Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstruction

TL;DR

This paper presents an adaptive mask-guided diffusion model for MRI reconstruction that intelligently separates frequency components in k-space, leading to improved image quality from under-sampled data.

Contribution

It introduces a novel adaptive mask mechanism based on k-space frequency distribution, enhancing MRI reconstruction by focusing on important frequency regions.

Findings

Improved MRI reconstruction quality over baseline methods.

Effective separation of high-frequency and low-frequency components.

Flexible framework for future k-space data optimization.

Abstract

As the deep learning revolution marches on, masked modeling has emerged as a distinctive approach that involves predicting parts of the original data that are proportionally masked during training, and has demonstrated exceptional performance in multiple fields. Magnetic Resonance Imaging (MRI) reconstruction is a critical task in medical imaging that seeks to recover high-quality images from under-sampled k-space data. However, previous MRI reconstruction strategies usually optimized the entire image domain or k-space, without considering the importance of different frequency regions in the k-space This work introduces a diffusion model based on adaptive masks (AMDM), which utilizes the adaptive adjustment of frequency distribution based on k-space data to develop a hybrid masks mechanism that adapts to different k-space inputs. This enables the effective separation of high-frequency and low-frequency components, producing diverse frequency-specific representations. Additionally, the k-space frequency distribution informs the generation of adaptive masks, which, in turn, guide a closed-loop diffusion process. Experimental results verified the ability of this method to learn specific frequency information and thereby improved the quality of MRI reconstruction, providing a flexible framework for optimizing k-space data using masks in the future.