Evaluating structural uncertainty in accelerated MRI: are voxelwise measures useful surrogates?

• URL: http://arxiv.org/abs/2503.10527v2
• Date: Mon, 11 Aug 2025 17:36:37 GMT
• Title: Evaluating structural uncertainty in accelerated MRI: are voxelwise measures useful surrogates?
• Authors: Luca L. C. Trautmann, Peter A. Wijeratne, Itamar Ronen, Ivor J. A. Simpson,
• Abstract summary: We show that voxel level uncertainty does not provide insight into morphological uncertainty.<n>We use segmentation as a clinically-relevant downstream task and deploy ensembles of reconstruction modes to measure uncertainty in the reconstructions.
• Score: 1.8124328823188356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Introducing accelerated reconstruction algorithms into clinical settings requires measures of uncertainty quantification that accurately assess the relevant uncertainty introduced by the reconstruction algorithm. Many currently deployed approaches quantifying uncertainty by focusing on measuring the variability in voxelwise intensity variation. Although these provide interpretable maps, they lack a structural interpretation and do not show a clear relationship to how the data will be analysed subsequently. In this work we show that voxel level uncertainty does not provide insight into morphological uncertainty. To do so, we use segmentation as a clinically-relevant downstream task and deploy ensembles of reconstruction modes to measure uncertainty in the reconstructions. We show that variability and bias in the morphological structures are present and within-ensemble variability cannot be predicted well with uncertainty measured only by voxel intensity variations.

Related papers

• Benchmarking Uncertainty and its Disentanglement in multi-label Chest X-Ray Classification [11.21639536740362]
  We evaluate 13 uncertainty quantification methods for convolutional (ResNet) and transformer-based (Vision Transformer) architectures.<n>We extend Evidential Deep Learning, HetClass NNs, and Deep Deterministic Uncertainty to the multi-label setting.
  arXiv  Detail & Related papers  (2025-08-06T13:58:17Z)
• Mind the Detail: Uncovering Clinically Relevant Image Details in Accelerated MRI with Semantically Diverse Reconstructions [29.673866614937392]
  We show that existing techniques can fail to reconstruct small and rare pathologies, thus leading to potentially wrong diagnosis decisions (false negatives)<n>We propose Semantically Diverse Reconstructions'' (SDR), a method which generates novel reconstructions with enhanced semantic variability while all of them are fully consistent with the measured data.<n>We show that SDR significantly reduces the chance of false-negative diagnoses (higher recall) and improves mean average precision compared to the original reconstructions.
  arXiv  Detail & Related papers  (2025-07-01T11:11:09Z)
• 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.<n> PUQ employs a two-stage reconstruction and parameter fitting framework, where phase-wise uncertainty is estimated during reconstruction and utilized in the fitting stage.<n>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)
• ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning [51.26601171361753]
  We propose ContextMRI, a text-conditioned diffusion model for MRI that integrates granular metadata into the reconstruction process.<n>We show that increasing the fidelity of metadata, ranging from slice location and contrast to patient age, sex, and pathology, systematically boosts reconstruction performance.
  arXiv  Detail & Related papers  (2025-01-08T05:15:43Z)
• 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)
• Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation [8.64414399041931]
  Uncertainty quantification (UQ) is an indicator of the trustworthiness of automated deep-learning (DL) tools in the context of white matter lesion (WML) segmentation. We develop measures for quantifying uncertainty at lesion and patient scales, derived from structural prediction discrepancies. The results from a multi-centric MRI dataset of 444 patients demonstrate that our proposed measures more effectively capture model errors at the lesion and patient scales.
  arXiv  Detail & Related papers  (2023-11-15T13:04:57Z)
• On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
  Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input. DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases. We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
  arXiv  Detail & Related papers  (2023-06-23T03:09:03Z)
• CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI Reconstruction [25.078280843551322]
  We introduce a self-supervised pretraining procedure using contrastive learning to improve the accuracy of undersampled MRI reconstruction. Our experiments demonstrate improved reconstruction accuracy across a range of acceleration factors and datasets.
  arXiv  Detail & Related papers  (2023-06-01T10:29:58Z)
• PixCUE -- Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification [0.0]
  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.
  arXiv  Detail & Related papers  (2023-02-28T22:26:18Z)
• Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty [57.023423137202485]
  Concerns regarding the reliability of medical image segmentation persist among clinicians.<n>We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks.<n>By leveraging subjective logic theory, we explicitly model probability and uncertainty for medical image segmentation.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosis [9.295643448425182]
  This paper focuses on the uncertainty estimation for white matter lesions (WML) segmentation in magnetic resonance imaging (MRI) On one side, voxel-scale segmentation errors cause the erroneous delineation of the lesions; on the other side, lesion-scale detection errors lead to wrong lesion counts. This work aims to compare the ability of different voxel- and lesion-scale uncertainty measures to capture errors related to segmentation and lesion detection, respectively.
  arXiv  Detail & Related papers  (2022-11-09T11:53:29Z)
• Stable Deep MRI Reconstruction using Generative Priors [13.400444194036101]
  We propose a novel deep neural network based regularizer which is trained in a generative setting on reference magnitude images only. The results demonstrate competitive performance, on par with state-of-the-art end-to-end deep learning methods.
  arXiv  Detail & Related papers  (2022-10-25T08:34:29Z)
• 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)
• Negligible effect of brain MRI data preprocessing for tumor segmentation [36.89606202543839]
  We conduct experiments on three publicly available datasets and evaluate the effect of different preprocessing steps in deep neural networks. Our results demonstrate that most popular standardization steps add no value to the network performance. We suggest that image intensity normalization approaches do not contribute to model accuracy because of the reduction of signal variance with image standardization.
  arXiv  Detail & Related papers  (2022-04-11T17:29:36Z)
• The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties [3.46329153611365]
  In this paper we demonstrate the efficacy of a physics-informed, uncertainty-aware, segmentation network. We show that the proposed approach also accurately extrapolates to out-of-distribution sequence samples. We demonstrate a significant improvement in terms of coefficients of variation, backed by uncertainty based volumetric validation.
  arXiv  Detail & Related papers  (2021-11-04T11:52:49Z)
• 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)
• DEUP: Direct Epistemic Uncertainty Prediction [56.087230230128185]
  Epistemic uncertainty is part of out-of-sample prediction error due to the lack of knowledge of the learner. We propose a principled approach for directly estimating epistemic uncertainty by learning to predict generalization error and subtracting an estimate of aleatoric uncertainty.
  arXiv  Detail & Related papers  (2021-02-16T23:50:35Z)
• 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)
• Approaching Neural Network Uncertainty Realism [53.308409014122816]
  Quantifying or at least upper-bounding uncertainties is vital for safety-critical systems such as autonomous vehicles. We evaluate uncertainty realism -- a strict quality criterion -- with a Mahalanobis distance-based statistical test. We adopt it to the automotive domain and show that it significantly improves uncertainty realism compared to a plain encoder-decoder model.
  arXiv  Detail & Related papers  (2021-01-08T11:56:12Z)
• Learnable Uncertainty under Laplace Approximations [65.24701908364383]
  We develop a formalism to explicitly "train" the uncertainty in a decoupled way to the prediction itself. We show that such units can be trained via an uncertainty-aware objective, improving standard Laplace approximations' performance.
  arXiv  Detail & Related papers  (2020-10-06T13:43:33Z)
• 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.