Self-Supervised Denoising for Longitudinal MRI

In MR guided radiation therapy, images of patients are acquired daily. However, scan times are long to acquire images with an acceptable SNR for treatment planning and adaptation. In this study we test a self-supervised machine learning based approach for denoising data that can utilize previous scans of the same patient to improve quality of the denoised image. Results on a numerical phantom and clinical images are presented and compared to a popular non-machine learning denoising algorithm.