Unsupervised motion artifact correction of turbo spin-echo MRI using deep image prior.

Journal: Magnetic resonance in medicine
Published Date: Jan 28, 2024

Abstract

PURPOSE: In MRI, motion artifacts can significantly degrade image quality. Motion artifact correction methods using deep neural networks usually required extensive training on large datasets, making them time-consuming and resource-intensive. In this paper, an unsupervised deep learning-based motion artifact correction method for turbo-spin echo MRI is proposed using the deep image prior framework.

Authors

  • Jongyeon Lee
    School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
  • Hyunseok Seo
    Medical Physics Division in the Department of Radiation Oncology, School of Medicine, Stanford University, Stanford, CA, 94305-5847, USA.
  • Wonil Lee
    Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
  • HyunWook Park
    Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.

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