Deep Learning Automated Background Phase Error Correction for Abdominopelvic 4D Flow MRI.

Journal: Radiology
Published Date: Nov 30, 2021

Abstract

Background Four-dimensional (4D) flow MRI has the potential to provide hemodynamic insights for a variety of abdominopelvic vascular diseases, but its clinical utility is currently impaired by background phase error, which can be challenging to correct. Purpose To assess the feasibility of using deep learning to automatically perform image-based background phase error correction in 4D flow MRI and to compare its effectiveness relative to manual image-based correction. Materials and Methods A convenience sample of 139 abdominopelvic 4D flow MRI acquisitions performed between January 2016 and July 2020 was retrospectively collected. Manual phase error correction was performed using dedicated imaging software and served as the reference standard. After reserving 40 examinations for testing, the remaining examinations were randomly divided into training (86% [85 of 99]) and validation (14% [14 of 99]) data sets to train a multichannel three-dimensional U-Net convolutional neural network. Flow measurements were obtained for the infrarenal aorta, common iliac arteries, common iliac veins, and inferior vena cava. Statistical analyses included Pearson correlation, Bland-Altman analysis, and tests with Bonferroni correction. Results A total of 139 patients (mean age, 47 years ± 14 [standard deviation]; 108 women) were included. Inflow-outflow correlation improved after manual correction ( = 0.94, < .001) compared with that before correction ( = 0.50, < .001). Automated correction showed similar results ( = 0.91, < .001) and demonstrated very strong correlation with manual correction ( = 0.98, < .001). Both correction methods reduced inflow-outflow variance, improving mean difference from -0.14 L/min (95% limits of agreement: -1.61, 1.32) (uncorrected) to 0.05 L/min (95% limits of agreement: -0.32, 0.42) (manually corrected) and 0.05 L/min (95% limits of agreement: -0.38, 0.49) (automatically corrected). There was no significant difference in inflow-outflow variance between manual and automated correction methods ( = .10). Conclusion Deep learning automated phase error correction reduced inflow-outflow bias and variance of volumetric flow measurements in four-dimensional flow MRI, achieving results comparable with manual image-based phase error correction. © RSNA, 2021 See also the editorial by Roldán-Alzate and Grist in this issue.

Authors

  • Sophie You
    From the School of Medicine (S.Y., E.M.M.), Department of Cardiovascular Medicine (P.R.T.), and Department of Radiology (J.L., A.C.R., A.H.), University of California, San Diego, 9300 Campus Point Dr, La Jolla, CA 92037-0841; and Department of Radiology, Stanford University School of Medicine, Stanford, Calif (M.T.A., S.S.V.).
  • Evan M Masutani
    From the Departments of Bioengineering (E.M.M.) and Radiology (A.H.), University of California, San Diego, 9300 Campus Point Dr, MC 0841, San Diego, CA 92037-0841; and GE Healthcare, Menlo Park, Calif (N.B.).
  • Marcus T Alley
    From the School of Medicine (S.Y., E.M.M.), Department of Cardiovascular Medicine (P.R.T.), and Department of Radiology (J.L., A.C.R., A.H.), University of California, San Diego, 9300 Campus Point Dr, La Jolla, CA 92037-0841; and Department of Radiology, Stanford University School of Medicine, Stanford, Calif (M.T.A., S.S.V.).
  • Shreyas S Vasanawala
  • Pam R Taub
    Division of Cardiovascular Medicine, University of California, San Diego, CA 92093, USA.
  • Joy Liau
    From the School of Medicine (S.Y., E.M.M.), Department of Cardiovascular Medicine (P.R.T.), and Department of Radiology (J.L., A.C.R., A.H.), University of California, San Diego, 9300 Campus Point Dr, La Jolla, CA 92037-0841; and Department of Radiology, Stanford University School of Medicine, Stanford, Calif (M.T.A., S.S.V.).
  • Anne C Roberts
    From the School of Medicine (S.Y., E.M.M.), Department of Cardiovascular Medicine (P.R.T.), and Department of Radiology (J.L., A.C.R., A.H.), University of California, San Diego, 9300 Campus Point Dr, La Jolla, CA 92037-0841; and Department of Radiology, Stanford University School of Medicine, Stanford, Calif (M.T.A., S.S.V.).
  • Albert Hsiao
    Department of Radiology, University of California, San Diego, 9452 Medical Center Dr, 4th Floor, La Jolla, CA 92037 (T.A.R., S.J.K., K.E.J., A.C.Y., S.S.B., L.D.H., A.H.); and Department of Mathematics and Statistics, San Diego State University, San Diego, Calif (K.A.H.).

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