Can 1.25 mm thin-section images generated with Deep Learning Image Reconstruction technique replace standard-of-care 5 mm images in abdominal CT?

Journal: Abdominal radiology (New York)
Published Date: Jun 27, 2023

Abstract

BACKGROUND: CT image reconstruction has evolved from filtered back projection to hybrid- and model-based iterative reconstruction. Deep learning-based image reconstruction is a relatively new technique that uses deep convolutional neural networks to improve image quality.

Authors

  • Jinjin Cao
    Department of Radiology, Massachusetts General Hospital, White 270, 55 Fruit Street, Boston, MA, 02114, USA.
  • Nayla Mroueh
    Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA.
  • Nisanard Pisuchpen
    Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, WAC 240, Boston, MA, 02114, USA.
  • Anushri Parakh
    Radiology, Massachusetts General Hospital, White 270 | 55 Fruit Street, Boston, 02114, USA.
  • Simon Lennartz
    Faculty of Medicine and University Hospital Cologne, Institute for Diagnostic and Interventional Radiology, University of Cologne, Kerpener Str. 62, 50937, Cologne, Germany.
  • Theodore T Pierce
    Department of Radiology, Massachusetts General Hospital, White 270, 55 Fruit Street, Boston, MA, 02114, USA.
  • Avinash R Kambadakone
    Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Ste 236, Boston, MA 02114.

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