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You searched for +publisher:"Rutgers University" +contributor:("Rosen, Mark"). Showing records 1 – 3 of 3 total matches.

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Rutgers University

1. Chappelow, Jonathan, 1980-. Multimodal image registration using multivariate information theoretic similarity measures: applications to prostate cancer diagnosis and targeted treatment.

Degree: PhD, Biomedical Engineering, 2011, Rutgers University

Multimodal and multiprotocol image registration refers to the process of alignment of two or more images obtained from different imaging modalities (e.g. digitized histology and MRI) and protocols (e.g. T2-w and PD-w MRI). Registration is a critical component in medical applications including image guided surgery, image fusion for cancer diagnosis and treatment planning, and automated tissue annotation. However, registration is often complicated on account of differences in both the image intensities and the shape of the underlying anatomy. For example, non-linear differences in the overall shape of the prostate between in vivo MRI and ex vivo whole mount histology (WMH) often exist as a result of the presence of an endorectal coil during pre-operative MR imaging and deformations to the specimen during slide preparation. To overcome these challenges, we present new registration techniques termed Combined Feature Ensemble Mutual Information (COFEMI) and Collection of Image-derived Non-linear Attributes for Registration Using Splines (COLLINARUS). The goal COFEMI is to provide a similarity measure that is driven by unique low level textural features, for registration that is more robust to intensity artifacts and modality differences than measures restricted to intensities alone. COLLINARUS offers the robustness of COFEMI to artifacts and modality differences, while allowing fully automated non-linear image warping at multiple scales via a hierarchical B-spline mesh grid. In addition, since routine clinical imaging procedures often involve the acquisition of multiple imaging protocols, we present a technique termed Multi-attribute Combined Mutual Information (MACAMI) to leverage the availability of multiple image sets to improve registration. We apply our registration techniques to a unique clinical dataset comprising 150 sets of in vivo MRI and post-operative WMH images from 25 patient studies in order to retrospectively establish the spatial extent of prostate cancer (CaP) on structural (T2-w) and functional (DCE) in vivo MRI. Accurate mapping of CaP on MRI is used to facilitate the development and evaluation of a system for computer-assisted detection (CAD) of CaP on multiprotocol MRI. We also demonstrate our registration and CAD algorithms in developing radiation therapy treatment plans that provide dose escalation to CaP by elastically registering diagnostic MRI with planning CT.

Advisors/Committee Members: Chappelow, Jonathan, 1980- (author), Madabhushi, Anant (chair), Bhanot, Gyan (internal member), Boustany, Nada (internal member), Bloch, B Nicolas (outside member), Tomaszewski, John E (outside member), Rosen, Mark (outside member).

Subjects/Keywords: Imaging systems in medicine; Prostate—Cancer – Imaging; Prostate—Cancer – Treatment

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APA (6th Edition):

Chappelow, Jonathan, 1. (2011). Multimodal image registration using multivariate information theoretic similarity measures: applications to prostate cancer diagnosis and targeted treatment. (Doctoral Dissertation). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061604

Chicago Manual of Style (16th Edition):

Chappelow, Jonathan, 1980-. “Multimodal image registration using multivariate information theoretic similarity measures: applications to prostate cancer diagnosis and targeted treatment.” 2011. Doctoral Dissertation, Rutgers University. Accessed December 06, 2019. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061604.

MLA Handbook (7th Edition):

Chappelow, Jonathan, 1980-. “Multimodal image registration using multivariate information theoretic similarity measures: applications to prostate cancer diagnosis and targeted treatment.” 2011. Web. 06 Dec 2019.

Vancouver:

Chappelow, Jonathan 1. Multimodal image registration using multivariate information theoretic similarity measures: applications to prostate cancer diagnosis and targeted treatment. [Internet] [Doctoral dissertation]. Rutgers University; 2011. [cited 2019 Dec 06]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061604.

Council of Science Editors:

Chappelow, Jonathan 1. Multimodal image registration using multivariate information theoretic similarity measures: applications to prostate cancer diagnosis and targeted treatment. [Doctoral Dissertation]. Rutgers University; 2011. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061604

2. Agner, Shannon Christine, 1980-. A computerized image analysis framework for dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) with applications to breast cancer.

Degree: PhD, Biomedical Engineering, 2011, Rutgers University

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) provides a wealth of information about the anatomy of the breast, particularly in the setting of breast cancer diagnosis. In addition to the images it provides regarding the architecture of breast tissue, it also provides functional information about blood flow by means of the DCE study. The sensitivity of DCE-MRI has been reported at close to 100%, so the difficult tasks for the radiologist in reviewing breast DCE-MRI are: (1) discerning between which lesions are benign and which are malignant; and (2) doing so for a patient study that involves hundreds of images and is 4-dimensional. Because of the great detail and volume of information DCE-MRI provides, computational methods for both extracting and analyzing information derived from the images are useful in distilling the entire patient study down to the most salient images and features for the radiologist to examine. In this dissertation, computer-based methods developed for analyzing the data acquired in a breast DCE-MRI patient study are described. In the first part, pre-processing methods used for aligning the images of the timedependent DCE study are explained. Because segmentation is important for describing the morphology of the lesion as well as the region of interest for any subsequent quantitative analysis of a lesion, as a second step to pre-processing, a spectral embedding based active contour (SEAC) method for segmentation of lesions is developed and tested. A feature developed for extracting the spatiotemporal characteristics of breast lesions, termed textural kinetics, is then described, and its utility is demonstrated for distinguishing benign from malignant lesions as well as in identifying triple negative breast lesions, a lesion type that is extremely aggressive and has no targeted therapies. Finally, these quantitative methods are summarized in a computer aided diagnosis framework that provides insight into the biologic nature of breast lesion subtypes as well as for directing treatment and determining prognosis.

Advisors/Committee Members: Agner, Shannon Christine, 1980- (author), Madabhushi, Anant (chair), Ganesan, Shridar (internal member), Cai, Li (internal member), Rosen, Mark A (outside member), Feldman, Michael D (outside member).

Subjects/Keywords: Breast – Magnetic resonance imaging; Breast – Cancer – Magnetic resonance imaging

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

APA (6th Edition):

Agner, Shannon Christine, 1. (2011). A computerized image analysis framework for dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) with applications to breast cancer. (Doctoral Dissertation). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000060977

Chicago Manual of Style (16th Edition):

Agner, Shannon Christine, 1980-. “A computerized image analysis framework for dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) with applications to breast cancer.” 2011. Doctoral Dissertation, Rutgers University. Accessed December 06, 2019. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000060977.

MLA Handbook (7th Edition):

Agner, Shannon Christine, 1980-. “A computerized image analysis framework for dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) with applications to breast cancer.” 2011. Web. 06 Dec 2019.

Vancouver:

Agner, Shannon Christine 1. A computerized image analysis framework for dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) with applications to breast cancer. [Internet] [Doctoral dissertation]. Rutgers University; 2011. [cited 2019 Dec 06]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000060977.

Council of Science Editors:

Agner, Shannon Christine 1. A computerized image analysis framework for dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) with applications to breast cancer. [Doctoral Dissertation]. Rutgers University; 2011. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000060977


Rutgers University

3. Tiwari, Pallavi. A hierarchical spectral clustering and non-linear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy:.

Degree: MS, Biomedical Engineering, 2008, Rutgers University

Magnetic Resonance Spectroscopy (MRS) is a unique non-invasive method which has recently been shown to have great potential in screening of prostate cancer (CaP). MRS provides functional information regarding the concentrations of different biochemicals present in the prostate at single or multiple locations within a rectangular grid of spectra superposed on the structural T2-weighted Magnetic Resonance Imaging (MRI). Changes in relative concentration of specific metabolites including choline, creatine and citrate compared to "normal" levels is highly indicative of the presence of CaP. Most previous attempts at developing computerized schemes for automated prostate cancer detection using MRS have been centered on developing peak area quantification algorithms. These methods seek to obtain area under peaks corresponding to choline, creatine and citrate which is then used to compute relative concentrations of these metabolites. However, manual identification of metabolite peaks on the MR spectra, let alone via automated algorithms, is a challenging problem on account of low SNR, baseline irregularity, peak-overlap, and peak distortion. In this thesis work a novel computer aided detection (CAD) scheme for prostate MRS is presented that integrates non-linear dimensionality reduction (NLDR) with an unsupervised hierarchical clustering algorithm to automatically identify cancerous spectra. The methodology comprises of two specific aims. Aim 1 is to first automatically localize the prostate region followed in Aim 2 by automated cancer detection on the prostate obtained in Aim 1. In Aim 1, a hierarchical spectral clustering algorithm is used to distinguish between informative and non-informative spectra in order to localize the region of interest (ROI) corresponding to the prostate. Once the prostate ROI is localized, in Aim 2, a non-linear dimensionality reduction (NLDR) scheme in conjunction with a replicated k-means clustering algorithm is used to automatically discriminate between 3 classes of spectra (normal, CaP, and intermediate tissue classes). Results of qualitative and quantitative evaluation of the methodology over 18 1.5 Tesla (T) in-vivo prostate T2-w and MRS studies obtained from the multi-site, multi-institutional ACRIN trial, for which corresponding histological ground truth of spatial extent of CaP is available, reveal that the CAD scheme has a high detection sensitivity (89.60) and specificity (78.98). Results further suggest that the CAD scheme has a higher detection accuracy compared to such commonly used MRS analysis schemes as z-score and PCA. Advisors/Committee Members: Tiwari, Pallavi (author), Cai, Li (chair), Madabhushi, Anant (internal member), Kim, Sobin (internal member), Rosen, Mark (outside member).

Subjects/Keywords: Prostate – Cancer; Magnetic resonance imaging

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

APA (6th Edition):

Tiwari, P. (2008). A hierarchical spectral clustering and non-linear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy:. (Masters Thesis). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000050467a

Chicago Manual of Style (16th Edition):

Tiwari, Pallavi. “A hierarchical spectral clustering and non-linear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy:.” 2008. Masters Thesis, Rutgers University. Accessed December 06, 2019. http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000050467a.

MLA Handbook (7th Edition):

Tiwari, Pallavi. “A hierarchical spectral clustering and non-linear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy:.” 2008. Web. 06 Dec 2019.

Vancouver:

Tiwari P. A hierarchical spectral clustering and non-linear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy:. [Internet] [Masters thesis]. Rutgers University; 2008. [cited 2019 Dec 06]. Available from: http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000050467a.

Council of Science Editors:

Tiwari P. A hierarchical spectral clustering and non-linear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy:. [Masters Thesis]. Rutgers University; 2008. Available from: http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000050467a

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