Unsupervised MRI Reconstruction with Generative Adversarial Networks

• URL: http://arxiv.org/abs/2008.13065v1
• Date: Sat, 29 Aug 2020 22:00:49 GMT
• Title: Unsupervised MRI Reconstruction with Generative Adversarial Networks
• Authors: Elizabeth K. Cole, John M. Pauly, Shreyas S. Vasanawala, Frank Ong
• Abstract summary: We present a deep learning framework for MRI reconstruction without any fully-sampled data using generative adversarial networks. We test the proposed method in two scenarios: retrospectively undersampled fast spin echo knee exams and prospectively undersampled abdominal DCE.
• Score: 14.253509181850502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning-based image reconstruction methods have achieved promising results across multiple MRI applications. However, most approaches require large-scale fully-sampled ground truth data for supervised training. Acquiring fully-sampled data is often either difficult or impossible, particularly for dynamic contrast enhancement (DCE), 3D cardiac cine, and 4D flow. We present a deep learning framework for MRI reconstruction without any fully-sampled data using generative adversarial networks. We test the proposed method in two scenarios: retrospectively undersampled fast spin echo knee exams and prospectively undersampled abdominal DCE. The method recovers more anatomical structure compared to conventional methods.

Related papers

• Unsupervised Adaptive Implicit Neural Representation Learning for Scan-Specific MRI Reconstruction [8.721677700107639]
  We propose an unsupervised, adaptive coarse-to-fine framework that enhances reconstruction quality without being constrained by the sparsity levels or patterns in under-sampling. We integrate a novel learning strategy that progressively refines the use of acquired k-space signals for self-supervision. Our method outperforms current state-of-the-art scan-specific MRI reconstruction techniques, for up to 8-fold under-sampling.
  arXiv  Detail & Related papers  (2023-12-01T16:00:16Z)
• One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction [20.84830225817378]
  Deep Learning (DL) has proven effective for fast MRI image reconstruction, but its broader applicability has been constrained. We present a novel Physics-Informed Synthetic data learning framework for Fast MRI, called PISF. PISF marks a breakthrough by enabling generalized DL for multi-scenario MRI reconstruction through a single trained model.
  arXiv  Detail & Related papers  (2023-07-25T03:11:24Z)
• Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models [75.52575380824051]
  We present a learning method to optimize sub-sampling patterns for compressed sensing multi-coil MRI. We use a single-step reconstruction based on the posterior mean estimate given by the diffusion model and the MRI measurement process. Our method requires as few as five training images to learn effective sampling patterns.
  arXiv  Detail & Related papers  (2023-06-05T22:09:06Z)
• Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction [14.754843942604472]
  We present a fully self-supervised approach for accelerated non-Cartesian MRI reconstruction. In training, the undersampled data are split into disjoint k-space domain partitions. For the image-level self-supervision, we enforce appearance consistency obtained from the original undersampled data.
  arXiv  Detail & Related papers  (2023-02-18T06:11:49Z)
• 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)
• One-dimensional Deep Low-rank and Sparse Network for Accelerated MRI [19.942978606567547]
  Deep learning has shown astonishing performance in accelerated magnetic resonance imaging (MRI) Most state-of-the-art deep learning reconstructions adopt the powerful convolutional neural network and perform 2D convolution. We present a new approach that explores the 1D convolution, making the deep network much easier to be trained and generalized.
  arXiv  Detail & Related papers  (2021-12-09T06:39:55Z)
• 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)
• 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)
• Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation [60.663499381212425]
  We enhance the image quality by using a Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique. In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.
  arXiv  Detail & Related papers  (2021-04-05T13:05:22Z)
• MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data [75.73881040581767]
  We propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations. Our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.
  arXiv  Detail & Related papers  (2020-02-09T14:11:50Z)

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.