Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy
We have developed a method for automated probabilistic reconstruction of a set of major white-matter pathways from diffusion-weighted MR images. Our method is called TRACULA (TRActs Constrained by UnderLying Anatomy) and utilizes prior information on the anatomy of the pathways from a set of trainin...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | en_US |
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Frontiers Research Foundation
2017
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| Online Access: | http://hdl.handle.net/1721.1/107436 |
| _version_ | 1810974113629470720 |
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| author | Yendiki, Anastasia Panneck, Patricia Srinivasan, Priti Stevens, Allison Zollei, Lilla Augustinack, Jean Wang, Ruopeng Salat, David Ehrlich, Stefan Behrens, Tim Jbabdi, Saad Gollub, Randy Fischl, Bruce |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Yendiki, Anastasia Panneck, Patricia Srinivasan, Priti Stevens, Allison Zollei, Lilla Augustinack, Jean Wang, Ruopeng Salat, David Ehrlich, Stefan Behrens, Tim Jbabdi, Saad Gollub, Randy Fischl, Bruce |
| author_sort | Yendiki, Anastasia |
| collection | MIT |
| description | We have developed a method for automated probabilistic reconstruction of a set of major white-matter pathways from diffusion-weighted MR images. Our method is called TRACULA (TRActs Constrained by UnderLying Anatomy) and utilizes prior information on the anatomy of the pathways from a set of training subjects. By incorporating this prior knowledge in the reconstruction procedure, our method obviates the need for manual interaction with the tract solutions at a later stage and thus facilitates the application of tractography to large studies. In this paper we illustrate the application of the method on data from a schizophrenia study and investigate whether the inclusion of both patients and healthy subjects in the training set affects our ability to reconstruct the pathways reliably. We show that, since our method does not constrain the exact spatial location or shape of the pathways but only their trajectory relative to the surrounding anatomical structures, a set a of healthy training subjects can be used to reconstruct the pathways accurately in patients as well as in controls. |
| first_indexed | 2024-09-23T08:18:19Z |
| format | Article |
| id | mit-1721.1/107436 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:18:19Z |
| publishDate | 2017 |
| publisher | Frontiers Research Foundation |
| record_format | dspace |
| spelling | mit-1721.1/1074362022-09-23T12:11:08Z Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy Yendiki, Anastasia Panneck, Patricia Srinivasan, Priti Stevens, Allison Zollei, Lilla Augustinack, Jean Wang, Ruopeng Salat, David Ehrlich, Stefan Behrens, Tim Jbabdi, Saad Gollub, Randy Fischl, Bruce Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Fischl, Bruce We have developed a method for automated probabilistic reconstruction of a set of major white-matter pathways from diffusion-weighted MR images. Our method is called TRACULA (TRActs Constrained by UnderLying Anatomy) and utilizes prior information on the anatomy of the pathways from a set of training subjects. By incorporating this prior knowledge in the reconstruction procedure, our method obviates the need for manual interaction with the tract solutions at a later stage and thus facilitates the application of tractography to large studies. In this paper we illustrate the application of the method on data from a schizophrenia study and investigate whether the inclusion of both patients and healthy subjects in the training set affects our ability to reconstruct the pathways reliably. We show that, since our method does not constrain the exact spatial location or shape of the pathways but only their trajectory relative to the surrounding anatomical structures, a set a of healthy training subjects can be used to reconstruct the pathways accurately in patients as well as in controls. National Institute for Biomedical Imaging and Bioengineering (U.S.) (Pathway to Independence Award EB008129) National Institutes of Health (U.S.). Blueprint for Neuroscience Research (U01-MH093765) National Center for Research Resources (U.S.) (P41-RR14075 and U24-RR021382) National Institute for Biomedical Imaging and Bioengineering (U.S.) (R01-EB006758) National Institute on Aging (R01-AG022381) National Center for Complementary and Alternative Medicine (U.S.) (RC1-AT005728) National Institute of Neurological Disorders and Stroke (U.S.) (R01-NS052585, R21-NS072652, and R01-NS070963) Ellison Medical Foundation. Autism & Dyslexia Project 2017-03-16T16:17:03Z 2017-03-16T16:17:03Z 2011-10 2011-03 Article http://purl.org/eprint/type/JournalArticle 1662-5196 http://hdl.handle.net/1721.1/107436 Yendiki, Anastasia. “Automated Probabilistic Reconstruction of White-Matter Pathways in Health and Disease Using an Atlas of the Underlying Anatomy.” Frontiers in Neuroinformatics 5 (2011): n. pag. © 2011 Frontiers Media en_US http://dx.doi.org/10.3389/fninf.2011.00023 Frontiers in Neuroinformatics Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/ application/pdf Frontiers Research Foundation Frontiers |
| spellingShingle | Yendiki, Anastasia Panneck, Patricia Srinivasan, Priti Stevens, Allison Zollei, Lilla Augustinack, Jean Wang, Ruopeng Salat, David Ehrlich, Stefan Behrens, Tim Jbabdi, Saad Gollub, Randy Fischl, Bruce Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy |
| title | Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy |
| title_full | Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy |
| title_fullStr | Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy |
| title_full_unstemmed | Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy |
| title_short | Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy |
| title_sort | automated probabilistic reconstruction of white matter pathways in health and disease using an atlas of the underlying anatomy |
| url | http://hdl.handle.net/1721.1/107436 |
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