A novel deep-learning–based approach for automatic reorientation of 3D cardiac SPECT images

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.

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.

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.