Deep learning-based harmonization of trabecular bone microstructures between high- and low-resolution CT imaging.

Journal: Medical physics
PMID: 38415781

Abstract

BACKGROUND: Osteoporosis is a bone disease related to increased bone loss and fracture-risk. The variability in bone strength is partially explained by bone mineral density (BMD), and the remainder is contributed by bone microstructure. Recently, clinical CT has emerged as a viable option for in vivo bone microstructural imaging. Wide variations in spatial-resolution and other imaging features among different CT scanners add inconsistency to derived bone microstructural metrics, urging the need for harmonization of image data from different scanners.

Authors

  • Indranil Guha
    Department of Electrical and Computer Engineering, College of Engineering, University of Iowa, Iowa City, Iowa, USA.
  • Syed Ahmed Nadeem
    Department of Radiology, University of Iowa, Iowa City, IA, 52242.
  • Xiaoliu Zhang
  • Paul A DiCamillo
    Department of Radiology, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA.
  • Steven M Levy
    Department of Preventive and Community Dentistry, University of Iowa, Iowa City, Iowa, USA.
  • Ge Wang
    Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, New York, USA.
  • Punam K Saha
    Departments of Radiology and Electrical and Computer Engineering, University of Iowa, Iowa City, IA, 52242.

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