Michael Ankele1, Lek-Heng Lim2, Samuel Groeschel3, Thomas Schultz4. 1. Institute of Computer Science II, University of Bonn, Friedrich-Ebert-Allee 144, 53113, Bonn, Germany. 2. Department of Statistics, University of Chicago, 5747 S Ellis Ave, Chicago, IL, 60637, USA. 3. Department of Pediatric Neurology and Developmental Medicine and Experimental Pediatric Neuroimaging, University Children's Hospital Tübingen, Tübingen, Germany. 4. Institute of Computer Science II, University of Bonn, Friedrich-Ebert-Allee 144, 53113, Bonn, Germany. schultz@cs.uni-bonn.de.
Abstract
PURPOSE: Develop a multi-fiber tractography method that produces fast and robust results based on input data from a wide range of diffusion MRI protocols, including high angular resolution diffusion imaging, multi-shell imaging, and clinical diffusion spectrum imaging (DSI) METHODS: In a unified deconvolution framework for different types of diffusion MRI protocols, we represent fiber orientation distribution functions as higher-order tensors, which permits use of a novel positive definiteness constraint (H-psd) that makes estimation from noisy input more robust. The resulting directions are used for deterministic fiber tracking with branching. RESULTS: We quantify accuracy on simulated data, as well as condition numbers and computation times on clinical data. We qualitatively investigate the benefits when processing suboptimal data, and show direct comparisons to several state-of-the-art techniques. CONCLUSION: The proposed method works faster than state-of-the-art approaches, achieves higher angular resolution on simulated data with known ground truth, and plausible results on clinical data. In addition to working with the same data as previous methods for multi-tissue deconvolution, it also supports DSI data.
PURPOSE: Develop a multi-fiber tractography method that produces fast and robust results based on input data from a wide range of diffusion MRI protocols, including high angular resolution diffusion imaging, multi-shell imaging, and clinical diffusion spectrum imaging (DSI) METHODS: In a unified deconvolution framework for different types of diffusion MRI protocols, we represent fiber orientation distribution functions as higher-order tensors, which permits use of a novel positive definiteness constraint (H-psd) that makes estimation from noisy input more robust. The resulting directions are used for deterministic fiber tracking with branching. RESULTS: We quantify accuracy on simulated data, as well as condition numbers and computation times on clinical data. We qualitatively investigate the benefits when processing suboptimal data, and show direct comparisons to several state-of-the-art techniques. CONCLUSION: The proposed method works faster than state-of-the-art approaches, achieves higher angular resolution on simulated data with known ground truth, and plausible results on clinical data. In addition to working with the same data as previous methods for multi-tissue deconvolution, it also supports DSI data.
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