PURPOSE: Reconstructed transaxial cardiac SPECT images need to be reoriented into standard short-axis slices for subsequent accurate processing and analysis. We proposed a novel deep-learning-based method for fully automatic reorientation of cardiac SPECT images and evaluated its performance on data from two clinical centers. METHODS: We used a convolutional neural network to predict the 6 rigid-body transformation parameters and a spatial transformation network was then implemented to apply these parameters on the input images for image reorientation. A novel compound loss function which balanced the parametric similarity and penalized discrepancy of the prediction and training dataset was utilized in the training stage. Data from a set of 322 patients underwent data augmentation to 6440 groups of images for the network training, and a dataset of 52 patients from the same center and 23 patients from another center were used for evaluation. Similarity of the 6 parameters was analyzed between the proposed and the manual methods. Polar maps were generated from the output images and the averaged count values of the 17 segments were computed from polar maps to evaluate the quantitative accuracy of the proposed method. RESULTS: All the testing patients achieved automatic reorientation successfully. Linear regression results showed the 6 predicted rigid parameters and the average count value of the 17 segments having good agreement with the reference manual method. No significant difference by paired t-test was noticed between the rigid parameters of our method and the manual method (p > 0.05). Average count values of the 17 segments show a smaller difference of the proposed and manual methods than those between the existing and manual methods. CONCLUSION: The results strongly indicate the feasibility of our method in accurate automatic cardiac SPECT reorientation. This deep-learning-based reorientation method has great promise for clinical application and warrants further investigation.

Chest radiography (CR) is the primary examination for the evaluation and follow-up of various thoracic diseases. The number of examinations is steadily on the increase, as is evidenced by the national health insurance data in Korea. However, due to the relative shortage of experienced radiologists, many institutions cannot provide timely interpretation of CRs or depend on outsourcing for interpretation. In this background, artificial intelligence (AI) for the evaluation of CR has been actively investigated, and several AI-based software as medical devices (AI-SaMDs) have begun to be used in clinical practice. However, there has been limited discussion on how to use AI-SaMDs in clinical practice; there are also concerns about inappropriate use or abuse of AI-SaMDs resulting in patient harm and liability for physicians. This article introduces the current situation regarding the application of AI-SaMD for CR in clinical practice and presents the opinion of the Korean Society of Thoracic Radiology (KSTR) toward use of this application.