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

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 d...

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Published in:PloS one Vol. 15; no. 7; p. e0236418
Main Authors: Schilling, Kurt G., Blaber, Justin, Hansen, Colin, Cai, Leon, Rogers, Baxter, Anderson, Adam W., Smith, Seth, Kanakaraj, Praitayini, Rex, Tonia, Resnick, Susan M., Shafer, Andrea T., Cutting, Laurie E., Woodward, Neil, Zald, David, Landman, Bennett A.
Format: Journal Article
Language:English
Published: United States Public Library of Science 31.07.2020
Public Library of Science (PLoS)
Subjects:
Aging
Algorithms
Biology and Life Sciences
Brain - diagnostic imaging
Computer and Information Sciences
Computer engineering
Computer science
Data acquisition
Datasets
Deep learning
Diffusion
Diffusion Magnetic Resonance Imaging - methods
Distortion
Echo-Planar Imaging - methods
Education
Electrical engineering
Engineering and Technology
Gene mapping
Humans
Image processing
Image Processing, Computer-Assisted - methods
Laboratories
Machine learning
Magnetic permeability
Magnetic resonance
Magnetic resonance imaging
Medicine and Health Sciences
Methods
Neurosciences
Radiology
Registration
Research and Analysis Methods
Resonance
Source code
Training
United States
United States > US
Nashville Tennessee
Baltimore Maryland
ISSN:1932-6203, 1932-6203
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Summary: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 U-nets) 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.
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Competing Interests: This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN. This work was supported by the National Institutes of Health under award numbers R01EB017230, and T32EB001628, and in part by ViSE/VICTR VR3029 and the National Center for Research Resources, Grant UL1 RR024975-01, and Department of Defense award number W81XWH-17-2-055. This research was conducted with the support from Intramural Research Program, National Institute on Aging, NIH. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research, and this does not alter our adherence to PLOS ONE policies on sharing data and materials.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0236418