K-space Diffusion Model Based MR Reconstruction Method for Simultaneous Multislice Imaging

TL;DR

This paper introduces a novel k-space diffusion model for MRI SMS reconstruction that leverages Slice GRAPPA during sampling, enabling higher acceleration factors without requiring SMS data for training.

Contribution

The study presents a new SMS reconstruction method that does not depend on SMS training data, integrating Slice GRAPPA into a k-space diffusion framework.

Findings

Outperforms traditional SMS reconstruction methods

Achieves higher acceleration factors without in-plane aliasing

Does not require SMS data for training

Abstract

Simultaneous Multi-Slice(SMS) is a magnetic resonance imaging (MRI) technique which excites several slices concurrently using multiband radiofrequency pulses to reduce scanning time. However, due to its variable data structure and difficulty in acquisition, it is challenging to integrate SMS data as training data into deep learning frameworks.This study proposed a novel k-space diffusion model of SMS reconstruction that does not utilize SMS data for training. Instead, it incorporates Slice GRAPPA during the sampling process to reconstruct SMS data from different acquisition modes.Our results demonstrated that this method outperforms traditional SMS reconstruction methods and can achieve higher acceleration factors without in-plane aliasing.