Accounting For Inter-Subject Variations In Deep Learning For Reduced-Dose Studies In Cardiac Spect

Deep learning (DL) denoising has recently found applications in a wide range of important problems in medical imaging. A practical challenge encountered in clinical applications is that the acquired image data can exhibit great variability in terms of noise level among different subjects. In this study, we investigate whether it can be beneficial to exploit the varying data statistics among different subjects in a DL denoising network. We propose a modified loss function in the form of a weighted sum of mean-squared-errors for DL training in which the contribution from individual subjects is adjusted according to their noise levels. In the experiments we demonstrated this approach with a set of 895 clinical acquisitions in cardiac SPECT studies with 50% of standard dose. The quantitative results show that the proposed approach can further improve both the regional accuracy of the reconstructed left ventricle and the detection accuracy of perfusion defects.