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Fingerprinting Orientation Distribution Functions in diffusion MRI detects smaller crossing angles.
- Source :
-
NeuroImage [Neuroimage] 2019 Sep; Vol. 198, pp. 231-241. Date of Electronic Publication: 2019 May 16. - Publication Year :
- 2019
-
Abstract
- Diffusion tractography is routinely used to study white matter architecture and brain connectivity in vivo. A key step for successful tractography of neuronal tracts is the correct identification of tract directions in each voxel. Here we propose a fingerprinting-based methodology to identify these fiber directions in Orientation Distribution Functions, dubbed ODF-Fingerprinting (ODF-FP). In ODF-FP, fiber configurations are selected based on the similarity between measured ODFs and elements in a pre-computed library. In noisy ODFs, the library matching algorithm penalizes the more complex fiber configurations. ODF simulations and analysis of bootstrapped partial and whole-brain in vivo datasets show that the ODF-FP approach improves the detection of fiber pairs with small crossing angles while maintaining fiber direction precision, which leads to better tractography results. Rather than focusing on the ODF maxima, the ODF-FP approach uses the whole ODF shape to infer fiber directions to improve the detection of fiber bundles with small crossing angle. The resulting fiber directions aid tractography algorithms in accurately displaying neuronal tracts and calculating brain connectivity.<br /> (Copyright © 2019 Elsevier Inc. All rights reserved.)
- Subjects :
- Algorithms
Brain anatomy & histology
Computer Simulation
Humans
Neural Pathways anatomy & histology
Neural Pathways diagnostic imaging
Reproducibility of Results
Signal-To-Noise Ratio
White Matter anatomy & histology
Brain diagnostic imaging
Brain Mapping methods
Diffusion Magnetic Resonance Imaging
Image Processing, Computer-Assisted methods
White Matter diagnostic imaging
Subjects
Details
- Language :
- English
- ISSN :
- 1095-9572
- Volume :
- 198
- Database :
- MEDLINE
- Journal :
- NeuroImage
- Publication Type :
- Academic Journal
- Accession number :
- 31102735
- Full Text :
- https://doi.org/10.1016/j.neuroimage.2019.05.024