Deep Learning Estimation of Multi-Tissue Constrained Spherical Deconvolution with Limited Single Shell DW-MRI

• URL: http://arxiv.org/abs/2002.08820v1
• Date: Thu, 20 Feb 2020 15:59:03 GMT
• Title: Deep Learning Estimation of Multi-Tissue Constrained Spherical Deconvolution with Limited Single Shell DW-MRI
• Authors: Vishwesh Nath, Sudhir K. Pathak, Kurt G. Schilling, Walt Schneider, Bennett A. Landman
• Abstract summary: Deep learning can be used to estimate the information content captured by 8th order constrained spherical deconvolution (CSD) We examine two network architectures: Sequential network of fully connected dense layers with a residual block in the middle (ResDNN), and Patch based convolutional neural network with a residual block (ResCNN) The fiber orientation distribution function (fODF) can be recovered with high correlation as compared to the ground truth of MT-CST, which was derived from the multi-shell DW-MRI acquisitions.
• Score: 2.903217519429591
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion-weighted magnetic resonance imaging (DW-MRI) is the only non-invasive approach for estimation of intra-voxel tissue microarchitecture and reconstruction of in vivo neural pathways for the human brain. With improvement in accelerated MRI acquisition technologies, DW-MRI protocols that make use of multiple levels of diffusion sensitization have gained popularity. A well-known advanced method for reconstruction of white matter microstructure that uses multi-shell data is multi-tissue constrained spherical deconvolution (MT-CSD). MT-CSD substantially improves the resolution of intra-voxel structure over the traditional single shell version, constrained spherical deconvolution (CSD). Herein, we explore the possibility of using deep learning on single shell data (using the b=1000 s/mm2 from the Human Connectome Project (HCP)) to estimate the information content captured by 8th order MT-CSD using the full three shell data (b=1000, 2000, and 3000 s/mm2 from HCP). Briefly, we examine two network architectures: 1.) Sequential network of fully connected dense layers with a residual block in the middle (ResDNN), 2.) Patch based convolutional neural network with a residual block (ResCNN). For both networks an additional output block for estimation of voxel fraction was used with a modified loss function. Each approach was compared against the baseline of using MT-CSD on all data on 15 subjects from the HCP divided into 5 training, 2 validation, and 8 testing subjects with a total of 6.7 million voxels. The fiber orientation distribution function (fODF) can be recovered with high correlation (0.77 vs 0.74 and 0.65) as compared to the ground truth of MT-CST, which was derived from the multi-shell DW-MRI acquisitions. Source code and models have been made publicly available.

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