3D full-dose brain-PET volume recovery from low-dose data through deep learning: quantitative assessment and clinical evaluation.

Journal: European radiology
PMID: 39609283

Abstract

OBJECTIVES: Low-dose (LD) PET imaging would lead to reduced image quality and diagnostic efficacy. We propose a deep learning (DL) method to reduce radiotracer dosage for PET studies while maintaining diagnostic quality.

Authors

  • Rui Guo
    College of Chemistry&Chemical Engineering, Xiamen University, Xiamen 361005, China.
  • Jiale Wang
    School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China.
  • Ying Miao
    College of Biological Science and Engineering, Fuzhou University, Fuzhou, 350108, China.
  • Xinyu Zhang
    Wenzhou Medical University Renji College, Wenzhou, Zhejiang, 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.
  • 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.

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