Uncertainty Estimation and Propagation in Accelerated MRI Reconstruction

• URL: http://arxiv.org/abs/2308.02631v1
• Date: Fri, 4 Aug 2023 16:41:05 GMT
• Title: Uncertainty Estimation and Propagation in Accelerated MRI Reconstruction
• Authors: Paul Fischer, Thomas K\"ustner, Christian F. Baumgartner
• Abstract summary: Deep learning techniques are known to fail unexpectedly and hallucinate structures. Well-calibrated uncertainty quantification will be a key ingredient for safe use of this technology in clinical practice. We propose a novel probabilistic reconstruction technique (PHiRec) building on the idea of conditional hierarchical variational autoencoders.
• Score: 1.9392807501735063
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: MRI reconstruction techniques based on deep learning have led to unprecedented reconstruction quality especially in highly accelerated settings. However, deep learning techniques are also known to fail unexpectedly and hallucinate structures. This is particularly problematic if reconstructions are directly used for downstream tasks such as real-time treatment guidance or automated extraction of clinical paramters (e.g. via segmentation). Well-calibrated uncertainty quantification will be a key ingredient for safe use of this technology in clinical practice. In this paper we propose a novel probabilistic reconstruction technique (PHiRec) building on the idea of conditional hierarchical variational autoencoders. We demonstrate that our proposed method produces high-quality reconstructions as well as uncertainty quantification that is substantially better calibrated than several strong baselines. We furthermore demonstrate how uncertainties arising in the MR econstruction can be propagated to a downstream segmentation task, and show that PHiRec also allows well-calibrated estimation of segmentation uncertainties that originated in the MR reconstruction process.

Related papers

• How Should We Evaluate Uncertainty in Accelerated MRI Reconstruction? [1.124958340749622]
  We propose a new approach to evaluating reconstruction variability based on apparent anatomical changes in the reconstruction. We show that models with high scores on often used quality metrics such as SSIM and PSNR, can nonetheless display high levels of variance and bias in anatomical measures.
  arXiv  Detail & Related papers  (2025-03-13T16:34:22Z)
• Guiding Quantitative MRI Reconstruction with Phase-wise Uncertainty [2.0542982544717012]
  We in-troduce PUQ, a novel approach that pioneers the use of uncertainty infor-mation for qMRI reconstruction. PUQ employs a two-stage reconstruction and parameter fitting framework, where phase-wise uncertainty is estimated during reconstruction and utilized in the fitting stage. Compared to existing qMRI reconstruction methods, PUQ achieved the state-of-the-art performance in parameter map-pings.
  arXiv  Detail & Related papers  (2025-02-28T09:21:01Z)
• Segmentation-guided MRI reconstruction for meaningfully diverse reconstructions [33.89679056028516]
  Inverse problems, such as accelerated MRI reconstruction, are ill-posed and an infinite amount of possible and plausible solutions exist. This uncertainty is mostly not analyzed in the literature, even though probabilistic reconstruction models are commonly used. We build on MRI reconstruction approaches based on diffusion models, generating two meaningfully diverse reconstructions corresponding to an upper and lower bound segmentation.
  arXiv  Detail & Related papers  (2024-07-25T13:23:57Z)
• Orientation-Shared Convolution Representation for CT Metal Artifact Learning [63.67718355820655]
  During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts. Existing deep-learning-based methods have gained promising reconstruction performance. We propose an orientation-shared convolution representation strategy to adapt the physical prior structures of artifacts.
  arXiv  Detail & Related papers  (2022-12-26T13:56:12Z)
• Uncertainty Quantification for Deep Unrolling-Based Computational Imaging [0.0]
  We propose a learning-based image reconstruction framework that incorporates the observation model into the reconstruction task. We show that the proposed framework can provide uncertainty information while achieving comparable reconstruction performance to state-of-the-art deep unrolling methods.
  arXiv  Detail & Related papers  (2022-07-02T00:22:49Z)
• Rethinking the optimization process for self-supervised model-driven MRI reconstruction [16.5013498806588]
  K2Calibrate is a K-space adaptation strategy for self-supervised model-driven MR reconstruction optimization. It can reduce the network's reconstruction deterioration caused by statistically dependent noise. It achieves better results than five state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-18T03:41:36Z)
• Towards performant and reliable undersampled MR reconstruction via diffusion model sampling [67.73698021297022]
  DiffuseRecon is a novel diffusion model-based MR reconstruction method. It guides the generation process based on the observed signals. It does not require additional training on specific acceleration factors.
  arXiv  Detail & Related papers  (2022-03-08T02:25:38Z)
• 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)
• 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)
• 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)
• Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction [63.202627467245584]
  We introduce a Bayesian variational framework to quantify the model-immanent (epistemic) uncertainty. We demonstrate that our approach yields competitive results for undersampled MRI reconstruction.
  arXiv  Detail & Related papers  (2021-02-12T18:08:14Z)
• Adversarial Robust Training of Deep Learning MRI Reconstruction Models [0.0]
  We employ adversarial attacks to generate small synthetic perturbations, which are difficult to reconstruct for a trained Deep Learning reconstruction network. We then use robust training to increase the network's sensitivity to these small features and encourage their reconstruction. Experimental results show that by introducing robust training to a reconstruction network, the rate of false negative features can be reduced.
  arXiv  Detail & Related papers  (2020-10-30T19:26:14Z)

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.