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Baete SH, Cloos MA, Lin YC, Placantonakis DG, Shepherd T, Boada FE. Fingerprinting Orientation Distribution Functions in diffusion MRI detects smaller crossing angles. Neuroimage 2019; 198:231-241. [PMID: 31102735 DOI: 10.1016/j.neuroimage.2019.05.024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2019] [Revised: 05/09/2019] [Accepted: 05/10/2019] [Indexed: 12/18/2022] Open
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.
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Affiliation(s)
- Steven H Baete
- Center for Advanced Imaging Innovation and Research (CAI(2)R), NYU School of Medicine, New York, NY, USA; Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, New York, NY, USA.
| | - Martijn A Cloos
- Center for Advanced Imaging Innovation and Research (CAI(2)R), NYU School of Medicine, New York, NY, USA; Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, New York, NY, USA; The Sackler Institute of Graduate Biomedical Sciences, NYU School of Medicine, New York, NY, USA
| | - Ying-Chia Lin
- Center for Advanced Imaging Innovation and Research (CAI(2)R), NYU School of Medicine, New York, NY, USA; Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, New York, NY, USA
| | - Dimitris G Placantonakis
- Dept. of Neurosurgery, Perlmutter Cancer Center, Neuroscience Institute, Kimmel Center for Stem Cell Biology, NYU School of Medicine, New York, NY, USA
| | - Timothy Shepherd
- Center for Advanced Imaging Innovation and Research (CAI(2)R), NYU School of Medicine, New York, NY, USA; Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, New York, NY, USA
| | - Fernando E Boada
- Center for Advanced Imaging Innovation and Research (CAI(2)R), NYU School of Medicine, New York, NY, USA; Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, New York, NY, USA
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Kim HJ, Adluru N, Collins MD, Chung MK, Bendlin BB, Johnson SC, Davidson RJ, Singh V. Multivariate General Linear Models (MGLM) on Riemannian Manifolds with Applications to Statistical Analysis of Diffusion Weighted Images. PROCEEDINGS. IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION 2014; 2014:2705-2712. [PMID: 25580070 DOI: 10.1109/cvpr.2014.352] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Linear regression is a parametric model which is ubiquitous in scientific analysis. The classical setup where the observations and responses, i.e., (xi , yi ) pairs, are Euclidean is well studied. The setting where yi is manifold valued is a topic of much interest, motivated by applications in shape analysis, topic modeling, and medical imaging. Recent work gives strategies for max-margin classifiers, principal components analysis, and dictionary learning on certain types of manifolds. For parametric regression specifically, results within the last year provide mechanisms to regress one real-valued parameter, xi ∈ R, against a manifold-valued variable, yi ∈ . We seek to substantially extend the operating range of such methods by deriving schemes for multivariate multiple linear regression -a manifold-valued dependent variable against multiple independent variables, i.e., f : Rn → . Our variational algorithm efficiently solves for multiple geodesic bases on the manifold concurrently via gradient updates. This allows us to answer questions such as: what is the relationship of the measurement at voxel y to disease when conditioned on age and gender. We show applications to statistical analysis of diffusion weighted images, which give rise to regression tasks on the manifold GL(n)/O(n) for diffusion tensor images (DTI) and the Hilbert unit sphere for orientation distribution functions (ODF) from high angular resolution acquisition. The companion open-source code is available on nitrc.org/projects/riem_mglm.
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