PixCUE -- Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification

• URL: http://arxiv.org/abs/2303.00111v1
• Date: Tue, 28 Feb 2023 22:26:18 GMT
• Title: PixCUE -- Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification
• Authors: Mevan Ekanayake, Kamlesh Pawar, Gary Egan, Zhaolin Chen
• Abstract summary: We introduce a method to estimate uncertainty during MRI reconstruction using a pixel classification framework. We demonstrate that this approach generates uncertainty maps that highly correlate with the reconstruction errors. We conclude that PixCUE is capable of reliably estimating the uncertainty in MRI reconstruction with a minimum additional computational cost.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning (DL) models are capable of successfully exploiting latent representations in MR data and have become state-of-the-art for accelerated MRI reconstruction. However, undersampling the measurements in k-space as well as the over- or under-parameterized and non-transparent nature of DL make these models exposed to uncertainty. Consequently, uncertainty estimation has become a major issue in DL MRI reconstruction. To estimate uncertainty, Monte Carlo (MC) inference techniques have become a common practice where multiple reconstructions are utilized to compute the variance in reconstruction as a measurement of uncertainty. However, these methods demand high computational costs as they require multiple inferences through the DL model. To this end, we introduce a method to estimate uncertainty during MRI reconstruction using a pixel classification framework. The proposed method, PixCUE (stands for Pixel Classification Uncertainty Estimation) produces the reconstructed image along with an uncertainty map during a single forward pass through the DL model. We demonstrate that this approach generates uncertainty maps that highly correlate with the reconstruction errors with respect to various MR imaging sequences and under numerous adversarial conditions. We also show that the estimated uncertainties are correlated to that of the conventional MC method. We further provide an empirical relationship between the uncertainty estimations using PixCUE and well-established reconstruction metrics such as NMSE, PSNR, and SSIM. We conclude that PixCUE is capable of reliably estimating the uncertainty in MRI reconstruction with a minimum additional computational cost.

Related papers

• Unifying Subsampling Pattern Variations for Compressed Sensing MRI with Neural Operators [72.79532467687427]
  Compressed Sensing MRI reconstructs images of the body's internal anatomy from undersampled and compressed measurements. Deep neural networks have shown great potential for reconstructing high-quality images from highly undersampled measurements. We propose a unified model that is robust to different subsampling patterns and image resolutions in CS-MRI.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Robust MRI Reconstruction by Smoothed Unrolling (SMUG) [17.391075587858058]
  We propose a novel image reconstruction framework, termed Smoothed Unrolling (SMUG) SMUG advances a deep unrolling-based MRI reconstruction model using a randomized smoothing (RS)-based robust learning approach. We show that SMUG improves the robustness of MRI reconstruction with respect to a diverse set of instability sources.
  arXiv  Detail & Related papers  (2023-12-12T22:57:14Z)
• Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI [76.60362295758596]
  Off-resonance artifacts in magnetic resonance imaging (MRI) are visual distortions that occur when the actual resonant frequencies of spins within the imaging volume differ from the expected frequencies used to encode spatial information. We propose to resolve these artifacts by lifting the 2D MRI reconstruction problem to 3D, introducing an additional "spectral" dimension to model this off-resonance.
  arXiv  Detail & Related papers  (2023-11-22T05:44:51Z)
• Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz [9.143327181265976]
  Supervised deep learning-based approaches have been investigated for solving inverse problems including image reconstruction. It is essential to assess whether a given input falls within the training data distribution for diagnostic purposes. We propose a method based on the local Lipschitz-based metric to distinguish out-of-distribution images from in-distribution with an area under the curve of 99.94%.
  arXiv  Detail & Related papers  (2023-05-12T17:17:01Z)
• 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)
• 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)
• 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)
• Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction [5.129343375966527]
  We propose a scalable and efficient framework to simultaneously quantify aleatoric and epistemic uncertainties in learned iterative image reconstruction. We show that our method exhibits competitive performance against conventional benchmarks for computed tomography with both sparse view and limited angle data.
  arXiv  Detail & Related papers  (2020-11-17T04:12:52Z)
• Probabilistic 3D surface reconstruction from sparse MRI information [58.14653650521129]
  We present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction. Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets.
  arXiv  Detail & Related papers  (2020-10-05T14:18:52Z)
• Learning to Predict Error for MRI Reconstruction [67.76632988696943]
  We demonstrate that predictive uncertainty estimated by the current methods does not highly correlate with prediction error. We propose a novel method that estimates the target labels and magnitude of the prediction error in two steps.
  arXiv  Detail & Related papers  (2020-02-13T15:55:32Z)

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.