A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI

TL;DR

This survey reviews the recent advances of diffusion probabilistic models in MRI, highlighting their applications in image synthesis, reconstruction, translation, segmentation, and anomaly detection, despite high computational costs.

Contribution

It provides a comprehensive overview of DPMs tailored for MRI, categorizing models by diffusion type and summarizing their applications and limitations.

Findings

DPMs achieve high-quality MRI image generation.

Diffusion models are effective in MRI reconstruction and segmentation.

Current challenges include high computational demands.

Abstract

Diffusion probabilistic models (DPMs) which employ explicit likelihood characterization and a gradual sampling process to synthesize data, have gained increasing research interest. Despite their huge computational burdens due to the large number of steps involved during sampling, DPMs are widely appreciated in various medical imaging tasks for their high-quality and diversity of generation. Magnetic resonance imaging (MRI) is an important medical imaging modality with excellent soft tissue contrast and superb spatial resolution, which possesses unique opportunities for DPMs. Although there is a recent surge of studies exploring DPMs in MRI, a survey paper of DPMs specifically designed for MRI applications is still lacking. This review article aims to help researchers in the MRI community to grasp the advances of DPMs in different applications. We first introduce the theory of two dominant kinds of DPMs, categorized according to whether the diffusion time step is discrete or continuous, and then provide a comprehensive review of emerging DPMs in MRI, including reconstruction, image generation, image translation, segmentation, anomaly detection, and further research topics. Finally, we discuss the general limitations as well as limitations specific to the MRI tasks of DPMs and point out potential areas that are worth further exploration.