mlsanityData to 7 Oct 2026

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

arXiv 2501.03293 ↗First posted 6 Jan 2025eess.IVcited by 1

On the map: Diffusion models → Medical imaging and segmentation

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.

Key facts. Cited by 1 paper in the map (data through 7 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.

Builds on

4 of 10 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024MR Optimized Reconstruction of Simultaneous Multi-Slice Imaging Using Diffusion Model5 Aug

Other references · 3

  1. 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 384
  2. 2019Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)cited by 29
  3. 2023Physics-Informed DeepMRI: Bridging the Gap from Heat Diffusion to k-Space Interpolationcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Back to Physics: Operator-Guided Generative Paths for SMS MRI Reconstructioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Robust Simultaneous Multislice MRI Reconstruction Using Slice-Wise Learned Generative Diffusion Priors
  2. 2023Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstruction
  3. 2023Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction
  4. 2023Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without Noise
  5. 2022High-Frequency Space Diffusion Models for Accelerated MRI
  6. 2025Physics-Guided Diffusion Priors for Multi-Slice Reconstruction in Scientific Imaging
  7. 2025Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstruction
  8. 2023Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models