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You searched for +publisher:"Georgia Tech" +contributor:("Skrinjar, Oskar"). Showing records 1 – 10 of 10 total matches.

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1. Rehman, Tauseef ur. Efficient numerical method for solution of L² optimal mass transport problem.

Degree: PhD, Electrical and Computer Engineering, 2010, Georgia Tech

 In this thesis, a novel and efficient numerical method is presented for the computation of the L² optimal mass transport mapping in two and three… (more)

Subjects/Keywords: Optimal mass transport; Image registration; Image morphing; Active contour tracking; Mass transfer; Differential equations, Linear Numerical solutions

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APA (6th Edition):

Rehman, T. u. (2010). Efficient numerical method for solution of L² optimal mass transport problem. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/33891

Chicago Manual of Style (16th Edition):

Rehman, Tauseef ur. “Efficient numerical method for solution of L² optimal mass transport problem.” 2010. Doctoral Dissertation, Georgia Tech. Accessed March 07, 2021. http://hdl.handle.net/1853/33891.

MLA Handbook (7th Edition):

Rehman, Tauseef ur. “Efficient numerical method for solution of L² optimal mass transport problem.” 2010. Web. 07 Mar 2021.

Vancouver:

Rehman Tu. Efficient numerical method for solution of L² optimal mass transport problem. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1853/33891.

Council of Science Editors:

Rehman Tu. Efficient numerical method for solution of L² optimal mass transport problem. [Doctoral Dissertation]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/33891

2. Fornwalt, Brandon Kenneth. New methods for quantifying the synchrony of contraction and relaxation in the heart.

Degree: PhD, Biomedical Engineering, 2008, Georgia Tech

 Synchronous contraction and relaxation of the myocardium is required to optimize cardiac function. Regional timing of contraction and relaxation is dyssynchronous in many patients with… (more)

Subjects/Keywords: Tissue doppler; Echocardiography; Heart failure; Magnetic resonance imaging; Ventricular asynchrony; Ventricular dyssynchrony; Cardiac resynchronization; Coincidence; Heart beat; Heart Contraction; Diastole (Cardiac cycle); Cardiac pacing; Heart Left ventricle Diseases Treatment

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APA (6th Edition):

Fornwalt, B. K. (2008). New methods for quantifying the synchrony of contraction and relaxation in the heart. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/24800

Chicago Manual of Style (16th Edition):

Fornwalt, Brandon Kenneth. “New methods for quantifying the synchrony of contraction and relaxation in the heart.” 2008. Doctoral Dissertation, Georgia Tech. Accessed March 07, 2021. http://hdl.handle.net/1853/24800.

MLA Handbook (7th Edition):

Fornwalt, Brandon Kenneth. “New methods for quantifying the synchrony of contraction and relaxation in the heart.” 2008. Web. 07 Mar 2021.

Vancouver:

Fornwalt BK. New methods for quantifying the synchrony of contraction and relaxation in the heart. [Internet] [Doctoral dissertation]. Georgia Tech; 2008. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1853/24800.

Council of Science Editors:

Fornwalt BK. New methods for quantifying the synchrony of contraction and relaxation in the heart. [Doctoral Dissertation]. Georgia Tech; 2008. Available from: http://hdl.handle.net/1853/24800


Georgia Tech

3. Arumuganainar, Ponnappan. Automatic soft plaque detection from CTA.

Degree: MS, Biomedical Engineering, 2008, Georgia Tech

 This thesis explores two possible ways of detecting soft plaque present in the coronary arteries, using CTA imagery. The coronary arteries are vessels that supply… (more)

Subjects/Keywords: Atherosclerosis; Soft plaque; Level set methods; Active contours; Image segmentation; Medical image processing; CT angiography; Watershed transform; Atherosclerotic plaque; Blood-vessels Imaging; Angiography

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APA (6th Edition):

Arumuganainar, P. (2008). Automatic soft plaque detection from CTA. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/26690

Chicago Manual of Style (16th Edition):

Arumuganainar, Ponnappan. “Automatic soft plaque detection from CTA.” 2008. Masters Thesis, Georgia Tech. Accessed March 07, 2021. http://hdl.handle.net/1853/26690.

MLA Handbook (7th Edition):

Arumuganainar, Ponnappan. “Automatic soft plaque detection from CTA.” 2008. Web. 07 Mar 2021.

Vancouver:

Arumuganainar P. Automatic soft plaque detection from CTA. [Internet] [Masters thesis]. Georgia Tech; 2008. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1853/26690.

Council of Science Editors:

Arumuganainar P. Automatic soft plaque detection from CTA. [Masters Thesis]. Georgia Tech; 2008. Available from: http://hdl.handle.net/1853/26690


Georgia Tech

4. Kasimoglu, Ismail Hakki. Estimation of a Coronary Vessel Wall Deformation with High-Frequency Ultrasound Elastography.

Degree: PhD, Electrical and Computer Engineering, 2007, Georgia Tech

 Elastography, which is based on applying pressure and estimating the resulting deformation, involves the forward problem to obtain the strain distributions and inverse problem to… (more)

Subjects/Keywords: Elastography; High-frequency ultrasound; Image acquisition; Image segmentation; Porcine coronary artery; Stopping function; Geodesic active contour; Local phase; Level set; Ultrasonics in medicine; Coronary arteries Mechanical properties; Elasticity; Strains and stresses

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APA (6th Edition):

Kasimoglu, I. H. (2007). Estimation of a Coronary Vessel Wall Deformation with High-Frequency Ultrasound Elastography. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/19762

Chicago Manual of Style (16th Edition):

Kasimoglu, Ismail Hakki. “Estimation of a Coronary Vessel Wall Deformation with High-Frequency Ultrasound Elastography.” 2007. Doctoral Dissertation, Georgia Tech. Accessed March 07, 2021. http://hdl.handle.net/1853/19762.

MLA Handbook (7th Edition):

Kasimoglu, Ismail Hakki. “Estimation of a Coronary Vessel Wall Deformation with High-Frequency Ultrasound Elastography.” 2007. Web. 07 Mar 2021.

Vancouver:

Kasimoglu IH. Estimation of a Coronary Vessel Wall Deformation with High-Frequency Ultrasound Elastography. [Internet] [Doctoral dissertation]. Georgia Tech; 2007. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1853/19762.

Council of Science Editors:

Kasimoglu IH. Estimation of a Coronary Vessel Wall Deformation with High-Frequency Ultrasound Elastography. [Doctoral Dissertation]. Georgia Tech; 2007. Available from: http://hdl.handle.net/1853/19762


Georgia Tech

5. Bistoquet, Arnaud. Cardiac motion recovery from magnetic resonance images using incompressible deformable models.

Degree: PhD, Electrical and Computer Engineering, 2008, Georgia Tech

 The study of myocardial motion is essential for understanding the normal heart function and developing new treatments for cardiovascular diseases. The goals of my PhD… (more)

Subjects/Keywords: Cardiac deformation recovery; Incompressibility; Cardiac MRI; Heart Imaging; Heart Diseases Diagnosis Equipment and supplies; Magnetic resonance imaging; Diagnostic imaging; Imaging systems in medicine

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APA (6th Edition):

Bistoquet, A. (2008). Cardiac motion recovery from magnetic resonance images using incompressible deformable models. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/24628

Chicago Manual of Style (16th Edition):

Bistoquet, Arnaud. “Cardiac motion recovery from magnetic resonance images using incompressible deformable models.” 2008. Doctoral Dissertation, Georgia Tech. Accessed March 07, 2021. http://hdl.handle.net/1853/24628.

MLA Handbook (7th Edition):

Bistoquet, Arnaud. “Cardiac motion recovery from magnetic resonance images using incompressible deformable models.” 2008. Web. 07 Mar 2021.

Vancouver:

Bistoquet A. Cardiac motion recovery from magnetic resonance images using incompressible deformable models. [Internet] [Doctoral dissertation]. Georgia Tech; 2008. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1853/24628.

Council of Science Editors:

Bistoquet A. Cardiac motion recovery from magnetic resonance images using incompressible deformable models. [Doctoral Dissertation]. Georgia Tech; 2008. Available from: http://hdl.handle.net/1853/24628


Georgia Tech

6. Burrell, Lauren S. Feature analysis of functional mri data for mapping epileptic networks.

Degree: PhD, Electrical and Computer Engineering, 2008, Georgia Tech

 This research focused on the development of a methodology for analyzing functional magnetic resonance imaging (fMRI) data collected from patients with epilepsy in order to… (more)

Subjects/Keywords: Feature extraction; Brain mapping; Feature fusion; Magnetic resonance imaging; Brain – Localization of functions

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APA (6th Edition):

Burrell, L. S. (2008). Feature analysis of functional mri data for mapping epileptic networks. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/26528

Chicago Manual of Style (16th Edition):

Burrell, Lauren S. “Feature analysis of functional mri data for mapping epileptic networks.” 2008. Doctoral Dissertation, Georgia Tech. Accessed March 07, 2021. http://hdl.handle.net/1853/26528.

MLA Handbook (7th Edition):

Burrell, Lauren S. “Feature analysis of functional mri data for mapping epileptic networks.” 2008. Web. 07 Mar 2021.

Vancouver:

Burrell LS. Feature analysis of functional mri data for mapping epileptic networks. [Internet] [Doctoral dissertation]. Georgia Tech; 2008. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1853/26528.

Council of Science Editors:

Burrell LS. Feature analysis of functional mri data for mapping epileptic networks. [Doctoral Dissertation]. Georgia Tech; 2008. Available from: http://hdl.handle.net/1853/26528


Georgia Tech

7. Mohan, Vandana. Computer vision and machine learning methods for the analysis of brain and cardiac imagery.

Degree: PhD, Electrical and Computer Engineering, 2010, Georgia Tech

 Medical imagery is increasingly evolving towards higher resolution and throughput. The increasing volume of data and the usage of multiple and often novel imaging modalities… (more)

Subjects/Keywords: Schizophrenia detection; Cingulum bundle; Tubular surface segmentation; Branch detection; Mass spectrometry analysis; Tumor boundary detection; Biomarker detection; Glioma; Tumor classification; DESI; Vessel segmentation; Medical imaging; Fiber bundle segmentation; Computer vision; Diagnostic imaging; Computer vision in medicine; Image processing; Machine learning

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APA (6th Edition):

Mohan, V. (2010). Computer vision and machine learning methods for the analysis of brain and cardiac imagery. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/39628

Chicago Manual of Style (16th Edition):

Mohan, Vandana. “Computer vision and machine learning methods for the analysis of brain and cardiac imagery.” 2010. Doctoral Dissertation, Georgia Tech. Accessed March 07, 2021. http://hdl.handle.net/1853/39628.

MLA Handbook (7th Edition):

Mohan, Vandana. “Computer vision and machine learning methods for the analysis of brain and cardiac imagery.” 2010. Web. 07 Mar 2021.

Vancouver:

Mohan V. Computer vision and machine learning methods for the analysis of brain and cardiac imagery. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1853/39628.

Council of Science Editors:

Mohan V. Computer vision and machine learning methods for the analysis of brain and cardiac imagery. [Doctoral Dissertation]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/39628


Georgia Tech

8. Sundareswaran, Kartik Sivaram. Characterizing single ventricle hemodynamics using phase contrast magnetic resonance imaging.

Degree: PhD, Biomedical Engineering, 2008, Georgia Tech

 Single ventricle congenital heart defects afflict 2 per every 1000 births. They are characterized by cyanotic mixing between the de-oxygenated blood coming back from the… (more)

Subjects/Keywords: Congenital heart disease; Phase contrast magnetic resonance imaging; Image processing; Blood flow; Hemodynamics; Magnetic resonance imaging; Fluid dynamics; Newtonian fluids; Ventricular remodeling

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APA (6th Edition):

Sundareswaran, K. S. (2008). Characterizing single ventricle hemodynamics using phase contrast magnetic resonance imaging. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/31748

Chicago Manual of Style (16th Edition):

Sundareswaran, Kartik Sivaram. “Characterizing single ventricle hemodynamics using phase contrast magnetic resonance imaging.” 2008. Doctoral Dissertation, Georgia Tech. Accessed March 07, 2021. http://hdl.handle.net/1853/31748.

MLA Handbook (7th Edition):

Sundareswaran, Kartik Sivaram. “Characterizing single ventricle hemodynamics using phase contrast magnetic resonance imaging.” 2008. Web. 07 Mar 2021.

Vancouver:

Sundareswaran KS. Characterizing single ventricle hemodynamics using phase contrast magnetic resonance imaging. [Internet] [Doctoral dissertation]. Georgia Tech; 2008. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1853/31748.

Council of Science Editors:

Sundareswaran KS. Characterizing single ventricle hemodynamics using phase contrast magnetic resonance imaging. [Doctoral Dissertation]. Georgia Tech; 2008. Available from: http://hdl.handle.net/1853/31748


Georgia Tech

9. Ozan, Cem. Mechanical modeling of brain and breast tissue.

Degree: PhD, Civil and Environmental Engineering, 2008, Georgia Tech

 We propose a new approach for defining mechanical properties of the brain tissue in-vivo by taking MRI or CT images of a brain response to… (more)

Subjects/Keywords: Human brain; Female breast; Subdural hematoma; Ventriculostomy; Breast augmentation; Tissues – Models; Deformations (Mechanics); Imaging systems in medicine; Brain – Mechanical properties; Breast – Mechanical properties; Subdural hematoma

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APA (6th Edition):

Ozan, C. (2008). Mechanical modeling of brain and breast tissue. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/22632

Chicago Manual of Style (16th Edition):

Ozan, Cem. “Mechanical modeling of brain and breast tissue.” 2008. Doctoral Dissertation, Georgia Tech. Accessed March 07, 2021. http://hdl.handle.net/1853/22632.

MLA Handbook (7th Edition):

Ozan, Cem. “Mechanical modeling of brain and breast tissue.” 2008. Web. 07 Mar 2021.

Vancouver:

Ozan C. Mechanical modeling of brain and breast tissue. [Internet] [Doctoral dissertation]. Georgia Tech; 2008. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1853/22632.

Council of Science Editors:

Ozan C. Mechanical modeling of brain and breast tissue. [Doctoral Dissertation]. Georgia Tech; 2008. Available from: http://hdl.handle.net/1853/22632


Georgia Tech

10. Kitajima, Hiroumi D. In Vitro Fluid Dynamics of Stereolithographic Single Ventricle Congenital Heart Defects From In Vivo Magnetic Resonance Imaging.

Degree: PhD, Biomedical Engineering, 2007, Georgia Tech

 Background: Single ventricle congenital heart defects with cyanotic mixing between systemic and pulmonary circulations afflict 2 per 1000 live births. Following the atriopulmonary connection proposed… (more)

Subjects/Keywords: Patient-specific modeling; Congenital heart disease; Cardiovascular fluid dynamics; Segmentation; Image processing; Phase contrast magnetic resonance imaging; Computer-aided design; Rapid prototyping; Stereolithography; Particle image velocimetry; Congenital heart disease; Magnetic resonance imaging; Fluid dynamics; Hemodynamics; Surgery

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APA (6th Edition):

Kitajima, H. D. (2007). In Vitro Fluid Dynamics of Stereolithographic Single Ventricle Congenital Heart Defects From In Vivo Magnetic Resonance Imaging. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/25074

Chicago Manual of Style (16th Edition):

Kitajima, Hiroumi D. “In Vitro Fluid Dynamics of Stereolithographic Single Ventricle Congenital Heart Defects From In Vivo Magnetic Resonance Imaging.” 2007. Doctoral Dissertation, Georgia Tech. Accessed March 07, 2021. http://hdl.handle.net/1853/25074.

MLA Handbook (7th Edition):

Kitajima, Hiroumi D. “In Vitro Fluid Dynamics of Stereolithographic Single Ventricle Congenital Heart Defects From In Vivo Magnetic Resonance Imaging.” 2007. Web. 07 Mar 2021.

Vancouver:

Kitajima HD. In Vitro Fluid Dynamics of Stereolithographic Single Ventricle Congenital Heart Defects From In Vivo Magnetic Resonance Imaging. [Internet] [Doctoral dissertation]. Georgia Tech; 2007. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1853/25074.

Council of Science Editors:

Kitajima HD. In Vitro Fluid Dynamics of Stereolithographic Single Ventricle Congenital Heart Defects From In Vivo Magnetic Resonance Imaging. [Doctoral Dissertation]. Georgia Tech; 2007. Available from: http://hdl.handle.net/1853/25074

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