Self-Supervised High-Dimentional Magnetic Resonance Image Denoising Using Super-Resolved Single Noisy Image

Denoising of magnetic resonance image (MRI) is a critical step in MRI image processing and analysis. With the advantage of not requiring paired noisy-clean images for training, self-supervised denoising methods are emerging as competitive alternatives to supervised denoising methods in MRI denoising. However, current self-supervised image denoising methods are not effective enough for MRI. In this work, we propose Noise2SR-M (N2SR-M), a self supervised denoising method for MR images, which is more efficient for high-dimensional MR images. N2SR-M is designed for training with paired noisy data of different sizes divided from a single high-dimensional noisy input image. Our N2SR-M model is able to utilize the redundant information from the additional image dimension to generate noisy image pairs for the denoising task. With the combination of additional dimension constraint and the effectiveness of SR method based training image pair generation, our model is more efficient for denoising high-dimensional MR images. The quantitative and qualitative improvements in blood oxygenation level dependent (BOLD) imaging denoising task demonstrate that N2SR-M successfully restores detailed image contents and removes tiny structural noise and artifacts from noise-corrupted high-dimensional MRI. Moreover, the denoised BOLD image also induces more efficient R2* image computation.