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Image-based motion artifact reduction on liver dynamic contrast enhanced MRI

  • Yunan Wu
    Affiliations
    Department of Electrical Computer Engineering, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA

    Department of Diagnostic Radiology, Rush University Medical Center, 1653 W. Congress Pkwy, Jelke Ste 181, Chicago, IL 60612, USA
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  • Junchi Liu
    Affiliations
    Medical Imaging Research Center and Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
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  • Gregory M. White
    Affiliations
    Department of Diagnostic Radiology, Rush University Medical Center, 1653 W. Congress Pkwy, Jelke Ste 181, Chicago, IL 60612, USA
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  • Jie Deng
    Correspondence
    Corresponding author at: Department of Radiation Oncology, UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA.
    Affiliations
    Department of Diagnostic Radiology, Rush University Medical Center, 1653 W. Congress Pkwy, Jelke Ste 181, Chicago, IL 60612, USA

    Department of Radiation Oncology, UT Southwestern Medical Center, 2280 Inwood Rd, Dallas, TX 75235, USA
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Published:December 22, 2022DOI:https://doi.org/10.1016/j.ejmp.2022.12.001

      Highlights

      • The Generative Adversarial Network to reduce motion artifacts on liver MRI.
      • Training with the perceptual loss to preserve anatomical structure details.
      • Multiple types and degrees of motion artifacts simulation in image and k-space.
      • Good generalization of the model evaluated on liver MRI images with real motion.

      Abstract

      Liver MRI images often suffer from degraded quality due to ghosting or blurring artifacts caused by patient respiratory or bulk motion. In this study, we developed a two-stage deep learning model to reduce motion artifact on dynamic contrast enhanced (DCE) liver MRIs. The stage-I network utilized a deep residual network with a densely connected multi-resolution block (DRN-DCMB) network to remove most motion artifacts. The stage-II network applied the generative adversarial network (GAN) and perceptual loss compensation to preserve image structural features. The stage-I network served as the generator of GAN and its pretrained parameters in stage-I were further updated via backpropagation during stage-II training. The stage-I network was trained using small image patches with simulated motion artifacts including image-space rotational and translational motion, and K-space based centric and interleaved linear motion, sinusoidal, and rotational motion to mimic liver motion patterns. The stage-II network training used full-size images with the same types of simulated motion. The liver DCE-MRI image volumes without obvious motion artifacts in 10 patients were used for the training process, of which 1020 images of 8 patients were used for training and 240 images of 2 patients for validation. Finally, the whole two-stage deep learning model was tested with simulated motion images (312 clean images from 5 test patients) and patient images with real motion artifacts (28 motion images from 12 patients). The resulted images after two-stage processing demonstrated reduced motion artifacts while preserved anatomic details without image blurriness, with SSIM of 0.935 ± 0.092, MSE of 60.7 ± 9.0 × 10-3, and PSNR of 32.054 ± 2.219.

      Abbreviations:

      DCE-MRI (dynamic contrast enhanced magnetic resonance imaging), DRN-DCMB (deep residual network with densely connected multi-resolution block), CS (compressed sensing), CNNs (convolutional neural networks), DL (deep learning), 3D (three-dimensional), FFT (Fast Fourier Transform), BN (batch normalization), LeakyReLU (Leaky rectified linear unit), MSE (mean square error), SSIM (structural similarity index), TR (repetition time), TE (echo time), FA (flip angle), MARC (motion artifact reduction with convolution), GAN (generative adversarial network)

      Keywords

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