Upstream Machine Learning in Radiology.

Journal: Radiologic clinics of North America
Published Date: Nov 1, 2021

Abstract

Machine learning (ML) and Artificial intelligence (AI) has the potential to dramatically improve radiology practice at multiple stages of the imaging pipeline. Most of the attention has been garnered by applications focused on improving the end of the pipeline: image interpretation. However, this article reviews how AI/ML can be applied to improve upstream components of the imaging pipeline, including exam modality selection, hardware design, exam protocol selection, data acquisition, image reconstruction, and image processing. A breadth of applications and their potential for impact is shown across multiple imaging modalities, including ultrasound, computed tomography, and MRI.

Authors

  • Christopher M Sandino
    Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA.
  • Elizabeth K Cole
    Department of Radiology, Stanford University, Stanford, California, USA.
  • Cagan Alkan
    Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA 94305, USA.
  • Akshay S Chaudhari
    Department of Radiology, Stanford University, Stanford, California.
  • Andreas M Loening
    Department of Radiology, School of Medicine, Stanford University, Stanford, California, USA.
  • Dongwoon Hyun
  • Jeremy Dahl
    Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA.
  • Abdullah-Al-Zubaer Imran
    Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA.
  • Adam S Wang
    Department of Radiology, Stanford University, Stanford, California, USA.
  • Shreyas S Vasanawala

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