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

• Published in: Proceedings of SPIE, the international society for optical engineering Vol. 11313
• Main Authors: Nath, Vishwesh, Pathak, Sudhir K, Schilling, Kurt G, Schneider, Walt, Landman, Bennett A
• Format: Journal Article
• Language: English
• Published: United States 01.02.2020
• Subjects: Deep learning; DW-MRI; Spherical Harmonics; MT-CSD; Tractography
• ISSN: 0277-786X; 1996-756X
• DOI: 10.1117/12.2549455

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/mm from the Human Connectome Project (HCP)) to estimate the information content captured by 8 order MT-CSD using the full three shell data (b=1000, 2000, and 3000 s/mm 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) and low root mean squared error ResCNN:0.0124, ResDNN:0.0168 and sCSD:0.0323 as compared to the ground truth of MT-CST, which was derived from the multi-shell DW-MRI acquisitions. The mean squared error between the MT-CSD estimates for white matter tissue fraction and for the predictions are ResCNN:0.0249 vs ResDNN:0.0264. We illustrate the applicability of high definition fiber tractography on a single testing subject with arcuate and corpus callosum Tractography. In summary, the proposed approach provides a promising framework to estimate MT-CSD with limited single shell data. Source code and models have been made publicly available.