Benchmarking Self-Supervised Methods for Accelerated MRI Reconstruction

• URL: http://arxiv.org/abs/2502.14009v1
• Date: Wed, 19 Feb 2025 12:10:44 GMT
• Title: Benchmarking Self-Supervised Methods for Accelerated MRI Reconstruction
• Authors: Andrew Wang, Mike Davies,
• Abstract summary: Reconstructing MRI from highly undersampled measurements is crucial for accelerating medical imaging. While supervised deep learning approaches have shown remarkable success, they rely on fully-sampled ground truth data. We present the first comprehensive review of loss functions from all feedforward self-supervised methods and the first benchmark on accelerated MRI reconstruction without ground truth.
• Score: 2.260147251787331
• License:
• Abstract: Reconstructing MRI from highly undersampled measurements is crucial for accelerating medical imaging, but is challenging due to the ill-posedness of the inverse problem. While supervised deep learning approaches have shown remarkable success, they rely on fully-sampled ground truth data, which is often impractical or impossible to obtain. Recently, numerous self-supervised methods have emerged that do not require ground truth, however, the lack of systematic comparison and standard experimental setups have hindered research. We present the first comprehensive review of loss functions from all feedforward self-supervised methods and the first benchmark on accelerated MRI reconstruction without ground truth, showing that there is a wide range in performance across methods. In addition, we propose Multi-Operator Equivariant Imaging (MO-EI), a novel framework that builds on the imaging model considered in existing methods to outperform all state-of-the-art and approaches supervised performance. Finally, to facilitate reproducible benchmarking, we provide implementations of all methods in the DeepInverse library (https://deepinv.github.io) and easy-to-use demo code at https://andrewwango.github.io/deepinv-selfsup-fastmri.

Related papers

• Fully Unsupervised Dynamic MRI Reconstruction via Diffeo-Temporal Equivariance [2.260147251787331]
  Supervised learning methods are flawed as they assume periodicity, disallowing imaging of true motion. We propose an unsupervised framework to learn to reconstruct dynamic MRI sequences from undersampled measurements alone. Our method is agnostic to the underlying neural network architecture and can be used to adapt the latest paradigms and post-processing approaches.
  arXiv  Detail & Related papers  (2024-10-11T09:16:30Z)
• A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection [52.228708947607636]
  This paper introduces a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework for new methods. The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics. We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
  arXiv  Detail & Related papers  (2024-06-05T13:40:07Z)
• SinSR: Diffusion-Based Image Super-Resolution in a Single Step [119.18813219518042]
  Super-resolution (SR) methods based on diffusion models exhibit promising results. But their practical application is hindered by the substantial number of required inference steps. We propose a simple yet effective method for achieving single-step SR generation, named SinSR.
  arXiv  Detail & Related papers  (2023-11-23T16:21:29Z)
• RIDE: Self-Supervised Learning of Rotation-Equivariant Keypoint Detection and Invariant Description for Endoscopy [83.4885991036141]
  RIDE is a learning-based method for rotation-equivariant detection and invariant description. It is trained in a self-supervised manner on a large curation of endoscopic images. It sets a new state-of-the-art performance on matching and relative pose estimation tasks.
  arXiv  Detail & Related papers  (2023-09-18T08:16:30Z)
• 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)
• Re-Evaluating LiDAR Scene Flow for Autonomous Driving [80.37947791534985]
  Popular benchmarks for self-supervised LiDAR scene flow have unrealistic rates of dynamic motion, unrealistic correspondences, and unrealistic sampling patterns. We evaluate a suite of top methods on a suite of real-world datasets. We show that despite the emphasis placed on learning, most performance gains are caused by pre- and post-processing steps.
  arXiv  Detail & Related papers  (2023-04-04T22:45:50Z)
• 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)
• Generalized Real-World Super-Resolution through Adversarial Robustness [107.02188934602802]
  We present Robust Super-Resolution, a method that leverages the generalization capability of adversarial attacks to tackle real-world SR. Our novel framework poses a paradigm shift in the development of real-world SR methods. By using a single robust model, we outperform state-of-the-art specialized methods on real-world benchmarks.
  arXiv  Detail & Related papers  (2021-08-25T22:43:20Z)
• 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)
• Unsupervised MRI Reconstruction with Generative Adversarial Networks [14.253509181850502]
  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.
  arXiv  Detail & Related papers  (2020-08-29T22:00:49Z)
• Image Matching across Wide Baselines: From Paper to Practice [80.9424750998559]
  We introduce a comprehensive benchmark for local features and robust estimation algorithms. Our pipeline's modular structure allows easy integration, configuration, and combination of different methods. We show that with proper settings, classical solutions may still outperform the perceived state of the art.
  arXiv  Detail & Related papers  (2020-03-03T15:20:57Z)

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.