Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment.

Journal: European journal of nuclear medicine and molecular imaging
PMID: 39136740

Abstract

PURPOSE: Respiratory motion (RM) significantly impacts image quality in thoracoabdominal PET/CT imaging. This study introduces a unified data-driven respiratory motion correction (uRMC) method, utilizing deep learning neural networks, to solve all the major issues caused by RM, i.e., PET resolution loss, attenuation correction artifacts, and PET-CT misalignment.

Authors

  • Yihuan Lu
  • Fei Kang
    Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an, 710032, China.
  • Duo Zhang
    Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China.
  • Yue Li
    School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, China.
  • Hao Liu
    Key Laboratory of Development and Maternal and Child Diseases of Sichuan Province, Department of Pediatrics, Sichuan University, Chengdu, China.
  • Chen Sun
    State Key Laboratory of Characteristic Chinese Medicine Resources in Southwest China, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
  • Hao Zeng
    European Laboratory for Non Linear Spectroscopy (LENS), University of Florence, 50019 Sesto Fiorentino, Italy.
  • Lei Shi
  • Yumo Zhao
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.

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