Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model.
Diffusion-weighted imaging and tractography provide a unique, non-invasive technique to study the macroscopic structure and connectivity of brain white matter in vivo. Global tractography methods aim at reconstructing the full-brain fiber configuration that best explains the measured data, based on a generative signal model. In this work, we incorporate a multi-shell multi-tissue model based on spherical convolution, into a global tractography framework, which allows to deal with partial volume effects. The required tissue response functions can be estimated from and hence calibrated to the data. The resulting track reconstruction is quantitatively related to the apparent fiber density in the data. In addition, the fiber orientation distribution for white matter and the volume fractions of gray matter and cerebrospinal fluid are produced as ancillary results. Validation results on simulated data demonstrate that this data-driven approach improves over state-of-the-art streamline and global tracking methods, particularly in the valid connection rate. Results in human brain data correspond to known white matter anatomy and show improved modeling of partial voluming. This work is an important step toward detecting and quantifying white matter changes and connectivity in healthy subjects and patients.
KU Leuven, Department of Electrical Engineering (ESAT), Processing of Speech and Images (PSI), Medical Image Computing, Leuven, Belgium; UZ Leuven, Medical Imaging Research Center, Leuven, Belgium. Electronic address: daan.christiaens@esat.kuleuven.be.
Daan
D
Christiaens
University of Freiburg Medical Center, Department of Radiology, Medical Physics, Freiburg, Germany.
Marco
M
Reisert
KU Leuven, Department of Electrical Engineering (ESAT), Processing of Speech and Images (PSI), Medical Image Computing, Leuven, Belgium; Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia; UZ Leuven, Medical Imaging Research Center, Leuven, Belgium.
Thijs
T
Dhollander
KU Leuven, Department of Imaging & Pathology, Translational MRI, Leuven, Belgium; UZ Leuven, Department of Radiology, Leuven, Belgium; UZ Leuven, Medical Imaging Research Center, Leuven, Belgium.
Stefan
S
Sunaert
KU Leuven, Department of Electrical Engineering (ESAT), Processing of Speech and Images (PSI), Medical Image Computing, Leuven, Belgium; iMinds, Medical IT Department, Leuven, Belgium; UZ Leuven, Medical Imaging Research Center, Leuven, Belgium.
Paul
P
Suetens
KU Leuven, Department of Electrical Engineering (ESAT), Processing of Speech and Images (PSI), Medical Image Computing, Leuven, Belgium; UZ Leuven, Medical Imaging Research Center, Leuven, Belgium.
Frederik
F
Maes
D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D066128:Gray Matter / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008390:Markov Chains; D009010:Monte Carlo Method; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D066127:White Matter / Q000033:anatomy & histology*
Diffusion-weighted imaging; Global tractography; Multi-shell; Multi-tissue model