Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps

Abstract

Diffusion magnetic resonance images may suffer from geometric distortions due to susceptibility induced off resonance fields, which cause geometric mismatch with anatomical images and ultimately affect subsequent quantification of microstructural or connectivity indices. State-of-the art diffusion distortion correction methods typically require data acquired with reverse phase encoding directions, resulting in varying magnitudes and orientations of distortion, which allow estimation of an undistorted volume. Alternatively, additional field maps acquisitions can be used along with sequence information to determine warping fields. However, not all imaging protocols include these additional scans and cannot take advantage of state-of-the art distortion correction. To avoid additional acquisitions, structural MRI (undistorted scans) can be used as registration targets for intensity driven correction. In this study, we aim to (1) enable susceptibility distortion correction with historical and/or limited diffusion datasets that do not include specific sequences for distortion correction and (2) avoid the computationally intensive registration procedure typically required for distortion correction using structural scans. To achieve these aims, we use deep learning (3D Unets) to synthesize an undistorted b0 image that matches geometry of structural T1w images and intensity contrasts from diffusion images. Importantly, the training dataset is heterogenous, consisting of varying acquisitions of both structural and diffusion. We apply our approach to a withheld test set and show that distortions are successfully corrected after processing. We quantitatively evaluate the proposed distortion correction and intensity-based registration against state-of-the-art distortion correction (FSL topup). The results illustrate that the proposed pipeline results in b0 images that are geometrically similar to non-distorted structural images, and more closely match state-of-the-art correction with additional acquisitions. In addition, we show generalizability of the proposed approach to datasets that were not in the original training / validation / testing datasets. These datasets included varying populations, contrasts, resolutions, and magnitudes and orientations of distortion and show efficacious distortion correction. The method is available as a Singularity container, source code, and an executable trained model to facilitate evaluation.

MASiVar: Multisite, multiscanner, and multisubject acquisitions for studying variability in diffusion weighted MRI

“…Reverse phase‐encoded (b = 0 s/mm 2 volumes were also acquired for all scans in all cohorts except for those from 1 subject in cohort II at site 3. Most sessions also included a T 1 ‐weighted image for structural analysis or distortion correction 22 . All images were de‐identified and all scans were acquired only after informed consent under supervision of the project institutional review board.…”

“…23 In brief, all acquisitions per scan were denoised with the Marchenko-Pastur technique, [24][25][26] intensity normalized, and distortion corrected. Distortion correction included susceptibilityinduced distortion correction 27 using reverse phase-encoded b = 0 s/mm 2 volumes when available and the Synb0-DisCo deep learning framework 22 and associated T 1 image when not, eddy current-induced distortion correction, intervolume motion correction, and slice-wise signal dropout imputation. 28,29 The estimated volume-to-volume displacement corrected during preprocessing, and SNRs of the scans are reported in Supporting Information Figure S1.…”

“…Most sessions also included a T 1 -weighted image for structural analysis or distortion correction. 22 All images were de-identified and all scans were acquired only after informed consent under supervision of the project institutional review board.…”

Immediate neural network impact after the loss of a semantic hub

“…Susceptibility distortions on preoperative diffusion-weighted MRI data were corrected using the SynB0 DisCo tool in conjunction with the FSL TOPUP tool 73,74 . The FSL tool 'EDDY' was used to correct for motion and eddy current distortions with the bvecs rotated appropriately 75 .…”

MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging

“…In brief, all acquisitions per scan were denoised with the Marchenko-Pastur technique (Cordero-Grande et al, 2019;Veraart et al, 2016bVeraart et al, , 2016a, intensity normalized such that the average b = 0 s/mm 2 intensity distributions within the brain maximally intersected, and distortion corrected. Distortion correction included susceptibility-induced distortion correction (Andersson et al, 2003) using APA b = 0 s/mm 2 volumes when available and the Synb0-DisCo deep learning framework (Schilling et al, 2020a) when not, eddy current-induced distortion correction, intervolume motion correction, and slice-wise signal drop out imputation .…”