C Lin1, Y-C Chang2, H-Y Chiu1, C-H Cheng3, H-M Huang4. 1. Department of Nuclear Medicine, Chang Gung Memorial Hospital, No. 5, Fuxing Street, Gueishan Dist., Taoyuan, 33305, Taiwan. 2. Department of Nuclear Medicine, Chang Gung Memorial Hospital, No. 5, Fuxing Street, Gueishan Dist., Taoyuan, 33305, Taiwan; Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, No. 259, Wenhua 1st Rd., Guishan Dist., Taoyuan, 33302, Taiwan. 3. Department of Paediatrics, Chang Gung University, No. 259, Wenhua 1st Rd, Guishan Dist., Taoyuan, 33302, Taiwan; Department of Paediatrics, Chang Gung Memorial Hospital, No. 5, Fuxing Street, Gueishan Dist., Taoyuan, 33305, Taiwan. 4. Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, No.1, Sec. 1, Jen Ai Rd, Zhongzheng Dist., Taipei City, 100, Taiwan. Electronic address: b9003205@gmail.com.
Abstract
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.
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.