Clinical Pilot of a Deep Learning Elastic Registration Algorithm to Improve Misregistration Artifact and Image Quality on Routine Oncologic PET/CT.

Journal: Academic radiology
PMID: 39462735

Abstract

RATIONALE AND OBJECTIVES: Misregistration artifacts between the PET and attenuation correction CT (CTAC) exams can degrade image quality and cause diagnostic errors. Deep learning (DL)-warped elastic registration methods have been proposed to improve misregistration errors.

Authors

  • Jordan H Chamberlin
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive Room 2221 ART, Charleston, SC 29425.
  • Joshua Schaefferkoetter
    Siemens Medical Solutions USA, Inc., 810 Innovation Drive, Knoxville, TN, 37932, USA. joshua.schaefferkoetter@siemens.com.
  • James Hamill
    Siemens Medical Solutions USA, Inc., 810 Innovation Drive, Knoxville, Tennessee 37932, USA.
  • Ismail M Kabakus
    Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC.
  • Kevin P Horn
    Department of Radiology, Medical University of South Carolina, Charleston, South Carolina, USA.
  • Jim O'Doherty
    Siemens Healthineers, Princeton, New Jersey.
  • Saeed Elojeimy
    Division of Nuclear Medicine, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America. Electronic address: elojeim@musc.edu.

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