Harmonizing 1.5 T/3T diffusion weighted MRI through development of deep learning stabilized microarchitecture estimators

Posted by Vishwesh Nath on Friday, January 17, 2020 in Deep Learning, Diffusion Weighted MRI, Harmonization, Longitudinal, Magnetic resonance imaging, Neuroimaging, Registration, Reproducibility.

Nath V, Remedios S, Parvathaneni P, Hansen CB, Bayrak RG, Bermudez C, Blaber JA, Schilling KG, Janve VA, Gao Y, Huo Y. Harmonizing 1.5 T/3T diffusion weighted MRI through development of deep learning stabilized microarchitecture estimators. In Medical Imaging 2019: Image Processing 2019 Mar 15 (Vol. 10949, p. 109490O). International Society for Optics and Photonics.

Abstract

Diffusion weighted magnetic resonance imaging (DW-MRI) is interpreted as a quantitative method that is sensitive to
tissue microarchitecture at a millimeter scale. However, the sensitization is dependent on acquisition sequences (e.g.,
diffusion time, gradient strength, etc.) and susceptible to imaging artifacts. Hence, comparison of quantitative DW-MRI
biomarkers across field strengths (including different scanners, hardware performance, and sequence design
considerations) is a challenging area of research. We propose a novel method to estimate microstructure using DW-MRI
that is robust to scanner difference between 1.5T and 3T imaging. We propose to use a null space deep network (NSDN)
architecture to model DW-MRI signal as fiber orientation distributions (FOD) to represent tissue microstructure. The
NSDN approach is consistent with histologically observed microstructure (on previously acquired ex vivo squirrel monkey
dataset) and scan-rescan data. The contribution of this work is that we incorporate identical dual networks (IDN) to
minimize the influence of scanner effects via scan-rescan data. Briefly, our estimator is trained on two datasets. First, a
histology dataset was acquired on three squirrel monkeys with corresponding DW-MRI and confocal histology (512
independent voxels). Second, 37 control subjects from the Baltimore Longitudinal Study of Aging (67-95 y/o) were
identified who had been scanned at 1.5T and 3T scanners (b-value of 700 s/mm2, voxel resolution at 2.2mm, 30-32 gradient
volumes) with an average interval of 4 years (standard deviation 1.3 years). After, image registration, we used paired white
matter (WM) voxels for 17 subjects and 440 histology voxels for training and 20 subjects and 72 histology voxels for
testing. We compare the proposed estimator with super-resolved constrained spherical deconvolution (CSD) and a
previously presented regression deep neural network (DNN). NSDN outperformed CSD and DNN in angular correlation
coefficient (ACC) 0.81 versus 0.28 and 0.46, mean squared error (MSE) 0.001 versus 0.003 and 0.03, and general fractional
anisotropy (GFA) 0.05 versus 0.05 and 0.09. Further validation and evaluation with contemporaneous imaging are
necessary, but the NSDN is promising avenue for building understanding of microarchitecture in a consistent and device-independent manner.