Multi-compartment model analysis in diffusion tensor imaging
Diffusion Weighted Imaging (DWI) and Diffusion Tensor Imaging (DTI) are newly emerging techniques in Magnetic Resonance Imaging (MRI). These techniques enable studying connectivity and fibre orientations in different regions of the brain and also detecting abnormalities due to pathological condition...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
2012
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/50594 |
| _version_ | 1826126426723581952 |
|---|---|
| author | Rashi Samur |
| author2 | Vitali Zagorodnov |
| author_facet | Vitali Zagorodnov Rashi Samur |
| author_sort | Rashi Samur |
| collection | NTU |
| description | Diffusion Weighted Imaging (DWI) and Diffusion Tensor Imaging (DTI) are newly emerging techniques in Magnetic Resonance Imaging (MRI). These techniques enable studying connectivity and fibre orientations in different regions of the brain and also detecting abnormalities due to pathological conditions and physiological defects in the brain, which were not possible with conventional MRI techniques. One challenging area of research in DWI is the estimation of multiple diffusion compartments within individual voxels of a DWI image. The concept behind this estimation is that different regions of the brain have different diffusivities, and a single voxel in the image can contain more than one such diffusion component. This report presents our thorough study of multi-compartment estimation problem in DWI, focusing primarily on two compartment estimation problem due to two major types of diffusion compartments – water diffusion and vascular blood flow. There are two methods of estimation proposed in this study, both achieving accurate results when compared with the ground truth values. The algorithms are tested both on synthetic data as well as on actual brain data. |
| first_indexed | 2024-10-01T06:52:27Z |
| format | Thesis |
| id | ntu-10356/50594 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:52:27Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | ntu-10356/505942023-03-04T00:37:00Z Multi-compartment model analysis in diffusion tensor imaging Rashi Samur Vitali Zagorodnov School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision DRNTU::Engineering::Computer science and engineering::Mathematics of computing::Probability and statistics Diffusion Weighted Imaging (DWI) and Diffusion Tensor Imaging (DTI) are newly emerging techniques in Magnetic Resonance Imaging (MRI). These techniques enable studying connectivity and fibre orientations in different regions of the brain and also detecting abnormalities due to pathological conditions and physiological defects in the brain, which were not possible with conventional MRI techniques. One challenging area of research in DWI is the estimation of multiple diffusion compartments within individual voxels of a DWI image. The concept behind this estimation is that different regions of the brain have different diffusivities, and a single voxel in the image can contain more than one such diffusion component. This report presents our thorough study of multi-compartment estimation problem in DWI, focusing primarily on two compartment estimation problem due to two major types of diffusion compartments – water diffusion and vascular blood flow. There are two methods of estimation proposed in this study, both achieving accurate results when compared with the ground truth values. The algorithms are tested both on synthetic data as well as on actual brain data. MASTER OF ENGINEERING (SCE) 2012-07-17T01:58:17Z 2012-07-17T01:58:17Z 2012 2012 Thesis Rashi, S. (2012). Multi-compartment model analysis in diffusion tensor imaging. Master’s thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/50594 10.32657/10356/50594 en 59 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision DRNTU::Engineering::Computer science and engineering::Mathematics of computing::Probability and statistics Rashi Samur Multi-compartment model analysis in diffusion tensor imaging |
| title | Multi-compartment model analysis in diffusion tensor imaging |
| title_full | Multi-compartment model analysis in diffusion tensor imaging |
| title_fullStr | Multi-compartment model analysis in diffusion tensor imaging |
| title_full_unstemmed | Multi-compartment model analysis in diffusion tensor imaging |
| title_short | Multi-compartment model analysis in diffusion tensor imaging |
| title_sort | multi compartment model analysis in diffusion tensor imaging |
| topic | DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision DRNTU::Engineering::Computer science and engineering::Mathematics of computing::Probability and statistics |
| url | https://hdl.handle.net/10356/50594 |
| work_keys_str_mv | AT rashisamur multicompartmentmodelanalysisindiffusiontensorimaging |