Novel deep learning-based noise reduction technique for prostate magnetic resonance imaging.

Journal: Abdominal radiology (New York)
Published Date: Feb 12, 2021

Abstract

INTRODUCTION: Magnetic resonance imaging (MRI) has played an increasingly major role in the evaluation of patients with prostate cancer, although prostate MRI presents several technical challenges. Newer techniques, such as deep learning (DL), have been applied to medical imaging, leading to improvements in image quality. Our goal is to evaluate the performance of a new deep learning-based reconstruction method, "DLR" in improving image quality and mitigating artifacts, which is now commercially available as AIR Recon DL (GE Healthcare, Waukesha, WI). We hypothesize that applying DLR to the T2WI images of the prostate provides improved image quality and reduced artifacts.

Authors

  • Xinzeng Wang
    MR Clinical Solutions and Research Collaborations, GE Healthcare, Houston, TX, USA.
  • Jingfei Ma
    Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
  • Priya Bhosale
    Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, 1515 Holcombe Blvd., Houston, TX, USA.
  • Juan J Ibarra Rovira
    Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, 1515 Holcombe Blvd., Houston, TX, USA.
  • Aliya Qayyum
    Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, 1515 Holcombe Blvd., Houston, TX, USA.
  • Jia Sun
    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei 430079, China.
  • Ersin Bayram
    MR Clinical Solutions and Research Collaborations, GE Healthcare, Houston, TX, USA.
  • Janio Szklaruk
    Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, 1515 Holcombe Blvd., Houston, TX, USA. JSzklaru@mdanderson.org.

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