A Comprehensive Survey on Magnetic Resonance Image Reconstruction

• URL: http://arxiv.org/abs/2503.07097v1
• Date: Mon, 10 Mar 2025 09:20:53 GMT
• Title: A Comprehensive Survey on Magnetic Resonance Image Reconstruction
• Authors: Xiaoyan Kui, Zijie Fan, Zexin Ji, Qinsong Li, Chengtao Liu, Weixin Si, Beiji Zou,
• Abstract summary: Deep learning-based MRI reconstruction has made significant progress in recent years.<n>However, MRI reconstruction remains a challenging problem that has yet to be fully resolved.
• Score: 6.157032790444799
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Magnetic resonance imaging (MRI) reconstruction is a fundamental task aimed at recovering high-quality images from undersampled or low-quality MRI data. This process enhances diagnostic accuracy and optimizes clinical applications. In recent years, deep learning-based MRI reconstruction has made significant progress. Advancements include single-modality feature extraction using different network architectures, the integration of multimodal information, and the adoption of unsupervised or semi-supervised learning strategies. However, despite extensive research, MRI reconstruction remains a challenging problem that has yet to be fully resolved. This survey provides a systematic review of MRI reconstruction methods, covering key aspects such as data acquisition and preprocessing, publicly available datasets, single and multi-modal reconstruction models, training strategies, and evaluation metrics based on image reconstruction and downstream tasks. Additionally, we analyze the major challenges in this field and explore potential future directions.

Related papers

• Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration [1.167578793004766]
  Long acquisition times can lead to patient discomfort, motion artifacts, and limiting real-time applications. Deep learning (DL) has emerged as a powerful tool for improving MRI reconstruction.
  arXiv  Detail & Related papers  (2025-01-24T01:07:58Z)
• 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.<n>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)
• CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction [62.61209705638161]
  There has been growing interest in deep learning-based CMR imaging algorithms. Deep learning methods require large training datasets. This dataset includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects.
  arXiv  Detail & Related papers  (2023-09-19T15:14:42Z)
• CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI Reconstruction [25.078280843551322]
  We introduce a self-supervised pretraining procedure using contrastive learning to improve the accuracy of undersampled MRI reconstruction. Our experiments demonstrate improved reconstruction accuracy across a range of acceleration factors and datasets.
  arXiv  Detail & Related papers  (2023-06-01T10:29:58Z)
• Iterative Data Refinement for Self-Supervised MR Image Reconstruction [18.02961646651716]
  We propose a data refinement framework for self-supervised MR image reconstruction. We first analyze the reason of the performance gap between self-supervised and supervised methods. Then, we design an effective self-supervised training data refinement method to reduce this data bias.
  arXiv  Detail & Related papers  (2022-11-24T06:57:16Z)
• Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction [68.80715727288514]
  We show how to unfold an iterative MGDUN algorithm into a novel model-guided deep unfolding network by taking the MRI observation matrix. In this paper, we propose a novel Model-Guided interpretable Deep Unfolding Network (MGDUN) for medical image SR reconstruction.
  arXiv  Detail & Related papers  (2022-09-15T03:58:30Z)
• 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)
• Multi-modal Aggregation Network for Fast MR Imaging [85.25000133194762]
  We propose a novel Multi-modal Aggregation Network, named MANet, which is capable of discovering complementary representations from a fully sampled auxiliary modality. In our MANet, the representations from the fully sampled auxiliary and undersampled target modalities are learned independently through a specific network. Our MANet follows a hybrid domain learning framework, which allows it to simultaneously recover the frequency signal in the $k$-space domain.
  arXiv  Detail & Related papers  (2021-10-15T13:16:59Z)
• End-to-End AI-based MRI Reconstruction and Lesion Detection Pipeline for Evaluation of Deep Learning Image Reconstruction [1.2649531564873526]
  We propose an end-to-end deep learning framework for image reconstruction and pathology detection. The solution is demonstrated for a use case in detecting meniscal tears on knee MRI studies.
  arXiv  Detail & Related papers  (2021-09-23T17:46:08Z)
• Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning [62.17532253489087]
  Deep learning methods have been shown to produce superior performance on MR image reconstruction. These methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations. We propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients' privacy.
  arXiv  Detail & Related papers  (2021-03-03T03:04:40Z)
• Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurations [40.263770807921524]
  Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. The Multi-Coil Magnetic Resonance Image (MC-MRI) Reconstruction Challenge provides a benchmark that aims at addressing these issues. We describe the challenge experimental design, and summarize the results of a set of baseline and state of the art brain MRI reconstruction models.
  arXiv  Detail & Related papers  (2020-11-10T04:11:48Z)

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.