Deep learning-based CT-free attenuation correction for cardiac SPECT: a new approach.

Journal: BMC medical imaging
PMID: 39905320

Abstract

BACKGROUND: Computed tomography attenuation correction (CTAC) is commonly used in cardiac SPECT imaging to reduce soft-tissue attenuation artifacts. However, CTAC is prone to inaccuracies due to CT artifacts and SPECT-CT mismatch, along with additional radiation exposure to patients. Thus, these limitations have led to increasing interest in CT-free AC, with deep learning (DL) offering promising solutions. We proposed a new DL-based CT-free AC methods for cardiac SPECT.

Authors

  • Pei Yang
    Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Zeao Zhang
    Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu, 610065, China.
  • Jianan Wei
    Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, People's Republic of China.
  • Lisha Jiang
    Laboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital of Sichuan University, No. 37 Guo Xue Alley, Chengdu, 610041, People's Republic of China.
  • Liqian Yu
    Department of Nuclear Medicine, West China Hospital of Sichuan University, No.37 Guo Xue Alley, Chengdu, 610041, China.
  • Huawei Cai
    Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610041, PR China. Electronic address: hw.cai@yahoo.com.
  • Lin Li
    Department of Medicine III, LMU University Hospital, LMU Munich, Munich, Germany.
  • Quan Guo
  • Zhen Zhao

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