Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI.

Autor: Nath V; Computer Science, Vanderbilt University, Nashville, TN, USA. Electronic address: vishwesh.nath@vanderbilt.edu., Schilling KG; Biomedical Engineering, Vanderbilt University, Nashville, TN, USA., Parvathaneni P; Electrical Engineering, Vanderbilt University, Nashville, TN, USA., Hansen CB; Computer Science, Vanderbilt University, Nashville, TN, USA., Hainline AE; Biostatistics, Vanderbilt University, Nashville, TN, USA., Huo Y; Biomedical Engineering, Vanderbilt University, Nashville, TN, USA., Blaber JA; Electrical Engineering, Vanderbilt University, Nashville, TN, USA., Lyu I; Computer Science, Vanderbilt University, Nashville, TN, USA., Janve V; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA., Gao Y; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA., Stepniewska I; Psychology, Vanderbilt University, Nashville, TN, USA., Anderson AW; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA., Landman BA; Computer Science, Vanderbilt University, Nashville, TN, USA; Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Electrical Engineering, Vanderbilt University, Nashville, TN, USA.
Jazyk: angličtina
Zdroj: Magnetic resonance imaging [Magn Reson Imaging] 2019 Oct; Vol. 62, pp. 220-227. Date of Electronic Publication: 2019 Jul 16.
DOI: 10.1016/j.mri.2019.07.012
Abstrakt: Purpose: Diffusion-weighted magnetic resonance imaging (DW-MRI) is of critical importance for characterizing in-vivo white matter. Models relating microarchitecture to observed DW-MRI signals as a function of diffusion sensitization are the lens through which DW-MRI data are interpreted. Numerous modern approaches offer opportunities to assess more complex intra-voxel structures. Nevertheless, there remains a substantial gap between intra-voxel estimated structures and ground truth captured by 3-D histology.
Methods: Herein, we propose a novel data-driven approach to model the non-linear mapping between observed DW-MRI signals and ground truth structures using a sequential deep neural network regression using residual block deep neural network (ResDNN). Training was performed on two 3-D histology datasets of squirrel monkey brains and validated on a third. A second validation was performed using scan-rescan datasets of 12 subjects from Human Connectome Project. The ResDNN was compared with multiple micro-structure reconstruction methods and super resolved-constrained spherical deconvolution (sCSD) in particular as baseline for both the validations.
Results: Angular correlation coefficient (ACC) is a correlation/similarity measure and can be interpreted as accuracy when compared with a ground truth. The median ACC of ResDNN is 0.82 and median ACC's of different variants of CSD are 0.75, 0.77, 0.79. The mean, median and std. of ResDNN & sCSD ACC across 12 subjects from HCP are 0.74, 0.88, 0.31 and 0.61, 0.71, 0.31 respectively.
Conclusion: This work highlights the ability of deep learning to capture linkages between ex-vivo ground truth data with feasible MRI sequences. The data-driven approach is applicable to human in-vivo data and results in intriguingly high reproducibility of orientation structure.
(Copyright © 2019 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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