Robust Simultaneous Multislice MRI Reconstruction Using Deep Generative Priors

• URL: http://arxiv.org/abs/2407.21600v2
• Date: Thu, 23 Jan 2025 07:53:34 GMT
• Title: Robust Simultaneous Multislice MRI Reconstruction Using Deep Generative Priors
• Authors: Shoujin Huang, Guanxiong Luo, Yunlin Zhao, Yilong Liu, Yuwan Wang, Kexin Yang, Jingzhe Liu, Hua Guo, Min Wang, Lingyan Zhang, Mengye Lyu,
• Abstract summary: We introduce ROGER, a robust SMS MRI reconstruction method based on deep generative priors. Our method incorporates a low-frequency enhancement (LFE) module to address the practical issue that SMS-accelerated fast spin echo (FSE) and echo planar imaging (EPI) sequences cannot easily embed fully-sampled autocalibration signals.
• Score: 4.735790956167054
• License:
• Abstract: Simultaneous multislice (SMS) imaging is a powerful technique for accelerating magnetic resonance imaging (MRI) acquisitions. However, SMS reconstruction remains challenging due to complex signal interactions between and within the excited slices. In this study, we introduce ROGER, a robust SMS MRI reconstruction method based on deep generative priors. Utilizing denoising diffusion probabilistic models (DDPM), ROGER begins with Gaussian noise and gradually recovers individual slices through reverse diffusion iterations while enforcing data consistency from measured k-space data within the readout concatenation framework. The posterior sampling procedure is designed such that the DDPM training can be performed on single-slice images without requiring modifications for SMS tasks. Additionally, our method incorporates a low-frequency enhancement (LFE) module to address the practical issue that SMS-accelerated fast spin echo (FSE) and echo planar imaging (EPI) sequences cannot easily embed fully-sampled autocalibration signals. Extensive experiments on both retrospectively and prospectively accelerated datasets demonstrate that ROGER consistently outperforms existing methods, enhancing both anatomical and functional imaging with strong out-of-distribution generalization. The source code and sample data for ROGER are available at https://github.com/Solor-pikachu/ROGER.

Related papers

• ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning [51.26601171361753]
  We propose ContextMRI, a text-conditioned diffusion model for MRI that integrates granular metadata into the reconstruction process. We show that increasing the fidelity of metadata, ranging from slice location and contrast to patient age, sex, and pathology, systematically boosts reconstruction performance.
  arXiv  Detail & Related papers  (2025-01-08T05:15:43Z)
• NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation [55.51412454263856]
  This paper proposes to directly modulate the generation process of diffusion models using fMRI signals. By training with about 67,000 fMRI-image pairs from various individuals, our model enjoys superior fMRI-to-image decoding capacity.
  arXiv  Detail & Related papers  (2024-03-27T02:42:52Z)
• DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models [11.068359534951783]
  DiffCMR perceives conditioning signals from the under-sampled MRI image slice and generates its corresponding fully-sampled MRI image slice. We validate DiffCMR with cine reconstruction and T1/T2 mapping tasks on MICCAI 2023 Cardiac MRI Reconstruction Challenge dataset. Results show that our method achieves state-of-the-art performance, exceeding previous methods by a significant margin.
  arXiv  Detail & Related papers  (2023-12-08T06:11:21Z)
• vSHARP: variable Splitting Half-quadratic Admm algorithm for Reconstruction of inverse-Problems [7.043932618116216]
  vSHARP (variable Splitting Half-quadratic ADMM algorithm for Reconstruction of inverse Problems) is a novel Deep Learning (DL)-based method for solving ill-posed inverse problems arising in Medical Imaging (MI) For data consistency, vSHARP unrolls a differentiable gradient descent process in the image domain, while a DL-based denoiser, such as a U-Net architecture, is applied to enhance image quality. Our comparative analysis with state-of-the-art methods demonstrates the superior performance of vSHARP in these applications.
  arXiv  Detail & Related papers  (2023-09-18T17:26:22Z)
• Self-Supervised MRI Reconstruction with Unrolled Diffusion Models [27.143473617162304]
  We propose a novel self-supervised deep reconstruction model, named Self-Supervised Diffusion Reconstruction (SSDiffRecon) SSDiffRecon expresses a conditional diffusion process that interleaves cross-attention transformers for reverse diffusion steps with data-consistency blocks for physics-driven processing. Experiments on public brain MR datasets demonstrate the superiority of SSDiffRecon against state-of-the-art supervised, and self-supervised baselines in terms of reconstruction speed and quality.
  arXiv  Detail & Related papers  (2023-06-29T03:31:46Z)
• Holistic Multi-Slice Framework for Dynamic Simultaneous Multi-Slice MRI Reconstruction [8.02450593595801]
  We propose a novel DL-based framework for dynamic SMS reconstruction. Our main contributions are 1) a combination of data transformation steps and network design that effectively leverages the unique characteristics of undersampled dynamic SMS data, and 2) an MR physics-guided transfer learning strategy that addresses the data scarcity issue.
  arXiv  Detail & Related papers  (2023-01-03T21:09:51Z)
• A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction [50.1787181309337]
  We propose a convolutional long short-term memory (Conv-LSTM) based recurrent neural network (RNN), or ConvLR, to reconstruct interventional images with golden-angle radial sampling. The proposed algorithm has the potential to achieve real-time i-MRI for DBS and can be used for general purpose MR-guided intervention.
  arXiv  Detail & Related papers  (2022-03-28T14:03:45Z)
• ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer [60.27951773998535]
  We propose a recurrent transformer model, namely textbfReconFormer, for MRI reconstruction. It can iteratively reconstruct high fertility magnetic resonance images from highly under-sampled k-space data. We show that it achieves significant improvements over the state-of-the-art methods with better parameter efficiency.
  arXiv  Detail & Related papers  (2022-01-23T21:58:19Z)
• Reference-based Magnetic Resonance Image Reconstruction Using Texture Transforme [86.6394254676369]
  We propose a novel Texture Transformer Module (TTM) for accelerated MRI reconstruction. We formulate the under-sampled data and reference data as queries and keys in a transformer. The proposed TTM can be stacked on prior MRI reconstruction approaches to further improve their performance.
  arXiv  Detail & Related papers  (2021-11-18T03:06:25Z)
• Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers [0.0]
  We introduce a novel unsupervised MRI reconstruction method based on zero-Shot Learned Adrial TransformERs (SLATER) A zero-shot reconstruction is performed on undersampled test data, where inference is performed by optimizing network parameters. Experiments on brain MRI datasets clearly demonstrate the superior performance of SLATER against several state-of-the-art unsupervised methods.
  arXiv  Detail & Related papers  (2021-05-15T02:01:21Z)
• Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE) [50.65891535040752]
  We propose a diffusion encoding scheme, called Slice-Interleaved Diffusion. SIDE, that interleaves each diffusion-weighted (DW) image volume with slices encoded with different diffusion gradients. We also present a method based on deep learning for effective reconstruction of DW images from the highly slice-undersampled data.
  arXiv  Detail & Related papers  (2020-02-25T14:48:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.