Arbitrary scale super-resolution diffusion model for brain MRI images

Given the constraints posed by hardware capacity, scan duration, and patient cooperation, the reconstruction of magnetic resonance imaging (MRI) images emerges as a pivotal aspect of medical imaging research. Currently, deep learning-based super-resolution (SR) methods have been widely discussed in medical image processing due to their ability to reconstruct high-quality, high resolution (HR) images from low resolution (LR) inputs. However, most existing MRI SR methods are designed for specific magnifications and cannot generate MRI images at arbitrary scales, which hinders the radiologists from fully visualizing the lesions. Moreover, current arbitrary scale SR methods often suffer from issues like excessive smoothing and artifacts. In this paper, we propose an Arbitrary Scale Super-Resolution Diffusion Model (ASSRDM), which combines implicit neural representation with the denoising diffusion probabilistic model to achieve arbitrary-scale, high-fidelity medical images SR. Moreover, we formulate a continuous resolution regulation mechanism, comprising a multi-scale LR guidance network and a scaling factor. The scaling factor finely adjusts the resolution and dynamically influences the weighting of LR details and synthesized features in the final output. This capability allows the model to seamlessly adapt to the requirements of continuous resolution adjustments. Additionally, the multi-scale LR guidance network provides the denoising block with multi-resolution LR features to enrich texture information and restore high-frequency details. Extensive experiments conducted on the IXI and fastMRI datasets demonstrate that our ASSRDM exhibits superior performance compared to existing techniques and has tremendous potential in clinical practice.