4 ; URN:NBN:NL:UI:10-1874-9678 ; https://dspace.library.uu.nl/handle/1874/9678

► The thesis investigates the applications of iterative, statistical reconstruction (SR) algorithms in X-ray Computed Tomography. Emphasis is put on various aspects of system modeling in… (more)

▼ The thesis investigates the applications of iterative, statistical reconstruction (SR) algorithms in X-ray Computed Tomography. Emphasis is put on various aspects of system modeling in statistical reconstruction. Fundamental issues such as effects of object discretization and algorithm initialization on reconstructed images are thoroughly investigated. We show that artifacts may emerge in reconstructions if overly coarse image grids are used. Only by reconstructing on a finer grid than would be used in analytical algorithms one can retain the main advantage of SR over analytical methods: the improved resolution-noise trade-off. Furthermore, we demonstrate that the emergence of the abovementioned artifacts is more related to the density of object discretization than to the selection of image basis function: for coarse grids, artifacts emerge both for voxel- and blob-based image representations. Another important finding is that the use of an analytically reconstructed image as initial guess for SR may result in significant acceleration of SR's convergence around small, high-contrast structures. Speed-ups of up to one order of magnitude are achievable. The noise injected with this analytically computed initial estimate is promptly removed by SR, so the acceleration is obtained without any penalty in terms of resolution-noise trade-off. The thesis demonstrates also how the use of system modeling during reconstruction improves the quality of X-ray CT images. We introduce an efficient and accurate Monte Carlo-based scheme for the correction of scatter-induced image artifacts. To this end, a dedicated Monte Carlo (MC) simulator of X-ray CT scanners is developed. With the simulator, the scale of scatter-induced artifacts is investigated for the case of micro-CT imaging. It is found that scatter is responsible for as much as 50% of the strength of cupping artifacts present in the reconstructions of rat-sized objects. The proposed MC simulator uses an advanced fitting scheme to significantly reduce the computational time needed to obtain an accurate estimate of object scatter. Acceleration factors of three-four orders of magnitude are attainable. Such a rapid scatter simulation is no longer a computational bottleneck during image reconstruction. When the accelerated MC simulator is combined with poly-energetic statistical reconstruction algorithm, micro-CT images almost free of any scatter or beam hardening artifacts are achieved. Strong reduction of artifacts is attained both for real and simulated micro-CT data. Furthermore, we investigate a micro-CT configuration based on arrays of modular detectors. In such a design, appearance of gaps between the modules seems inevitable. We show that by using SR one may obtain volumetric reconstructions free of any gap-induced artifacts even for systems where discontinuities cover as much as 10% of detector area. It has only to be ascertained that the projection data collected with the modular detector is complete in the central imaging plane. An important advantage of SR is that no…

Subjects/Keywords: imaging; computed tomography; micro-CT; image reconstruction; maximum likelihood; statistical reconstruction; Monte Carlo simulation; scatter; image artifacts; image quality

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zbijewski, W. B. (2006). Model-based image reconstruction in X-ray computed tomography. (Doctoral Dissertation). University Utrecht. Retrieved from https://dspace.library.uu.nl/handle/1874/9678 ; URN:NBN:NL:UI:10-1874-9678 ; urn:isbn:90-393-4264-4 ; URN:NBN:NL:UI:10-1874-9678 ; https://dspace.library.uu.nl/handle/1874/9678

Chicago Manual of Style (16^th Edition):

Zbijewski, Wojciech Bartosz. “Model-based image reconstruction in X-ray computed tomography.” 2006. Doctoral Dissertation, University Utrecht. Accessed January 20, 2021. https://dspace.library.uu.nl/handle/1874/9678 ; URN:NBN:NL:UI:10-1874-9678 ; urn:isbn:90-393-4264-4 ; URN:NBN:NL:UI:10-1874-9678 ; https://dspace.library.uu.nl/handle/1874/9678.

MLA Handbook (7^th Edition):

Zbijewski, Wojciech Bartosz. “Model-based image reconstruction in X-ray computed tomography.” 2006. Web. 20 Jan 2021.

Vancouver:

Zbijewski WB. Model-based image reconstruction in X-ray computed tomography. [Internet] [Doctoral dissertation]. University Utrecht; 2006. [cited 2021 Jan 20]. Available from: https://dspace.library.uu.nl/handle/1874/9678 ; URN:NBN:NL:UI:10-1874-9678 ; urn:isbn:90-393-4264-4 ; URN:NBN:NL:UI:10-1874-9678 ; https://dspace.library.uu.nl/handle/1874/9678.

Council of Science Editors:

Zbijewski WB. Model-based image reconstruction in X-ray computed tomography. [Doctoral Dissertation]. University Utrecht; 2006. Available from: https://dspace.library.uu.nl/handle/1874/9678 ; URN:NBN:NL:UI:10-1874-9678 ; urn:isbn:90-393-4264-4 ; URN:NBN:NL:UI:10-1874-9678 ; https://dspace.library.uu.nl/handle/1874/9678

11. Zbijewski, Wojciech Bartosz. Model-based image reconstruction in X-ray computed tomography.

Degree: 2006, University Utrecht

URL: https://dspace.library.uu.nl/handle/1874/9678 ; URN:NBN:NL:UI:10-1874-9678 ; 1874/9678 ; urn:isbn:9039342644 ; URN:NBN:NL:UI:10-1874-9678 ; https://dspace.library.uu.nl/handle/1874/9678

► The thesis investigates the applications of iterative, statistical reconstruction (SR) algorithms in X-ray Computed Tomography. Emphasis is put on various aspects of system modeling in… (more)

▼ The thesis investigates the applications of iterative, statistical reconstruction (SR) algorithms in X-ray Computed Tomography. Emphasis is put on various aspects of system modeling in statistical reconstruction. Fundamental issues such as effects of object discretization and algorithm initialization on reconstructed images are thoroughly investigated. We show that artifacts may emerge in reconstructions if overly coarse image grids are used. Only by reconstructing on a finer grid than would be used in analytical algorithms one can retain the main advantage of SR over analytical methods: the improved resolution-noise trade-off. Furthermore, we demonstrate that the emergence of the abovementioned artifacts is more related to the density of object discretization than to the selection of image basis function: for coarse grids, artifacts emerge both for voxel- and blob-based image representations. Another important finding is that the use of an analytically reconstructed image as initial guess for SR may result in significant acceleration of SR's convergence around small, high-contrast structures. Speed-ups of up to one order of magnitude are achievable. The noise injected with this analytically computed initial estimate is promptly removed by SR, so the acceleration is obtained without any penalty in terms of resolution-noise trade-off. The thesis demonstrates also how the use of system modeling during reconstruction improves the quality of X-ray CT images. We introduce an efficient and accurate Monte Carlo-based scheme for the correction of scatter-induced image artifacts. To this end, a dedicated Monte Carlo (MC) simulator of X-ray CT scanners is developed. With the simulator, the scale of scatter-induced artifacts is investigated for the case of micro-CT imaging. It is found that scatter is responsible for as much as 50% of the strength of cupping artifacts present in the reconstructions of rat-sized objects. The proposed MC simulator uses an advanced fitting scheme to significantly reduce the computational time needed to obtain an accurate estimate of object scatter. Acceleration factors of three-four orders of magnitude are attainable. Such a rapid scatter simulation is no longer a computational bottleneck during image reconstruction. When the accelerated MC simulator is combined with poly-energetic statistical reconstruction algorithm, micro-CT images almost free of any scatter or beam hardening artifacts are achieved. Strong reduction of artifacts is attained both for real and simulated micro-CT data. Furthermore, we investigate a micro-CT configuration based on arrays of modular detectors. In such a design, appearance of gaps between the modules seems inevitable. We show that by using SR one may obtain volumetric reconstructions free of any gap-induced artifacts even for systems where discontinuities cover as much as 10% of detector area. It has only to be ascertained that the projection data collected with the modular detector is complete in the central imaging plane. An important advantage of SR is that no…

Subjects/Keywords: imaging; computed tomography; micro-CT; image reconstruction; maximum likelihood; statistical reconstruction; Monte Carlo simulation; scatter; image artifacts; image quality

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zbijewski, W. B. (2006). Model-based image reconstruction in X-ray computed tomography. (Doctoral Dissertation). University Utrecht. Retrieved from https://dspace.library.uu.nl/handle/1874/9678 ; URN:NBN:NL:UI:10-1874-9678 ; 1874/9678 ; urn:isbn:9039342644 ; URN:NBN:NL:UI:10-1874-9678 ; https://dspace.library.uu.nl/handle/1874/9678

Chicago Manual of Style (16^th Edition):

Zbijewski, Wojciech Bartosz. “Model-based image reconstruction in X-ray computed tomography.” 2006. Doctoral Dissertation, University Utrecht. Accessed January 20, 2021. https://dspace.library.uu.nl/handle/1874/9678 ; URN:NBN:NL:UI:10-1874-9678 ; 1874/9678 ; urn:isbn:9039342644 ; URN:NBN:NL:UI:10-1874-9678 ; https://dspace.library.uu.nl/handle/1874/9678.

MLA Handbook (7^th Edition):

Zbijewski, Wojciech Bartosz. “Model-based image reconstruction in X-ray computed tomography.” 2006. Web. 20 Jan 2021.

Vancouver:

Zbijewski WB. Model-based image reconstruction in X-ray computed tomography. [Internet] [Doctoral dissertation]. University Utrecht; 2006. [cited 2021 Jan 20]. Available from: https://dspace.library.uu.nl/handle/1874/9678 ; URN:NBN:NL:UI:10-1874-9678 ; 1874/9678 ; urn:isbn:9039342644 ; URN:NBN:NL:UI:10-1874-9678 ; https://dspace.library.uu.nl/handle/1874/9678.

Council of Science Editors:

Zbijewski WB. Model-based image reconstruction in X-ray computed tomography. [Doctoral Dissertation]. University Utrecht; 2006. Available from: https://dspace.library.uu.nl/handle/1874/9678 ; URN:NBN:NL:UI:10-1874-9678 ; 1874/9678 ; urn:isbn:9039342644 ; URN:NBN:NL:UI:10-1874-9678 ; https://dspace.library.uu.nl/handle/1874/9678

12. Mohy-ud-Din, Hassan. Motion Correction and Pharmacokinetic Analysis in Dynamic Positron Emission Tomography.

Degree: 2015, Johns Hopkins University

URL: http://jhir.library.jhu.edu/handle/1774.2/37900

► This thesis will focus on two important aspects of dynamic Positron Emission Tomography (PET): (i) Motion-compensation , and (ii) Pharmacokinetic analysis (also called parametric imaging)… (more)

Subjects/Keywords: Positron Emission Tomography; Motion Correction; Parametric Imaging; Kinetic Modeling; Molecular Imaging; Pharmacokinetic Analysis; Dual-biomarker Imaging; Cardiovascular Imaging; Myocardial Perfusion PET; Physiological Clustering; Robust Parameter Estimation; Statistical Image Reconstruction; Direct 4-D Image Reconstruction; Resolution Modeling

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Mohy-ud-Din, H. (2015). Motion Correction and Pharmacokinetic Analysis in Dynamic Positron Emission Tomography. (Thesis). Johns Hopkins University. Retrieved from http://jhir.library.jhu.edu/handle/1774.2/37900

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Mohy-ud-Din, Hassan. “Motion Correction and Pharmacokinetic Analysis in Dynamic Positron Emission Tomography.” 2015. Thesis, Johns Hopkins University. Accessed January 20, 2021. http://jhir.library.jhu.edu/handle/1774.2/37900.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Mohy-ud-Din, Hassan. “Motion Correction and Pharmacokinetic Analysis in Dynamic Positron Emission Tomography.” 2015. Web. 20 Jan 2021.

Vancouver:

Mohy-ud-Din H. Motion Correction and Pharmacokinetic Analysis in Dynamic Positron Emission Tomography. [Internet] [Thesis]. Johns Hopkins University; 2015. [cited 2021 Jan 20]. Available from: http://jhir.library.jhu.edu/handle/1774.2/37900.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mohy-ud-Din H. Motion Correction and Pharmacokinetic Analysis in Dynamic Positron Emission Tomography. [Thesis]. Johns Hopkins University; 2015. Available from: http://jhir.library.jhu.edu/handle/1774.2/37900

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Michigan

13. Erdogan, Hakan. Statistical image reconstruction algorithms using paraboloidal surrogates for PET transmission scans.

Degree: PhD, Electrical engineering, 1999, University of Michigan

URL: http://hdl.handle.net/2027.42/132124

► Positron Emission Tomography (PET) is a diagnostic imaging tool that provides images of radioactive substances injected into the body to trace biological functions. The radioactive… (more)

▼ Positron Emission Tomography (PET) is a diagnostic imaging tool that provides images of radioactive substances injected into the body to trace biological functions. The radioactive substance emits a positron which annihilates with an electron to produce two 511 keV photons traveling in approximately opposite directions to be coincidentally detected by two detectors. Many photons are absorbed or scattered, reducing the number of detected emission events. Attenuation correction is crucial for quantitatively accurate PET reconstructions. PET transmission scans are performed to estimate attenuation parameters which are in turn used to correct the emission scans for attenuation effects. The noise in estimating the attenuation parameters propagates to the emission images affecting their quality and quantitative correctness. Thus, attenuation image reconstruction is extremely important in PET. Conventional methods of attenuation correction are suboptimal and ignore the Poisson nature of the data. We propose to use penalized likelihood Image reconstruction techniques for transmission scans. Current algorithms for transmission tomography have two important problems: (1) they are not guaranteed to converge, (2) if they converge, the convergence is slow. We develop new fast and monotonic optimization algorithms for penalized likelihood image reconstruction based on a novel paraboloidal surrogates principle. We present results showing the speed of the new optimization algorithms as compared to previous ones. We apply the algorithms to PET data obtained from an anthropomorphic thorax phantom and real patient data. A transmission scan performed after the patient is injected is called a post-injection transmission scan. Post-injection transmission scans are desirable since the patient throughput is increased and motion artifacts are reduced as compared to pre-injection scans. However, there are emission counts contaminating the measurements. We include emission contamination in the post-injection transmission measurement statistical model and obtain better images as compared to conventional subtraction based approaches. We also present a joint estimation technique to estimate attenuation and emission images simultaneously from transmission and emission scans. We analyze noise propagation from transmission scans to emission images for some sequential image reconstruction methods. The results show that transmission noise affects emission image quality heavily, especially when short transmission scans are utilized. Advisors/Committee Members: Fessler, Jeffrey A. (advisor).

Subjects/Keywords: Algorithms; Image Reconstruction; Paraboloidal Surrogates; Pet; Statistical; Transmission Scans; Using

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Erdogan, H. (1999). Statistical image reconstruction algorithms using paraboloidal surrogates for PET transmission scans. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/132124

Chicago Manual of Style (16^th Edition):

Erdogan, Hakan. “Statistical image reconstruction algorithms using paraboloidal surrogates for PET transmission scans.” 1999. Doctoral Dissertation, University of Michigan. Accessed January 20, 2021. http://hdl.handle.net/2027.42/132124.

MLA Handbook (7^th Edition):

Erdogan, Hakan. “Statistical image reconstruction algorithms using paraboloidal surrogates for PET transmission scans.” 1999. Web. 20 Jan 2021.

Vancouver:

Erdogan H. Statistical image reconstruction algorithms using paraboloidal surrogates for PET transmission scans. [Internet] [Doctoral dissertation]. University of Michigan; 1999. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2027.42/132124.

Council of Science Editors:

Erdogan H. Statistical image reconstruction algorithms using paraboloidal surrogates for PET transmission scans. [Doctoral Dissertation]. University of Michigan; 1999. Available from: http://hdl.handle.net/2027.42/132124

University of Michigan

14. Yavuz, Mehmet. Statistical tomographic image reconstruction methods for randoms -precorrected PET measurements.

Degree: PhD, Electrical engineering, 2000, University of Michigan

URL: http://hdl.handle.net/2027.42/132500

► Medical Imaging systems such as positron emission tomography (PET) and electronically collimated single positron emission tomography (SPECT) record particle emission events based on timing coincidences.… (more)

▼ Medical Imaging systems such as positron emission tomography (PET) and electronically collimated single positron emission tomography (SPECT) record particle emission events based on timing coincidences. These systems record accidental coincidence (AC) events simultaneously with the true coincidence events. Similarly in low light-level imaging, thermoelectrons generated by photodetector are indistinguishable from photoelectrons generated by photo-conversion, and their effect is similar to the AC events. During PET emission scans, accidental coincidence (AC) events occur when photons that originate from separate positron-electron annihilations are mistakenly recorded as having arisen from the same annihilation. In PET, generally a significant portion of the collected data consists of AC events that are a primary source of background noise. Also, during PET transmission scans, photons that originate from different transmission sources cause AC events. In PET, the measurements are usually precorrected for AC events by real-time subtraction of the delayed window coincidences. Randoms subtraction compensates in mean for accidental coincidences, but destroys the Poisson statistics. We develop statistical image reconstruction methods for randoms pre-corrected PET measurements using penalized maximum likelihood (ML) estimation. We introduce two new approximations to the complicated exact log-likelihood of the precorrected measurements: one based on a shifted Poisson model, and the other based on saddle-point approximations to the measurement probability mass function (pmf). We compare estimators based on the new models to the conventional data-weighted least squares (WLS) and conventional maximum likelihood (based on the ordinary Poisson (OP) model) using experiments, simulations and analytic approximations. For transmission scans, we demonstrate that the proposed methods avoid the systematic bias of the WLS method, and lead to significantly lower variance than the conventional OP method. We also investigate the propagation of noise from the reconstructed attenuation maps into the emission images. Interestingly, the noise improvements in the emission images with the new methods are even greater than the improvements in the attenuation maps themselves. To corroborate the empirical studies, we develop analytical approximations to the reconstructed image covariance and we also develop analytical approximations for the propagation of noise from attenuation maps into the reconstructed emission images. The results of the analytic approximations are shown to be in good agreement with the experimental results supporting the improvements with the new methods. Similarly, for the emission reconstructions, we demonstrate that the proposed methods lead to significantly lower variance than the conventional OP method and also avoid systematic positive bias of the OP method. Although the SP model is shown to be slightly biased for emission scans with very low count rates, the… Advisors/Committee Members: Fessler, Jeffrey A. (advisor).

Subjects/Keywords: Accidental Coincidence; Image Reconstruction; Measurements; Methods; Pet; Randoms-precorrected; Statistical; Tomographic

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Yavuz, M. (2000). Statistical tomographic image reconstruction methods for randoms -precorrected PET measurements. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/132500

Chicago Manual of Style (16^th Edition):

Yavuz, Mehmet. “Statistical tomographic image reconstruction methods for randoms -precorrected PET measurements.” 2000. Doctoral Dissertation, University of Michigan. Accessed January 20, 2021. http://hdl.handle.net/2027.42/132500.

MLA Handbook (7^th Edition):

Yavuz, Mehmet. “Statistical tomographic image reconstruction methods for randoms -precorrected PET measurements.” 2000. Web. 20 Jan 2021.

Vancouver:

Yavuz M. Statistical tomographic image reconstruction methods for randoms -precorrected PET measurements. [Internet] [Doctoral dissertation]. University of Michigan; 2000. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2027.42/132500.

Council of Science Editors:

Yavuz M. Statistical tomographic image reconstruction methods for randoms -precorrected PET measurements. [Doctoral Dissertation]. University of Michigan; 2000. Available from: http://hdl.handle.net/2027.42/132500

University of Michigan

15. Yendiki, Anastasia. Analysis of signal detectability in statistically reconstructed tomographic images.

Degree: PhD, Electrical engineering, 2005, University of Michigan

URL: http://hdl.handle.net/2027.42/125552

► Imaging in general, and emission tomography in particular, has become an important tool in many areas of medical diagnosis. Several common applications of emission tomography,… (more)

▼ Imaging in general, and emission tomography in particular, has become an important tool in many areas of medical diagnosis. Several common applications of emission tomography, such as the diagnosis of lung tumors or myocardial perfusion defects, involve the detection of a spatially localized target signal in an image reconstructed from noisy data. Such detection tasks are affected by various design parameters of the imaging system and reconstruction algorithm. This thesis is concerned with optimizing regularized image reconstruction methods for emission tomography with respect to the detectability of a spatially localized target signal in the reconstructed images. We first consider the task of detecting a statistically varying signal of known location on a statistically varying background in a reconstructed tomographic image. We show that a broad family of linear observer models can achieve exactly optimal detection performance in this task if one chooses a suitable reconstruction method. This conclusion encompasses several well-known models from the literature, including those with a frequency-selective channel mechanism. Interestingly, the optimal linear reconstruction methods for many of these observer models are unregularized and in some cases quite unconventional. In the case of channelized models in particular, the observer's ability to prewhiten determines the extent to which its detection performance can benefit from regularization. That is, regularization is more important for channelized observers that have incomplete knowledge of the second-order statistics of the reconstructed images. Subsequently, we investigate detection tasks where the location of the target signal is unknown to the observer. This location uncertainty complicates the mathematical analysis of observer performance significantly. We consider model observers whose decisions are based on the maximum value of a linear local test statistic over all possible signal locations. Several of our conclusions about the known-location task extend to this case. Previous approaches to this problem have used Monte Carlo simulations to evaluate the localization performance of maximum-statistic observers. We propose an alternative approach, where approximations of tail probabilities for the maximum of correlated Gaussian random fields facilitate analytical evaluation of detection performance. We illustrate how these approximations can be used to optimize the probability of detection (at low probabilities of false alarm) for the observers of interest. Advisors/Committee Members: Fessler, Jeffrey A. (advisor).

Subjects/Keywords: Analysis; Detectability; Emission Tomography; Reconstructed; Signal Detection; Statistical Image Reconstruction; Statistically; Tomographic Images

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Yendiki, A. (2005). Analysis of signal detectability in statistically reconstructed tomographic images. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/125552

Chicago Manual of Style (16^th Edition):

Yendiki, Anastasia. “Analysis of signal detectability in statistically reconstructed tomographic images.” 2005. Doctoral Dissertation, University of Michigan. Accessed January 20, 2021. http://hdl.handle.net/2027.42/125552.

MLA Handbook (7^th Edition):

Yendiki, Anastasia. “Analysis of signal detectability in statistically reconstructed tomographic images.” 2005. Web. 20 Jan 2021.

Vancouver:

Yendiki A. Analysis of signal detectability in statistically reconstructed tomographic images. [Internet] [Doctoral dissertation]. University of Michigan; 2005. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2027.42/125552.

Council of Science Editors:

Yendiki A. Analysis of signal detectability in statistically reconstructed tomographic images. [Doctoral Dissertation]. University of Michigan; 2005. Available from: http://hdl.handle.net/2027.42/125552

University of Michigan

16. Yoon, Seongjin. Electron Beam X-Ray Computed Tomography for Multiphase Flows and An Experimental Study of Inter-channel Mixing.

Degree: PhD, Naval Architecture & Marine Engineering, 2017, University of Michigan

URL: http://hdl.handle.net/2027.42/138666

► This thesis consists of two parts. In the first, a high speed X-ray Computed Tomography (CT) system for multiphase flows is developed. X-ray Computed Tomography… (more)

▼ This thesis consists of two parts. In the first, a high speed X-ray Computed Tomography (CT) system for multiphase flows is developed. X-ray Computed Tomography (CT) has been employed in the study of multiphase flows. The systems developed to date often have excellent spatial resolution at the expense of poor temporal resolution. Hence, X-ray CT has mostly been employed to examining time averaged phase distributions. In the present work, we report on the development of a Scanning Electron Beam X-ray Tomography (SEBXT) CT system that will allow for much higher time resolution with acceptable spatial resolution. The designed system, however, can have issues such as beam-hardening and limited angle artifacts. In the present study, we developed a high speed, limited angle SEBXT system along with a new CT reconstruction algorithm designed to enhance the CT reconstruction results of such system. To test the performance of the CT system, we produced example CT reconstruction results for two test phantoms based on the actual measured sinograms and the simulated sinograms. The second part examines, the process by which fluid mixes between two parallel flow channels through a narrow gap. This flow is a canonical representation of the mixing and mass transfer processes that often occur in thermo-hydraulic systems. The mixing can be strongly related to the presence of large-scale periodic flow structures that form within the gap. In the present work, we have developed an experimental setup to examine the single-phase mixing through the narrow rectangular gaps connecting two rectangular channels. Our goal is to elucidate the underlying flow processes responsible for inter-channel mixing, and to produce high-fidelity data for validation of computational models. Dye concentration measurements were used to determine the time average rate of mixing. Particle Imaging Velocimetry (PIV) was used to measure the flow fields within the gap. A Proper Orthogonal Decomposition (POD) of the PIV flow fields revealed the presence of coherent flow structure. The decomposed flow fields were then used to predict the time averaged mixing, which closely matched the experimentally measured values. Advisors/Committee Members: Ceccio, Steven L (committee member), Makiharju, Simo (committee member), Fessler, Jeffrey A (committee member), Perlin, Marc (committee member).

Subjects/Keywords: electron beam X-ray CT; statistical CT reconstruction; gap mixing; particle image velocimetry; singular value decomposition; Naval Architecture and Marine Engineering; Engineering

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Yoon, S. (2017). Electron Beam X-Ray Computed Tomography for Multiphase Flows and An Experimental Study of Inter-channel Mixing. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/138666

Chicago Manual of Style (16^th Edition):

Yoon, Seongjin. “Electron Beam X-Ray Computed Tomography for Multiphase Flows and An Experimental Study of Inter-channel Mixing.” 2017. Doctoral Dissertation, University of Michigan. Accessed January 20, 2021. http://hdl.handle.net/2027.42/138666.

MLA Handbook (7^th Edition):

Yoon, Seongjin. “Electron Beam X-Ray Computed Tomography for Multiphase Flows and An Experimental Study of Inter-channel Mixing.” 2017. Web. 20 Jan 2021.

Vancouver:

Yoon S. Electron Beam X-Ray Computed Tomography for Multiphase Flows and An Experimental Study of Inter-channel Mixing. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2027.42/138666.

Council of Science Editors:

Yoon S. Electron Beam X-Ray Computed Tomography for Multiphase Flows and An Experimental Study of Inter-channel Mixing. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/138666

17. Panić Marko. Image Reconstruction from Undersampled Data with Application to Accelerated Magnetic Resonance Imaging.

Degree: 2020, University of Novi Sad

URL: https://www.cris.uns.ac.rs/DownloadFileServlet/Disertacija157235029179789.pdf?controlNumber=(BISIS)112152&fileName=157235029179789.pdf&id=14051&source=OATD&language=en ; https://www.cris.uns.ac.rs/record.jsf?recordId=112152&source=OATD&language=en

►

In dissertation a problem of reconstruction of images from undersampled measurements is considered which has direct application in creation of magnetic resonance images. The… (more)

▼

In dissertation a problem of reconstruction of images from undersampled measurements is considered which has direct application in creation of magnetic resonance images. The topic of the research is proposition of new regularization based methods for image reconstruction which are based on statistical Markov random field models and theory of compressive sensing. With the proposed signal model which follows the statistics of images, a new regularization functions are defined and four methods for reconstruction of magnetic resonance images are derived.

У докторској дисертацији разматран је проблем реконструкције сигнала слике из непотпуних мерења који има директну примену у креирању слика магнетне резнонаце. Предмет истраживања је везан за предлог нових регуларизационих метода реконструкције коришћењем статистичких модела Марковљевог случајног поља и теорије ретке репрезентације сигнала. На основу предложеног модела који на веродостојан начин репрезентује статистику сигнала слике предложене су регуларизационе функције и креирана четири алгоритма за реконструкцију слике магнетне резонанце.

U doktorskoj disertaciji razmatran je problem rekonstrukcije signala slike iz nepotpunih merenja koji ima direktnu primenu u kreiranju slika magnetne reznonace. Predmet istraživanja je vezan za predlog novih regularizacionih metoda rekonstrukcije korišćenjem statističkih modela Markovljevog slučajnog polja i teorije retke reprezentacije signala. Na osnovu predloženog modela koji na verodostojan način reprezentuje statistiku signala slike predložene su regularizacione funkcije i kreirana četiri algoritma za rekonstrukciju slike magnetne rezonance.

Advisors/Committee Members: Vukobratović Dejan, Pižurica Aleksandra, De Cooman Gert, Crnojević Vladimir, Sijbers Jan, Pullens Pim, Platiša Ljiljana.

Subjects/Keywords: Image reconstruction, sparse representation, statistical modelling, optimization, magnetic resonance; Реконструкција слике, ретка репрезентација, статистичко моделовање, оптимизација, магнетна резонанца; Rekonstrukcija slike, retka reprezentacija, statističko modelovanje, optimizacija, magnetna rezonanca

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Marko, P. (2020). Image Reconstruction from Undersampled Data with Application to Accelerated Magnetic Resonance Imaging. (Thesis). University of Novi Sad. Retrieved from https://www.cris.uns.ac.rs/DownloadFileServlet/Disertacija157235029179789.pdf?controlNumber=(BISIS)112152&fileName=157235029179789.pdf&id=14051&source=OATD&language=en ; https://www.cris.uns.ac.rs/record.jsf?recordId=112152&source=OATD&language=en

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Marko, Panić. “Image Reconstruction from Undersampled Data with Application to Accelerated Magnetic Resonance Imaging.” 2020. Thesis, University of Novi Sad. Accessed January 20, 2021. https://www.cris.uns.ac.rs/DownloadFileServlet/Disertacija157235029179789.pdf?controlNumber=(BISIS)112152&fileName=157235029179789.pdf&id=14051&source=OATD&language=en ; https://www.cris.uns.ac.rs/record.jsf?recordId=112152&source=OATD&language=en.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Marko, Panić. “Image Reconstruction from Undersampled Data with Application to Accelerated Magnetic Resonance Imaging.” 2020. Web. 20 Jan 2021.

Vancouver:

Marko P. Image Reconstruction from Undersampled Data with Application to Accelerated Magnetic Resonance Imaging. [Internet] [Thesis]. University of Novi Sad; 2020. [cited 2021 Jan 20]. Available from: https://www.cris.uns.ac.rs/DownloadFileServlet/Disertacija157235029179789.pdf?controlNumber=(BISIS)112152&fileName=157235029179789.pdf&id=14051&source=OATD&language=en ; https://www.cris.uns.ac.rs/record.jsf?recordId=112152&source=OATD&language=en.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Marko P. Image Reconstruction from Undersampled Data with Application to Accelerated Magnetic Resonance Imaging. [Thesis]. University of Novi Sad; 2020. Available from: https://www.cris.uns.ac.rs/DownloadFileServlet/Disertacija157235029179789.pdf?controlNumber=(BISIS)112152&fileName=157235029179789.pdf&id=14051&source=OATD&language=en ; https://www.cris.uns.ac.rs/record.jsf?recordId=112152&source=OATD&language=en

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

18. Barquero, Harold. Limited angular range X-ray micro-computerized tomography : derivation of anatomical information as a prior for optical luminescence tomography : Micro-tomographie par rayons X à angle limité : dérivation d’une information anatomique a priori pour la tomographie optique par luminescence.

Degree: Docteur es, Physique, 2015, Université de Strasbourg

URL: http://www.theses.fr/2015STRAE033

►

Cette thèse traite du couplage d'un tomographe optique par luminescence (LCT) et d'un tomographe par rayons X (XCT), en présence d'une contrainte sur la géométrie… (more)

▼

Cette thèse traite du couplage d'un tomographe optique par luminescence (LCT) et d'un tomographe par rayons X (XCT), en présence d'une contrainte sur la géométrie d'acquisition du XCT. La couverture angulaire du XCT est limitée à 90 degrés pour satisfaire des contraintes spatiales imposées par le LCT existant dans lequel le XCT doit être intégré. L'objectif est de dériver une information anatomique, à partir de l'image morphologique issue du XCT. Notre approche a consisté i) en l'implémentation d'un algorithme itératif régularisé pour la reconstruction tomographique à angle limité, ii) en la construction d'un atlas anatomique statistique de la souris et iii) en l'implémentation d'une chaîne automatique réalisant la segmentation des images XCT, l'attribution d'une signification anatomique aux éléments segmentés, le recalage de l'atlas statistique sur ces éléments et ainsi l'estimation des contours de certains tissus à faible contraste non identifiables en pratique dans une image XCT standard.

This thesis addresses the combination of an Optical Luminescence Tomograph (OLT) and X-ray Computerized Tomograph (XCT), dealing with geometrical constraints defined by the existing OLT system in which the XCT must be integrated. The result is an acquisition geometry of XCT with a 90 degrees angular range only. The aim is to derive an anatomical information from the morphological image obtained with the XCT. Our approach consisted i) in the implementation of a regularized iterative algorithm for the tomographic reconstruction with limited angle data, ii) in the construction of a statistical anatomical atlas of the mouse and iii) in the implementation of an automatic segmentation workflow performing the segmentation of XCT images, the labelling of the segmented elements, the registration of the statistical atlas on these elements and consequently the estimation of the outlines of low contrast tissues that can not be identified in practice in a standard XCT image.

Advisors/Committee Members: Brasse, David (thesis director).

Subjects/Keywords: Imagerie biomédicale multimodale; Reconstruction tomographique à angle limité; Segmentation; Recalage; Variation totale; Atlas anatomique statistique; Multimodal biomedical imaging; Limited angle tomography; Image segmentation; Image registration; Total variation; Statistical anatomical atlas; 539.7; 535.2

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Barquero, H. (2015). Limited angular range X-ray micro-computerized tomography : derivation of anatomical information as a prior for optical luminescence tomography : Micro-tomographie par rayons X à angle limité : dérivation d’une information anatomique a priori pour la tomographie optique par luminescence. (Doctoral Dissertation). Université de Strasbourg. Retrieved from http://www.theses.fr/2015STRAE033

Chicago Manual of Style (16^th Edition):

Barquero, Harold. “Limited angular range X-ray micro-computerized tomography : derivation of anatomical information as a prior for optical luminescence tomography : Micro-tomographie par rayons X à angle limité : dérivation d’une information anatomique a priori pour la tomographie optique par luminescence.” 2015. Doctoral Dissertation, Université de Strasbourg. Accessed January 20, 2021. http://www.theses.fr/2015STRAE033.

MLA Handbook (7^th Edition):

Barquero, Harold. “Limited angular range X-ray micro-computerized tomography : derivation of anatomical information as a prior for optical luminescence tomography : Micro-tomographie par rayons X à angle limité : dérivation d’une information anatomique a priori pour la tomographie optique par luminescence.” 2015. Web. 20 Jan 2021.

Vancouver:

Barquero H. Limited angular range X-ray micro-computerized tomography : derivation of anatomical information as a prior for optical luminescence tomography : Micro-tomographie par rayons X à angle limité : dérivation d’une information anatomique a priori pour la tomographie optique par luminescence. [Internet] [Doctoral dissertation]. Université de Strasbourg; 2015. [cited 2021 Jan 20]. Available from: http://www.theses.fr/2015STRAE033.

Council of Science Editors:

Barquero H. Limited angular range X-ray micro-computerized tomography : derivation of anatomical information as a prior for optical luminescence tomography : Micro-tomographie par rayons X à angle limité : dérivation d’une information anatomique a priori pour la tomographie optique par luminescence. [Doctoral Dissertation]. Université de Strasbourg; 2015. Available from: http://www.theses.fr/2015STRAE033

19. Kim, Donghwan. Accelerated Optimization Algorithms for Statistical 3D X-ray Computed Tomography Image Reconstruction.

Degree: PhD, Electrical Engineering: Systems, 2014, University of Michigan

URL: http://hdl.handle.net/2027.42/109007

► X-ray computed tomography (CT) has been widely celebrated for its ability to visualize patient anatomy, but increasing radiation exposure to patients is a concern. Statistical… (more)

▼ X-ray computed tomography (CT) has been widely celebrated for its ability to visualize patient anatomy, but increasing radiation exposure to patients is a concern. Statistical image reconstruction algorithms in X-ray CT can provide improved image quality for reduced dose levels in contrast to the conventional filtered back-projection (FBP) methods. However, the statistical approach requires substantial computation time. Therefore, this dissertation focuses on developing fast iterative algorithms for statistical reconstruction. Ordered subsets (OS) methods have been used widely in tomography problems, because they reduce the computational cost by using only a subset of the measurement data per iteration. They are already used in commercial PET and SPECT products. However, OS methods require too long a reconstruction time in X-ray CT to be used routinely for every clinical CT scan. In this dissertation, two main approaches are proposed for accelerating OS algorithms, one that uses new optimization transfer approaches and one that combines with momentum algorithms. The first, the separable quadratic surrogates (SQS) methods, one widely used optimization transfer method with OS methods, have been accelerated in three different ways. Among them, a nonuniform (NU) SQS method encouraging larger step sizes for the voxels that are expected to change more has highly accelerated OS methods. Second, combining OS methods and momentum approaches (OS-momentum) in a way that reuses previous updates with almost negligible increased computation resulted in a very fast convergence rate. This version focused on using widely celebrated Nesterov's momentum methods. OS-momentum algorithms sometimes encountered instability, so diminishing step size rule has been adapted for improving the stability while preserving the fast convergence rate. To further accelerate OS-momentum algorithms, this dissertation proposes novel momentum methods that are twice as fast yet have remarkably simple implementations comparable to Nesterov's methods. In addition to OS-type algorithms, one variant of the block coordinate descent (BCD) algorithm, called Axial BCD (ABCD), is investigated, which is specifically designed for 3D CT geometry. Simulated and real patient 3D CT scans are used to examine the acceleration of the proposed algorithms. Advisors/Committee Members: Fessler, Jeffrey A. (committee member), Epelman, Marina A. (committee member), Balzano, Laura Kathryn (committee member), Hero Iii, Alfred O. (committee member).

Subjects/Keywords: Computed Tomography, Statistical Image Reconstruction, Optimization Algorithms, Iterative Algorithms, Ordered Subsets, Gradient Methods; Electrical Engineering; Engineering

…but has been criticized for high radiation exposure. Statistical image reconstruction… …Statistical image reconstruction methods can improve resolution and reduce noise and artifacts even… …accelerated optimization algorithms for 3D X-ray CT statistical image reconstruction. Over the last… …statistical image reconstruction. Coordinate descent (CD) [14, 109]… …Background This chapter provides a background of X-ray CT image reconstruction and its statistical…

10 more images…

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Kim, D. (2014). Accelerated Optimization Algorithms for Statistical 3D X-ray Computed Tomography Image Reconstruction. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/109007

Chicago Manual of Style (16^th Edition):

Kim, Donghwan. “Accelerated Optimization Algorithms for Statistical 3D X-ray Computed Tomography Image Reconstruction.” 2014. Doctoral Dissertation, University of Michigan. Accessed January 20, 2021. http://hdl.handle.net/2027.42/109007.

MLA Handbook (7^th Edition):

Kim, Donghwan. “Accelerated Optimization Algorithms for Statistical 3D X-ray Computed Tomography Image Reconstruction.” 2014. Web. 20 Jan 2021.

Vancouver:

Kim D. Accelerated Optimization Algorithms for Statistical 3D X-ray Computed Tomography Image Reconstruction. [Internet] [Doctoral dissertation]. University of Michigan; 2014. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2027.42/109007.

Council of Science Editors:

Kim D. Accelerated Optimization Algorithms for Statistical 3D X-ray Computed Tomography Image Reconstruction. [Doctoral Dissertation]. University of Michigan; 2014. Available from: http://hdl.handle.net/2027.42/109007

University of Michigan

20. Yu, Feng. Statistical methods for transmission image reconstruction with nonlocal edge -preserving regularization.

Degree: PhD, Electrical engineering, 2000, University of Michigan

URL: http://hdl.handle.net/2027.42/132717

► Tomographic image reconstruction using statistical methods can improve image quality over the conventional filtered backprojection (FBP) method. The effectiveness of a statistical image reconstruction method… (more)

▼ Tomographic image reconstruction using statistical methods can improve image quality over the conventional filtered backprojection (FBP) method. The effectiveness of a statistical image reconstruction method depends on its three principal components: the statistical measurement model, the regularization method, and the iterative algorithm for maximizing the objective function. This dissertation contributes new methodology and/or analysis to each of these three components, emphasizing PET and SPECT transmission scans, which are essential for accurate attenuation correction in emission tomography. The first part considers edge-preserving regularization. We propose an objective function that incorporates nonlocal boundary information. We use an alternating minimization scheme with deterministic annealing to minimize our new objective function; we use variational techniques implemented using level sets to perform boundary extraction. We compare the bias/variance tradeoff of this novel algorithm with a penalized likelihood (with local Huber roughness penalty) algorithm. The second part analyzes the effect of deadtime on the counting statistics of detectors. We present a new way of analyzing the moments of the counting process for a counter system affected by various models of deadtime related to PET and SPECT imaging. We derive simple and exact expressions for the first and second moments of the number of recorded events under various models, for both singles counting and coincidence counting. From this analysis, we study the suitability of the Poisson statistical model assumed in most statistical image reconstruction algorithms. The third and final part considers the problem of reconstructing images for a certain transmission imaging geometry, where the transmitted beams of Photons overlap on the detector, such that a detector element may record photons that originated in different sources or source locations and thus traversed different paths through the object. We propose a new algorithm for statistical image reconstruction of attenuation maps that explicitly accounts for overlapping beams in transmission scans. The algorithm is guaranteed to monotonically increase the objective function at each iteration. The availability of this algorithm enables the possibility of deliberately designing systems with increased beam overlap so as to increase count rates. Advisors/Committee Members: Fessler, Jeffrey A. (advisor).

Subjects/Keywords: Edge-preserving Regularization; Filtered Backprojection; Medical Imaging; Methods; Nonlocal; Pet/spect; Statistical; Tomographic Image Reconstruction; Transmission

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Yu, F. (2000). Statistical methods for transmission image reconstruction with nonlocal edge -preserving regularization. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/132717

Chicago Manual of Style (16^th Edition):

Yu, Feng. “Statistical methods for transmission image reconstruction with nonlocal edge -preserving regularization.” 2000. Doctoral Dissertation, University of Michigan. Accessed January 20, 2021. http://hdl.handle.net/2027.42/132717.

MLA Handbook (7^th Edition):

Yu, Feng. “Statistical methods for transmission image reconstruction with nonlocal edge -preserving regularization.” 2000. Web. 20 Jan 2021.

Vancouver:

Yu F. Statistical methods for transmission image reconstruction with nonlocal edge -preserving regularization. [Internet] [Doctoral dissertation]. University of Michigan; 2000. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2027.42/132717.

Council of Science Editors:

Yu F. Statistical methods for transmission image reconstruction with nonlocal edge -preserving regularization. [Doctoral Dissertation]. University of Michigan; 2000. Available from: http://hdl.handle.net/2027.42/132717

21. Eichel, Justin. Statistical Model-Based Corneal Reconstruction.

Degree: 2013, University of Waterloo

URL: http://hdl.handle.net/10012/8069

► Precise measurements of corneal layer thickness are required to treat, evaluate risk of, and determine the progression of pathologies within the eye. The thickness measurements… (more)

▼ Precise measurements of corneal layer thickness are required to treat, evaluate risk of, and determine the progression of pathologies within the eye. The thickness measurements are typically acquired as 2d images, known as tomograms, from an optical coherence tomography (OCT) system. With the creation of ultra-high resolution OCT (UHROCT), there is active research in precisely measuring, in vivo, previously unresolvable corneal structures at arbitrary locations within the cornea to determine their relationship with corneal health. In order to obtain arbitrary corneal thickness measurements, existing reconstruction techniques require the cornea to be densely sampled so that a 3d representation can be interpolated from a stack of tomograms. Unfortunately, tomogram alignment relies solely on image properties such as pixel intensity, and does not constrain the reconstruction to corneal anatomy. Further, the reconstruction method cannot properly compensate for eye-motion. The deficiencies due to eye-motion are exacerbated due to the amount of time required in a single imaging session to acquire a sufficient number of tomograms in the region of interest. The proposed methodology is the first to incorporate models of the anatomy and the imaging system to address the limitations of existing corneal reconstruction methods. By constructing the model in such a way as to decouple anatomy from the imaging system, it becomes less computationally expensive to estimate model parameters. The decoupling provides an iterative methodology that can allow additional constraints to be introduced in the future. By combining sparsely sampled UHROCT measurements with a properly designed corneal model, reconstruction allows researchers to determine corneal layer thicknesses at arbitrary positions in both sampled and unsampled regions. The proposed methodology demonstrates an approach to decouple anatomy and physiology from measurements of a cornea, allowing for characterization of pathologies through corneal thickness measurements. Another significant contribution resulting from the corneal model allows five of the corneal layer boundaries to be automatically located and has already been used to process thousands of UHROCT tomograms. Recent studies using this method have also been used to correlate contact-lens wear to hypoxia and corneal layer swelling. While corneal reconstruction represents the main application of this work, the reconstruction methodology can be extended to other medical imaging domains and can even represent temporal changes in tissue with minor modifications to the framework.

Subjects/Keywords: reconstruction; statistical modelling; cornea; optical coherence tomography; segmentation; localization; image processing; pattern recognition

…Statistical reconstruction Measurements m i mi k mi,k R vector containing measurements… …transforming it to make the world a better place. Statistical model-based reconstruction encompasses… …based reconstruction When statistical model-based reconstruction is applied to a discipline… …measurements. During reconstruction, these raw measurements are fitted to a statistical model. If the… …measurement uncertainty. Statistical model-based reconstruction can generate estimates of regions…

10 more images…

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Eichel, J. (2013). Statistical Model-Based Corneal Reconstruction. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/8069

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Eichel, Justin. “Statistical Model-Based Corneal Reconstruction.” 2013. Thesis, University of Waterloo. Accessed January 20, 2021. http://hdl.handle.net/10012/8069.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Eichel, Justin. “Statistical Model-Based Corneal Reconstruction.” 2013. Web. 20 Jan 2021.

Vancouver:

Eichel J. Statistical Model-Based Corneal Reconstruction. [Internet] [Thesis]. University of Waterloo; 2013. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/10012/8069.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Eichel J. Statistical Model-Based Corneal Reconstruction. [Thesis]. University of Waterloo; 2013. Available from: http://hdl.handle.net/10012/8069

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

22. Patel, Rakeshkumar Chandulal. New techniques for spatial resolution enhancement of hyperspectral images;.

Degree: Image processing, 2009, INFLIBNET

URL: http://shodhganga.inflibnet.ac.in/handle/10603/54604

Abstract Available

Advisors/Committee Members: Joshi, Manjunath V..

Subjects/Keywords: Image processing – Digital techniques; Image reconstruction – Mathematical models; Resolution (Optics) – Mathematical models; Image processing – Digital techniques – Mathematical models; Resolution enhancement; Image processing – Statistical methods; Multivariate analysis; Multispectral photography

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Patel, R. C. (2009). New techniques for spatial resolution enhancement of hyperspectral images;. (Thesis). INFLIBNET. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/54604

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Patel, Rakeshkumar Chandulal. “New techniques for spatial resolution enhancement of hyperspectral images;.” 2009. Thesis, INFLIBNET. Accessed January 20, 2021. http://shodhganga.inflibnet.ac.in/handle/10603/54604.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Patel, Rakeshkumar Chandulal. “New techniques for spatial resolution enhancement of hyperspectral images;.” 2009. Web. 20 Jan 2021.

Vancouver:

Patel RC. New techniques for spatial resolution enhancement of hyperspectral images;. [Internet] [Thesis]. INFLIBNET; 2009. [cited 2021 Jan 20]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/54604.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Patel RC. New techniques for spatial resolution enhancement of hyperspectral images;. [Thesis]. INFLIBNET; 2009. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/54604

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

23. Brandt, Sami. Theorems and Algorithms for Multiple View Geometry with Applications to Electron Tomography.

Degree: 2002, Helsinki University of Technology

URL: http://lib.tkk.fi/Diss/2002/isbn9512261375/

►

The thesis considers both theory and algorithms for geometric computer vision. The framework of the work is built around the application of autonomous transmission electron… (more)

▼

The thesis considers both theory and algorithms for geometric computer vision. The framework of the work is built around the application of autonomous transmission electron microscope image registration. The theoretical part of the thesis first develops a consistent robust estimator that is evaluated in estimating two view geometry with both affine and projective camera models. The uncertainty of the fundamental matrix is similarly estimated robustly, and the previous observation whether the covariance matrix of the fundamental matrix contains disparity information of the scene is explained and its utilization in matching is discussed. For point tracking purposes, a reliable wavelet-based matching technique and two EM algorithms for the maximum likelihood affine reconstruction under missing data are proposed. The thesis additionally discusses identification of degeneracy as well as affine bundle adjustment. The application part of the thesis considers transmission electron microscope image registration, first with fiducial gold markers and thereafter without markers. Both methods utilize the techniques proposed in the theoretical part of the thesis and, in addition, a graph matching method is proposed for matching gold markers. Conversely, alignment without markers is disposed by tracking interest points of the intensity surface of the images. At the present level of development, the former method is more accurate but the latter is appropriate for situations where fiducial markers cannot be used. Perhaps the most significant result of the thesis is the proposed robust estimator because of consistence proof and its many application areas, which are not limited to the computer vision field. The other algorithms could be found useful in multiple view applications in computer vision that have to deal with uncertainty, matching, tracking, and reconstruction. From the viewpoint of image registration, the thesis further achieved its aims since two accurate image alignment methods are suggested for obtaining the most exact reconstructions in electron tomography.

Helsinki University of Technology Laboratory of Computational Engineering publications. Report B, ISSN 1455-0474; 30

Advisors/Committee Members: Helsinki University of Technology, Department of Electrical and Communications Engineering, Laboratory of Computational Engineering.

Subjects/Keywords: robust regression; robust estimation; statistical modeling; epipolar geometry; fundamental matrix; uncertainty; image matching; affine reconstruction; affine triangulation; degeneracy; bundle adjustment; image registration; image alignment; computer vision; electron tomography

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Brandt, S. (2002). Theorems and Algorithms for Multiple View Geometry with Applications to Electron Tomography. (Thesis). Helsinki University of Technology. Retrieved from http://lib.tkk.fi/Diss/2002/isbn9512261375/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Brandt, Sami. “Theorems and Algorithms for Multiple View Geometry with Applications to Electron Tomography.” 2002. Thesis, Helsinki University of Technology. Accessed January 20, 2021. http://lib.tkk.fi/Diss/2002/isbn9512261375/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Brandt, Sami. “Theorems and Algorithms for Multiple View Geometry with Applications to Electron Tomography.” 2002. Web. 20 Jan 2021.

Vancouver:

Brandt S. Theorems and Algorithms for Multiple View Geometry with Applications to Electron Tomography. [Internet] [Thesis]. Helsinki University of Technology; 2002. [cited 2021 Jan 20]. Available from: http://lib.tkk.fi/Diss/2002/isbn9512261375/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Brandt S. Theorems and Algorithms for Multiple View Geometry with Applications to Electron Tomography. [Thesis]. Helsinki University of Technology; 2002. Available from: http://lib.tkk.fi/Diss/2002/isbn9512261375/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

24. Perlmutter, David. Applications of Statistical Modeling in Iterative CT Image Reconstruction.

Degree: 2015, University of Washington

URL: http://hdl.handle.net/1773/33067

► Traditionally, x-ray CT images are produced by an algorithm called filtered back projection, or FBP. FBP is an analytical solution to the idealized CT image… (more)

Subjects/Keywords: CT; image quality; low dose; reconstruction; signal processing; statistical modeling; Electrical engineering; Biblical studies; Medical imaging and radiology; Electrical engineering

…presentation and quality of the resulting image. The classic reconstruction algorithm for CT is… …dose, leads to higher the image quality. Most statistical methods model x-ray data as either… …patient motion also degrade CT data. One challenge of statistical reconstruction lies in the… …1.2 Statistical Reconstruction Statistical reconstruction is a subset of (though… …Ideally, these estimates will converge to a stable final image. Analytic reconstruction methods…

10 more images…

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Perlmutter, D. (2015). Applications of Statistical Modeling in Iterative CT Image Reconstruction. (Thesis). University of Washington. Retrieved from http://hdl.handle.net/1773/33067

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Perlmutter, David. “Applications of Statistical Modeling in Iterative CT Image Reconstruction.” 2015. Thesis, University of Washington. Accessed January 20, 2021. http://hdl.handle.net/1773/33067.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Perlmutter, David. “Applications of Statistical Modeling in Iterative CT Image Reconstruction.” 2015. Web. 20 Jan 2021.

Vancouver:

Perlmutter D. Applications of Statistical Modeling in Iterative CT Image Reconstruction. [Internet] [Thesis]. University of Washington; 2015. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/1773/33067.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Perlmutter D. Applications of Statistical Modeling in Iterative CT Image Reconstruction. [Thesis]. University of Washington; 2015. Available from: http://hdl.handle.net/1773/33067

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

25. Cho, Jang Hwan. Improving Statistical Image Reconstruction for Cardiac X-ray Computed Tomography.

Degree: PhD, Electrical Engineering: Systems, 2014, University of Michigan

URL: http://hdl.handle.net/2027.42/110319

► Technological advances in CT imaging pose new challenges such as increased X-ray radiation dose and complexity of image reconstruction. Statistical image reconstruction methods use realistic… (more)

▼ Technological advances in CT imaging pose new challenges such as increased X-ray radiation dose and complexity of image reconstruction. Statistical image reconstruction methods use realistic models that incorporate the physics of the measurements and the statistical properties of the measurement noise, and they have potential to provide better image quality and dose reduction compared to the conventional filtered back-projection (FBP) method. However, statistical methods face several challenges that should be addressed before they can replace the FBP method universally. In this thesis, we develop various methods to overcome these challenges of statistical image reconstruction methods. Rigorous regularization design methods in Fourier domain were proposed to achieve more isotropic and uniform spatial resolution or noise properties. The design framework is general so that users can control the spatial resolution and the noise characteristics of the estimator. In addition, a regularization design method based on the hypothetical geometry concept was introduced to improve resolution or noise uniformity. Proposed designs using the new concept effectively improved the spatial resolution or noise uniformity in the reconstructed image. The hypothetical geometry idea is general enough to be applied to other scan geometries. Statistical weighting modification, based on how much each detector element affects insufficiently sampled region, was proposed to reduce the artifacts without degrading the temporal resolution within the region-of-interest (ROI). Another approach using an additional regularization term, that exploits information from the prior image, was investigated. Both methods effectively removed short-scan artifacts in the reconstructed image. We accelerated the family of ordered-subsets algorithms by introducing a double surrogate so that faster convergence speed can be achieved. Furthermore, we present a variable splitting based algorithm for motion-compensated image reconstruction (MCIR) problem that provides faster convergence compared to the conjugate gradient (CG) method. A sinogram-based motion estimation method that does not require any additional measurements other than the short-scan amount of data was introduced to provide decent initial estimates for the joint estimation. Proposed methods were evaluated using simulation and real patient data, and showed promising results for solving each challenge. Some of these methods can be combined to generate more complete solutions for CT imaging. Advisors/Committee Members: Fessler, Jeffrey A. (committee member), Noll, Douglas C. (committee member), Scott, Clayton D. (committee member), Balzano, Laura Kathryn (committee member).

Subjects/Keywords: Statistical image reconstruction for cardiac CT imaging; Regularization designs for isotropic and uniform spatial resolution or noise properties; Short-scan artifact removal using statistical weighting modification or additional prior regularization; Accelerating ordered-subsets (OS) method with double surrogate; Accelerating motion-compensated image reconstruction (MCIR) with variable splitting approach; Regularization designs using the hypothetical geometry; Biomedical Engineering; Electrical Engineering; Engineering (General); Engineering

…76 77 83 83 VI. Accelerated statistical image reconstruction methods… …145 xix ABSTRACT Improving Statistical Image Reconstruction for Cardiac X-ray Computed… …radiation dose and complexity of image reconstruction. Statistical image reconstruction methods… …x5B;131, 134, 136, 144]. Statistical image reconstruction (SIR), also known… …statistical image reconstruction methods and explores ways to overcome the challenges that it is…

10 more images…

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Cho, J. H. (2014). Improving Statistical Image Reconstruction for Cardiac X-ray Computed Tomography. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/110319

Chicago Manual of Style (16^th Edition):

Cho, Jang Hwan. “Improving Statistical Image Reconstruction for Cardiac X-ray Computed Tomography.” 2014. Doctoral Dissertation, University of Michigan. Accessed January 20, 2021. http://hdl.handle.net/2027.42/110319.

MLA Handbook (7^th Edition):

Cho, Jang Hwan. “Improving Statistical Image Reconstruction for Cardiac X-ray Computed Tomography.” 2014. Web. 20 Jan 2021.

Vancouver:

Cho JH. Improving Statistical Image Reconstruction for Cardiac X-ray Computed Tomography. [Internet] [Doctoral dissertation]. University of Michigan; 2014. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2027.42/110319.

Council of Science Editors:

Cho JH. Improving Statistical Image Reconstruction for Cardiac X-ray Computed Tomography. [Doctoral Dissertation]. University of Michigan; 2014. Available from: http://hdl.handle.net/2027.42/110319

ETH Zürich

26. Mishina, Ekaterina. Predictive properties of statistical shape models.

Degree: 2011, ETH Zürich

URL: http://hdl.handle.net/20.500.11850/152717

Subjects/Keywords: BILDREKONSTRUKTION + GESTALTSREKONSTRUKTION (MATHEMATISCHE BILDVERARBEITUNG); OBJEKTMODELLIERUNG (COMPUTERGRAFIK); ABBILDUNGSVERFAHREN + VISUALISIERUNGSVERFAHREN (MEDIZIN); STATISTISCHE MODELLE (MATHEMATISCHE STATISTIK); PROGNOSEN AUF STATISTISCHER BASIS (MATHEMATISCHE STATISTIK); UNVOLLSTÄNDIGE DATEN UND FEHLENDE DATEN (MATHEMATISCHE STATISTIK); IMAGE RECONSTRUCTION + SHAPE RECONSTRUCTION (MATHEMATICAL IMAGE PROCESSING); OBJECT MODELLING (COMPUTER GRAPHICS); MEDICAL IMAGING + MEDICAL VISUALIZATION TECHNIQUES; STATISTICAL MODELS (MATHEMATICAL STATISTICS); FORECASTING BASED ON STATISTICS (MATHEMATICAL STATISTICS); INCOMPLETE DATA AND MISSING DATA (MATHEMATICAL STATISTICS); info:eu-repo/classification/ddc/510; info:eu-repo/classification/ddc/004; Mathematics; Data processing, computer science

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Mishina, E. (2011). Predictive properties of statistical shape models. (Doctoral Dissertation). ETH Zürich. Retrieved from http://hdl.handle.net/20.500.11850/152717

Chicago Manual of Style (16^th Edition):

Mishina, Ekaterina. “Predictive properties of statistical shape models.” 2011. Doctoral Dissertation, ETH Zürich. Accessed January 20, 2021. http://hdl.handle.net/20.500.11850/152717.

MLA Handbook (7^th Edition):

Mishina, Ekaterina. “Predictive properties of statistical shape models.” 2011. Web. 20 Jan 2021.

Vancouver:

Mishina E. Predictive properties of statistical shape models. [Internet] [Doctoral dissertation]. ETH Zürich; 2011. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/20.500.11850/152717.

Council of Science Editors:

Mishina E. Predictive properties of statistical shape models. [Doctoral Dissertation]. ETH Zürich; 2011. Available from: http://hdl.handle.net/20.500.11850/152717

		in
And / Or
		in
And / Or
		in
And / Or
		in

Open Access Theses and Dissertations