A Noise-to-Noise Training Approach for Robust Motion-Compensated Processing in Cardiac-Gated Images

Deep learning (DL) based methods have become increasingly attractive in image motion estimation applications. In this study we investigate the use of a noise-to-noise (N2N) training strategy for improving the robustness of a DL motion-estimation network in the presence of high imaging noise in the image data. This strategy is based on the rationale that the image motion estimated from one noisy measurement of a given image pair should be equally applicable for registering the image pair when it is obtained from a different noisy measurement. In the experiments we demonstrated the proposed approach for motion-compensated noise suppression in cardiac-gated SPECT imaging, where we utilized the image data acquired from 237 clinical cases at 50% reduced dose. The proposed N2N approach yielded an average reduction of 18.5% in the mean-squared-error (MSE) of the gate frames compared to a traditional training approach. It also achieved a more uniform performance among the different cardiac phases in spite of their statistical image variability.