End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRI

• URL: http://arxiv.org/abs/2403.10346v1
• Date: Fri, 15 Mar 2024 14:31:35 GMT
• Title: End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRI
• Authors: George Yiasemis, Jan-Jakob Sonke, Jonas Teuwen,
• Abstract summary: We present a novel end-to-end framework for adaptive dynamic MRI subsampling and reconstruction. Our pipeline integrates a DL-based adaptive sampler, generating case-specific dynamic subsampling patterns, trained end-to-end with a state-of-the-art 2D dynamic reconstruction network. Our results indicate superior reconstruction quality, particularly at high accelerations.
• Score: 6.875699572081067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accelerating dynamic MRI is essential for enhancing clinical applications, such as adaptive radiotherapy, and improving patient comfort. Traditional deep learning (DL) approaches for accelerated dynamic MRI reconstruction typically rely on predefined or random subsampling patterns, applied uniformly across all temporal phases. This standard practice overlooks the potential benefits of leveraging temporal correlations and lacks the adaptability required for case-specific subsampling optimization, which holds the potential for maximizing reconstruction quality. Addressing this gap, we present a novel end-to-end framework for adaptive dynamic MRI subsampling and reconstruction. Our pipeline integrates a DL-based adaptive sampler, generating case-specific dynamic subsampling patterns, trained end-to-end with a state-of-the-art 2D dynamic reconstruction network, namely vSHARP, which effectively reconstructs the adaptive dynamic subsampled data into a moving image. Our method is assessed using dynamic cine cardiac MRI data, comparing its performance against vSHARP models that employ common subsampling trajectories, and pipelines trained to optimize dataset-specific sampling schemes alongside vSHARP reconstruction. Our results indicate superior reconstruction quality, particularly at high accelerations.

Related papers

• Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxiliary Refinement Network [7.043932618116216]
  We propose a deep learning-based reconstruction method for 2D dynamic multi-contrast, multi-scheme, and multi-acceleration MRI. Our approach integrates the state-of-the-art vSHARP model, which utilizes half-quadratic variable splitting and ADMM optimization.
  arXiv  Detail & Related papers  (2024-11-02T15:59:35Z)
• ReMatching Dynamic Reconstruction Flow [55.272357926111454]
  We introduce the ReMatching framework, designed to improve generalization quality by incorporating deformation priors into dynamic reconstruction models. The framework is highly adaptable and can be applied to various dynamic representations. Our evaluations on popular benchmarks involving both synthetic and real-world dynamic scenes demonstrate a clear improvement in reconstruction accuracy of current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-11-01T16:09:33Z)
• 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)
• 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)
• Universal Generative Modeling for Calibration-free Parallel Mr Imaging [13.875986147033002]
  We present an unsupervised deep learning framework for calibration-free parallel MRI. We make use of the merits of both wavelet transform and the adaptive iteration strategy in a unified framework. We train a powerful noise conditional score network by forming wavelet tensor as the network input.
  arXiv  Detail & Related papers  (2022-01-25T10:05:39Z)
• 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)
• Deep MRI Reconstruction with Radial Subsampling [2.7998963147546148]
  Retrospectively applying a subsampling mask onto the k-space data is a way of simulating the accelerated acquisition of k-space data in real clinical setting. We compare and provide a review for the effect of applying either rectilinear or radial retrospective subsampling on the quality of the reconstructions outputted by trained deep neural networks.
  arXiv  Detail & Related papers  (2021-08-17T17:45:51Z)
• Dynamic Mode Decomposition in Adaptive Mesh Refinement and Coarsening Simulations [58.720142291102135]
  Dynamic Mode Decomposition (DMD) is a powerful data-driven method used to extract coherent schemes. This paper proposes a strategy to enable DMD to extract from observations with different mesh topologies and dimensions.
  arXiv  Detail & Related papers  (2021-04-28T22:14:25Z)
• 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)
• Regularization-Agnostic Compressed Sensing MRI Reconstruction with Hypernetworks [21.349071909858218]
  We present a novel strategy of using a hypernetwork to generate the parameters of a separate reconstruction network as a function of the regularization weight(s) At test time, for a given under-sampled image, our model can rapidly compute reconstructions with different amounts of regularization. We analyze the variability of these reconstructions, especially in situations when the overall quality is similar.
  arXiv  Detail & Related papers  (2021-01-06T18:55:37Z)

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.