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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.19497 |
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| _version_ | 1866911941716344832 |
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| author | Khandelwal, Pulkit Duong, Michael Tran Levorse, Lisa Fuentes, Constanza Denning, Amanda Trotman, Winifred Ittyerah, Ranjit Bahena, Alejandra Schuck, Theresa Gabrielyan, Marianna Prabhakaran, Karthik Ohm, Daniel Mizsei, Gabor Robinson, John Munoz, Monica Detre, John Lee, Edward Irwin, David McMillan, Corey Tisdall, M. Dylan Das, Sandhitsu Wolk, David Yushkevich, Paul A. |
| author_facet | Khandelwal, Pulkit Duong, Michael Tran Levorse, Lisa Fuentes, Constanza Denning, Amanda Trotman, Winifred Ittyerah, Ranjit Bahena, Alejandra Schuck, Theresa Gabrielyan, Marianna Prabhakaran, Karthik Ohm, Daniel Mizsei, Gabor Robinson, John Munoz, Monica Detre, John Lee, Edward Irwin, David McMillan, Corey Tisdall, M. Dylan Das, Sandhitsu Wolk, David Yushkevich, Paul A. |
| contents | Magnetic resonance imaging (MRI) is the standard modality to understand human brain structure and function in vivo (antemortem). Decades of research in human neuroimaging has led to the widespread development of methods and tools to provide automated volume-based segmentations and surface-based parcellations which help localize brain functions to specialized anatomical regions. Recently ex vivo (postmortem) imaging of the brain has opened-up avenues to study brain structure at sub-millimeter ultra high-resolution revealing details not possible to observe with in vivo MRI. Unfortunately, there has been limited methodological development in ex vivo MRI primarily due to lack of datasets and limited centers with such imaging resources. Therefore, in this work, we present one-of-its-kind dataset of 82 ex vivo T2w whole brain hemispheres MRI at 0.3 mm isotropic resolution spanning Alzheimer's disease and related dementias. We adapted and developed a fast and easy-to-use automated surface-based pipeline to parcellate, for the first time, ultra high-resolution ex vivo brain tissue at the native subject space resolution using the Desikan-Killiany-Tourville (DKT) brain atlas. This allows us to perform vertex-wise analysis in the template space and thereby link morphometry measures with pathology measurements derived from histology. We will open-source our dataset docker container, Jupyter notebooks for ready-to-use out-of-the-box set of tools and command line options to advance ex vivo MRI clinical brain imaging research on the project webpage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19497 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Surface-based parcellation and vertex-wise analysis of ultra high-resolution ex vivo 7 tesla MRI in Alzheimer's disease and related dementias Khandelwal, Pulkit Duong, Michael Tran Levorse, Lisa Fuentes, Constanza Denning, Amanda Trotman, Winifred Ittyerah, Ranjit Bahena, Alejandra Schuck, Theresa Gabrielyan, Marianna Prabhakaran, Karthik Ohm, Daniel Mizsei, Gabor Robinson, John Munoz, Monica Detre, John Lee, Edward Irwin, David McMillan, Corey Tisdall, M. Dylan Das, Sandhitsu Wolk, David Yushkevich, Paul A. Computer Vision and Pattern Recognition Magnetic resonance imaging (MRI) is the standard modality to understand human brain structure and function in vivo (antemortem). Decades of research in human neuroimaging has led to the widespread development of methods and tools to provide automated volume-based segmentations and surface-based parcellations which help localize brain functions to specialized anatomical regions. Recently ex vivo (postmortem) imaging of the brain has opened-up avenues to study brain structure at sub-millimeter ultra high-resolution revealing details not possible to observe with in vivo MRI. Unfortunately, there has been limited methodological development in ex vivo MRI primarily due to lack of datasets and limited centers with such imaging resources. Therefore, in this work, we present one-of-its-kind dataset of 82 ex vivo T2w whole brain hemispheres MRI at 0.3 mm isotropic resolution spanning Alzheimer's disease and related dementias. We adapted and developed a fast and easy-to-use automated surface-based pipeline to parcellate, for the first time, ultra high-resolution ex vivo brain tissue at the native subject space resolution using the Desikan-Killiany-Tourville (DKT) brain atlas. This allows us to perform vertex-wise analysis in the template space and thereby link morphometry measures with pathology measurements derived from histology. We will open-source our dataset docker container, Jupyter notebooks for ready-to-use out-of-the-box set of tools and command line options to advance ex vivo MRI clinical brain imaging research on the project webpage. |
| title | Surface-based parcellation and vertex-wise analysis of ultra high-resolution ex vivo 7 tesla MRI in Alzheimer's disease and related dementias |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.19497 |