Abstract
Diffusion MRI (dMRI) tractography may help locate critical white matter (WM) tracts that should be preserved during neurosurgery. A key step in this process is estimating fiber orientation distribution (FOD), often done from a model such as constrained spherical deconvolution (CSD). Multi-shell (MS) multi-tissue CSD (M-CSD) provides a robust WM FOD by estimating the relative contribution to the dMRI signal from each tissue type (WM, grey matter, and cerebrospinal fluid), however, single-shell (SS) single tissue CSD (S-CSD) cannot independently estimate the signal contribution for each tissue type. S-CSD is therefore less accurate estimating FOD in voxels where multiple tissues are present. Due to that inaccuracy, tractography using S-CSD often generates more spurious WM streamlines compared to M-CSD. In this work, we present a framework to regress the M-CSD model coefficients from the S-CSD model coefficients using a convolutional neural network (CNN) in order to improve tractography. We construct a training dataset comprising acquired MS dMRI and paired synthetic SS dMRI, generated by selecting the outer shell from the MS dMRI. We select a High Resolution Network (HighResNet) as our choice of CNN to ensure subtle details of the CSD models are preserved during regression. The HighResNet is trained to perform patch-based regression from the S-CSD model coefficients and a co-registered T1-wieghted MR (T1) to the M-CSD model coefficients. We evaluate the method on patients with epilepsy who appeared structurally normal on T1. Four WM tracts related to language are extracted using a ROI-based probabilistic tractography. For comparison, M-CSD is as a pseudo ground truth. The original S-CSD generated tracts with Dice of 0.53–0.64, and the HighResNet regressed CSD models generated tracts with Dice of 0.73–0.77. We demonstrate HighResNet can regress M-CSD model coefficients from S-CSD model coefficients resulting in tracts more similar to the M-CSD generated tracts and with fewer spurious streamlines than S-CSD generated tracts.
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Acknowledgments
This research was funded/supported by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s and St. Thomas’ NHS Foundation Trust and King’s College London and/or the NIHR Clinical Research Facility. Oeslle Lucena is funded by EPSRC Research Council (EPSRC DTP EP/R513064/1). Sjoerd B. Vos is funded by the National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative BW.mn.BRC10269). We also thank NVIDIA for the Titan V GPU used in this work. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.
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Lucena, O., Vos, S.B., Vakharia, V., Duncan, J., Ourselin, S., Sparks, R. (2020). Convolutional Neural Networks for Fiber Orientation Distribution Enhancement to Improve Single-Shell Diffusion MRI Tractography. In: Bonet-Carne, E., Hutter, J., Palombo, M., Pizzolato, M., Sepehrband, F., Zhang, F. (eds) Computational Diffusion MRI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-52893-5_9
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