Reducing scan time of paediatric ^99mTc-DMSA SPECT via deep learning

AIM: To investigate the feasibility of reducing the scan time of paediatric technetium 99m (^99mTc) dimercaptosuccinic acid (DMSA) single-photon-emission computed tomographic (SPECT) using a deep learning (DL) method. MATERIAL AND METHODS: A total of 112 paediatric ^99mTc-DMSA renal SPECT scans were analysed retrospectively. Of the 112 examinations, 88 (84 for training and four for validation) were used to train a DL-based model that could generate full-acquisition-time reconstructed SPECT images from half-time acquisition. The remaining 24 examinations were used to evaluate the performance of the trained model. RESULTS: DL-based SPECT images obtained from half-time acquisition have image quality similar to the standard clinical SPECT images obtained from full-acquisition-time acquisition. Moreover, the accuracy, sensitivity and specificity of the DL-based SPECT images for detection of affected kidneys were 91.7%, 83.3%, and 100%, respectively. CONCLUSION: These preliminary results suggest that DL has the potential to reduce the scan time of paediatric ^99mTc-DMSA SPECT imaging while maintaining diagnostic accuracy.