Deep learning enhanced ultra-fast SPECT/CT bone scan in patients with suspected malignancy: quantitative assessment and clinical performance.

Journal: Physics in medicine and biology
Published Date:

Abstract

. To evaluate the clinical performance of deep learning-enhanced ultrafast single photon emission computed tomography/computed tomography (SPECT/CT) bone scans in patients with suspected malignancy.. In this prospective study, 102 patients with potential malignancy were enrolled and underwent a 20 min SPECT/CT and a 3 min SPECT scan. A deep learning model was applied to generate algorithm-enhanced images (3 min DL SPECT). The reference modality was the 20 min SPECT/CT scan. Two reviewers independently evaluated general image quality, Tc-99m MDP distribution, artifacts, and diagnostic confidence of 20 min SPECT/CT, 3 min SPECT/CT, and 3 min DL SPECT/CT images. The sensitivity, specificity, accuracy, and interobserver agreement were calculated. The lesion maximum standard uptake value (SUV) of the 3 min DL and 20 min SPECT/CT images was analyzed. The peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM) were evaluated.. The 3 min DL SPECT/CT images showed significantly superior general image quality, Tc-99m MDP distribution, artifacts, and diagnostic confidence than the 20 min SPECT/CT images (< 0.0001). The diagnostic performance of the 20 min and 3 min DL SPECT/CT images was similar for reviewer 1 (paired= 0.333,= 0.564) and reviewer 2 (paired= 0.05,= 0.823). The diagnosis results for the 20 min (kappa = 0.822) and 3 min DL (kappa = 0.732) SPECT/CT images showed high interobserver agreement. The 3 min DL SPECT/CT images had significantly higher PSNR and SSIM than the 3 min SPECT/CT images (51.44 versus 38.44,< 0.0001; 0.863 versus 0.752,< 0.0001). The SUVof the 3 min DL and 20 min SPECT/CT images showed a strong linear relationship (= 0.991;< 0.0001).Ultrafast SPECT/CT with a 1/7 acquisition time can be enhanced by a deep learning method to achieve comparable image quality and diagnostic value to those of standard acquisition.

Authors

  • Na Qi
    College of Life Science, Capital Normal University, Beijing, 100048, China.
  • Boyang Pan
    RadioDynamic Healthcare, Shanghai, People's Republic of China.
  • Qingyuan Meng
    Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, 200120, Shanghai, People's Republic of China.
  • Yihong Yang
    Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, 200120, Shanghai, People's Republic of China.
  • Tao Feng
    School of Pharmacy, Anhui University of Chinese Medicine, Anhui Key Laboratory of Modern Chinese Materia Medica Hefei 230012 People's Republic of China tfeng@mail.scuec.edu.cn wanggk@ahtcm.edu.cn.
  • Hui Liu
    Institute of Urology and Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
  • Nan-Jie Gong
    Vector Lab for Intelligent Medical Imaging and Neural Engineering, International Innovation Center of Tsinghua University, Shanghai, China.
  • Jun Zhao