Distributional Deep Learning for Super-Resolution of 4D Flow MRI under Domain Shift

• URL: http://arxiv.org/abs/2602.15167v1
• Date: Mon, 16 Feb 2026 20:11:42 GMT
• Title: Distributional Deep Learning for Super-Resolution of 4D Flow MRI under Domain Shift
• Authors: Xiaoyi Wen, Fei Jiang,
• Abstract summary: We develop a distributional deep learning framework for enhancing the resolution of 4D Flow MRI (4DF)<n>This is a novel imaging modality that captures hemodynamic flow velocity and clinically relevant metrics such as vessel wall stress.<n>We derive the theoretical properties of our distributional estimators and demonstrate that our framework significantly outperforms traditional deep learning approaches through real data applications.
• Score: 7.497091354189351
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Super-resolution is widely used in medical imaging to enhance low-quality data, reducing scan time and improving abnormality detection. Conventional super-resolution approaches typically rely on paired datasets of downsampled and original high resolution images, training models to reconstruct high resolution images from their artificially degraded counterparts. However, in real-world clinical settings, low resolution data often arise from acquisition mechanisms that differ significantly from simple downsampling. As a result, these inputs may lie outside the domain of the training data, leading to poor model generalization due to domain shift. To address this limitation, we propose a distributional deep learning framework that improves model robustness and domain generalization. We develop this approch for enhancing the resolution of 4D Flow MRI (4DF). This is a novel imaging modality that captures hemodynamic flow velocity and clinically relevant metrics such as vessel wall stress. These metrics are critical for assessing aneurysm rupture risk. Our model is initially trained on high resolution computational fluid dynamics (CFD) simulations and their downsampled counterparts. It is then fine-tuned on a small, harmonized dataset of paired 4D Flow MRI and CFD samples. We derive the theoretical properties of our distributional estimators and demonstrate that our framework significantly outperforms traditional deep learning approaches through real data applications. This highlights the effectiveness of distributional learning in addressing domain shift and improving super-resolution performance in clinically realistic scenarios.

Related papers

• DSA-SRGS: Super-Resolution Gaussian Splatting for Dynamic Sparse-View DSA Reconstruction [67.42242016220122]
  Digital subtraction angiography is a key imaging technique for the auxiliary diagnosis and treatment of cerebrovascular diseases.<n>Recent advancements in gaussian splatting and dynamic neural representations have enabled robust 3D vessel reconstruction from sparse dynamic inputs.<n>This paper proposes DSA-SRGS, the first super-resolution gaussian splatting framework for dynamic sparse-view DSA reconstruction.
  arXiv  Detail & Related papers  (2026-03-05T03:41:08Z)
• Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data [11.174208209806073]
  Undersampling strategies can accelerate image acquisition, but they often result in image artifacts and degraded quality.<n>Recent diffusion models have shown promise for reconstructing high-fidelity images from undersampled data by learning powerful image priors.<n>We introduce a conditional denoising diffusion framework with iterative data-consistency correction.
  arXiv  Detail & Related papers  (2025-10-07T18:01:08Z)
• Deep learning for temporal super-resolution 4D Flow MRI [34.90138772411514]
  The aim of this study was to implement and evaluate a residual network for temporal super-resolution 4D Flow MRI.<n>Training and testing were performed using synthetic 4D Flow MRI data originating from patient-specific in-silico models, as well as using in-vivo datasets.<n>Our results highlight the potential of utilizing data-driven neural networks for temporal super-resolution 4D Flow MRI.
  arXiv  Detail & Related papers  (2025-01-15T13:01:47Z)
• Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis [55.959002385347645]
  Latent Drifting enables diffusion models to be conditioned for medical images fitted for the complex task of counterfactual image generation.<n>We evaluate our method on three public longitudinal benchmark datasets of brain MRI and chest X-rays for counterfactual image generation.
  arXiv  Detail & Related papers  (2024-12-30T01:59:34Z)
• A Flow-based Truncated Denoising Diffusion Model for Super-resolution Magnetic Resonance Spectroscopic Imaging [34.32290273033808]
  This work introduces a Flow-based Truncated Denoising Diffusion Model for super-resolution MRSI. It shortens the diffusion process by truncating the diffusion chain, and the truncated steps are estimated using a normalizing flow-based network. We demonstrate that FTDDM outperforms existing generative models while speeding up the sampling process by over 9-fold.
  arXiv  Detail & Related papers  (2024-10-25T03:42:35Z)
• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  We propose a unified MRI reconstruction model robust to various measurement undersampling patterns and image resolutions.<n>Our model improves SSIM by 11% and PSNR by 4 dB over a state-of-the-art CNN (End-to-End VarNet) with 600$times$ faster inference than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models [69.9178140563928]
  Colonoscopy analysis is essential for assisting clinical diagnosis and treatment. The scarcity of annotated data limits the effectiveness and generalization of existing methods. We propose an Adaptive Refinement Semantic Diffusion Model (ArSDM) to generate colonoscopy images that benefit the downstream tasks.
  arXiv  Detail & Related papers  (2023-09-03T07:55:46Z)
• 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)
• Ultrasound Signal Processing: From Models to Deep Learning [64.56774869055826]
  Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions. Deep learning based methods, which are optimized in a data-driven fashion, have gained popularity. A relatively new paradigm combines the power of the two: leveraging data-driven deep learning, as well as exploiting domain knowledge.
  arXiv  Detail & Related papers  (2022-04-09T13:04:36Z)
• A comparative study of various Deep Learning techniques for spatio-temporal Super-Resolution reconstruction of Forced Isotropic Turbulent flows [0.45935798913942893]
  This study performs super-resolution analysis on turbulent flow fields spatially and temporally using various state-of-the-art machine learning techniques. The dataset used for this study is extracted from the 'isotropic 1024 coarse' dataset which is a part of Johns Hopkins Turbulence databases.
  arXiv  Detail & Related papers  (2021-07-07T17:16:55Z)

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.