Abstract
In the last two decades, numerous methods have been proposed to capture biological meaning from observed diffusion-weighted magnetic resonance imaging (DW-MRI) signals each addressing recovery of specific tissue properties (e.g. pathology based, volume fraction of tissues). Generically, specific tissue properties are recovered via a category of methods termed as tissue compartment modeling. Many recent compartmental approaches require two or more diffusivity shells. We hypothesize existence of a low dimension common representation for a wide variety of commonly used microstructural measures in common use. To test this hypothesis, we constructed 13 voxel-wise measurements from 5 distinct model-based approaches and used a multi-task deep convolutional network with a variable width bottleneck to infer these metrics from empirical DW-MRI. This is the first study to use data-driven exploration to map a common basis among DW-MRI modeling approaches. We propose to capture a compact feature space in the form of a bottleneck that preserves common features to all methods and retrieve information from single shell DW-MRI. We train on 3D patches of 40 Human Connectome Project (HCP) subjects (\(\sim \)27 million patches) where input is based on single shell DW-MRI and ground truth is estimated from all three shells of HCP data. We validate on 24 subjects and test on 25 subjects. The error reported for 5 microstructure methods on test set: 4.0%, 2.7%, 6.3%, 4.5% and 3.6%. We find that 6 features in the bottleneck layer efficiently capture an intrinsic feature space for the range of DW-MRI metrics explored.
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Acknowledgements
This work was supported by R01EB017230 (Landman). This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN. This project was supported in part by the National Center for Research Resources, Grant UL1 RR024975-01, and is now at the National Center for Advancing Translational Sciences, Grant 2 UL1 TR000445-06. This research was supported in part by the Intramural Research Program, National Institute on Aging, National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This work has been supported by Nvidia with supplement of hardware resources (GPUs) in the form of a Titan Xp. This work was supported by the U.S. Army Medical Research and Material Command and from the U.S. Department of Veterans Affairs Chronic Effects of Neurotrauma Consortium under Award No. W81XWH-13-2-0095. The U.S. Army Medical Research Acquisition Activity, and the Chronic Effects of Neurotrauma Consortium/Veterans Affairs Rehabilitation Research & Development project F1880, US Army 12342013 (W81XWH-12-2-0139), Office of Naval Research and Naval Health Research Center (W911QY-15-C-0043).
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Nath, V. et al. (2021). DW-MRI Microstructure Model of Models Captured Via Single-Shell Bottleneck Deep Learning. In: Gyori, N., Hutter, J., Nath, V., Palombo, M., Pizzolato, M., Zhang, F. (eds) Computational Diffusion MRI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-73018-5_12
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