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

Na Qi; Boyang Pan; Qingyuan Meng; Yihong Yang; Tao Feng; Hui Liu; Nan-Jie Gong; Jun Zhao

doi:10.1088/1361-6560/acddc6

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

Phys Med Biol. 2023 Jun 30;68(13). doi: 10.1088/1361-6560/acddc6.

Authors

Na Qi¹, Boyang Pan², Qingyuan Meng¹, Yihong Yang¹, Tao Feng³, Hui Liu⁴, Nan-Jie Gong³, Jun Zhao¹

Affiliations

¹ Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, 200120, Shanghai, People's Republic of China.
² RadioDynamic Healthcare, Shanghai, People's Republic of China.
³ Laboratory for Intelligent Medical Imaging, Tsinghua Cross-Strait Research Institute, Beijing, People's Republic of China.
⁴ Department of Engineering Physics, Tsinghua University, Beijing, People's Republic of China.

PMID: 37307847
DOI: 10.1088/1361-6560/acddc6

Abstract

Objectives. 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.Approach. 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_max) 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.Main results. 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 (P< 0.0001). The diagnostic performance of the 20 min and 3 min DL SPECT/CT images was similar for reviewer 1 (pairedX²= 0.333,P= 0.564) and reviewer 2 (pairedX²= 0.05,P= 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,P< 0.0001; 0.863 versus 0.752,P< 0.0001). The SUV_maxof the 3 min DL and 20 min SPECT/CT images showed a strong linear relationship (r= 0.991;P< 0.0001).Significance.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.

Keywords: SPECT/CT; bone; deep learning; diagnostic efficiency.

Creative Commons Attribution license.

MeSH terms

Deep Learning*
Humans
Prospective Studies
Single Photon Emission Computed Tomography Computed Tomography
Technetium Tc 99m Medronate*
Tomography, Emission-Computed, Single-Photon / methods

Substances

Technetium Tc 99m Medronate