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

arXiv: https://arxiv.org/abs/2501.03293 · HTML: https://mlsanity.com/paper/2501.03293/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 6 Jan 2025.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 4 of 4)

- [MoDL: Model Based Deep Learning Architecture for Inverse Problems](https://mlsanity.com/paper/1712.02862/) (2017)
- [Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)](https://mlsanity.com/paper/1912.03433/) (2019)
- [Physics-Informed DeepMRI: Bridging the Gap from Heat Diffusion to k-Space Interpolation](https://mlsanity.com/paper/2308.15918/) (2023)
- [MR Optimized Reconstruction of Simultaneous Multi-Slice Imaging Using Diffusion Model](https://mlsanity.com/paper/2408.08883/) (2024)

## Built on by (most cited first, 1 of 1)

- [Back to Physics: Operator-Guided Generative Paths for SMS MRI Reconstruction](https://mlsanity.com/paper/2602.07820/) (2026)

## Abstract (arXiv)

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.