Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy
Massachusetts General Hospital
Abstract
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…
Citation impact
- FWCI
- 14.76
- Percentile
- 100%
- References
- 142
Authors
1Topics & keywords
- Tractography
- White matter
- Computer science
- Artificial intelligence
- Set (abstract data type)
- Diffusion MRI
- Brain anatomy
- Probabilistic logic
Funding
- UDU.S. Department of EnergyAwards: DE-FG02-99ER62764, FG02-99ER62764, DE-FG02-, DE-FG02
- EMEllison Medical Foundation
- WTWellcome TrustAward: 1U54MH091657
- UCUniversity College London
- ECEuropean CommissionAward: 238292
- NINational Institutes of HealthAwards: 1U54MH091657, K99/R00, 1S10RR023401, EB006758, U01-MH093765, R01-NS070963, BIRN002, 1S10RR019307, P41-RR14075, 1U54MH091657-01, AG022381, 1S10RR023043, RR14075
- MRMedical Research CouncilAwards: 1U54MH091657, G0800578
- NINational Institute on AgingAwards: 1S10RR023043, 1S10RR019307, AG022381, 1U54MH091657, R01-AG022381, 1S10RR023401
- NINational Institute of Biomedical Imaging and BioengineeringAwards: 1S10RR023401, 1S10RR023043, R01-EB006758, 1U54MH091657, 1S10RR019307
- NCNational Center for Research ResourcesAwards: U24-RR021382, RR14075, P41-RR14075, BIRN002, 1S10RR019307, 1S10RR023043, 1S10RR023401
- NBNIH Blueprint for Neuroscience ResearchAwards: U01-MH093765, 1S10RR023401, 1S10RR023043, 1U54MH091657, 1S10RR019307