期刊
MEDICAL IMAGE ANALYSIS
卷 72, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.media.2021.102129
关键词
Diffusion weighted imaging; Fiber orientation distribution; Machine learning; Deep learning; Tractography
类别
资金
- National Institute of Biomedical Imaging and Bioengineering
- National Institute of Neurological Disorders and Stroke of the National Institutes of Health (NIH) [R01EB018988, R01NS106030, R01NS079788]
- Office of the Director of the NIH [S10 OD025111]
- Technological Innovations in Neuroscience Award from the McKnight Foundation
A machine learning-based technique is proposed for accurately estimating the number and orientations of fascicles in a voxel, outperforming classical and machine learning methods in predicting crossing fascicles and leading to more accurate tractography. This method also shows better robustness to measurement down sampling and expert quality assessment of tractography results compared to other methods.
Accurate modeling of diffusion-weighted magnetic resonance imaging measurements is necessary for accurate brain connectivity analysis. Existing methods for estimating the number and orientations of fascicles in an imaging voxel either depend on non-convex optimization techniques that are sensitive to initialization and measurement noise, or are prone to predicting spurious fascicles. In this paper, we propose a machine learning-based technique that can accurately estimate the number and orientations of fascicles in a voxel. Our method can be trained with either simulated or real diffusion-weighted imaging data. Our method estimates the angle to the closest fascicle for each direction in a set of discrete directions uniformly spread on the unit sphere. This information is then processed to extract the number and orientations of fascicles in a voxel. On realistic simulated phantom data with known ground truth, our method predicts the number and orientations of crossing fascicles more accurately than several classical and machine learning methods. It also leads to more accurate tractography. On real data, our method is better than or compares favorably with other methods in terms of robustness to measurement down sampling and also in terms of expert quality assessment of tractography results. (c) 2021 Elsevier B.V. All rights reserved.
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