Data-efficient generalization of AI transformers for noise reduction in ultra-fast lung PET scans.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: Feb 26, 2025

Abstract

PURPOSE: Respiratory motion during PET acquisition may produce lesion blurring. Ultra-fast 20-second breath-hold (U2BH) PET reduces respiratory motion artifacts, but the shortened scanning time increases statistical noise and may affect diagnostic quality. This study aims to denoise the U2BH PET images using a deep learning (DL)-based method.

Authors

  • Jiale Wang
    School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China.
  • Xinyu Zhang
    Wenzhou Medical University Renji College, Wenzhou, Zhejiang, China.
  • Ying Miao
    College of Biological Science and Engineering, Fuzhou University, Fuzhou, 350108, China.
  • Song Xue
  • Yu Zhang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Kuangyu Shi
    Universitätsklinik für Nuklearmedizin, Inselspital University Hospital Bern, University of Bern, Bern, Switzerland.
  • Rui Guo
    College of Chemistry&Chemical Engineering, Xiamen University, Xiamen 361005, China.
  • Biao Li
    Key Laboratory of Renewable Energy, Guangdong Key Laboratory of New and Renewable Energy Research and Development, Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China.
  • Guoyan Zheng
    Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland. guoyan.zheng@istb.unibe.ch.

Keywords

External Resources

Popular Topics
Recent Journals