Balancing Fidelity, Utility, and Privacy in Synthetic Cardiac MRI Generation: A Comparative Study

• URL: http://arxiv.org/abs/2603.04340v1
• Date: Wed, 04 Mar 2026 17:59:00 GMT
• Title: Balancing Fidelity, Utility, and Privacy in Synthetic Cardiac MRI Generation: A Comparative Study
• Authors: Madhura Edirisooriya, Dasuni Kawya, Ishan Kumarasinghe, Isuri Devindi, Mary M. Maleckar, Roshan Ragel, Isuru Nawinne, Vajira Thambawita,
• Abstract summary: Deep learning in cardiac MRI (CMR) is fundamentally constrained by both data scarcity and privacy regulations.<n>This study benchmarks three generative architectures: Denoising Diffusion Probabilistic Models (DDPM), Latent Diffusion Models (LDM), and Flow Matching (FM)<n>We evaluate generated data across three critical axes: fidelity, utility, and privacy.
• Score: 0.43217519111088665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning in cardiac MRI (CMR) is fundamentally constrained by both data scarcity and privacy regulations. This study systematically benchmarks three generative architectures: Denoising Diffusion Probabilistic Models (DDPM), Latent Diffusion Models (LDM), and Flow Matching (FM) for synthetic CMR generation. Utilizing a two-stage pipeline where anatomical masks condition image synthesis, we evaluate generated data across three critical axes: fidelity, utility, and privacy. Our results show that diffusion-based models, particularly DDPM, provide the most effective balance between downstream segmentation utility, image fidelity, and privacy preservation under limited-data conditions, while FM demonstrates promising privacy characteristics with slightly lower task-level performance. These findings quantify the trade-offs between cross-domain generalization and patient confidentiality, establishing a framework for safe and effective synthetic data augmentation in medical imaging.

Related papers

• Revisiting Global Token Mixing in Task-Dependent MRI Restoration: Insights from Minimal Gated CNN Baselines [43.505945728449774]
  Global token mixing has become a popular model design choice for MRI restoration.<n>We ask whether global token mixing is actually beneficial in each individual task across three representative settings.<n>For accelerated MRI reconstruction, the minimal unrolled gated-CNN baseline is already highly competitive.<n>For super-resolution, where low-frequency k-space data are largely preserved by the controlled low-pass degradation, local gated models remain competitive.<n>For denoising with pronounced spatially heteroscedastic noise, token-mixing models achieve the strongest overall performance.
  arXiv  Detail & Related papers  (2026-03-02T04:57:52Z)
• Pattern-Aware Diffusion Synthesis of fMRI/dMRI with Tissue and Microstructural Refinement [34.55493442995441]
  We propose PDS, a pattern-aware dual-modal 3D diffusion framework for cross-modality learning.<n>We also introduce a tissue refinement network integrated with a efficient microstructure refinement to maintain structural fidelity and fine details.<n>PDS achieves state-of-the-art results, with PSNR/SSIM scores of 29.83 dB/90.84% for fMRI synthesis and 30.00 dB/77.55% for dMRI synthesis.
  arXiv  Detail & Related papers  (2025-11-07T03:51:00Z)
• Adapting HFMCA to Graph Data: Self-Supervised Learning for Generalizable fMRI Representations [57.054499278843856]
  Functional magnetic resonance imaging (fMRI) analysis faces significant challenges due to limited dataset sizes and domain variability between studies.<n>Traditional self-supervised learning methods inspired by computer vision often rely on positive and negative sample pairs.<n>We propose adapting a recently developed Hierarchical Functional Maximal Correlation Algorithm (HFMCA) to graph-structured fMRI data.
  arXiv  Detail & Related papers  (2025-10-05T12:35:01Z)
• On the MIA Vulnerability Gap Between Private GANs and Diffusion Models [51.53790101362898]
  Generative Adversarial Networks (GANs) and diffusion models have emerged as leading approaches for high-quality image synthesis.<n>We present the first unified theoretical and empirical analysis of the privacy risks faced by differentially private generative models.
  arXiv  Detail & Related papers  (2025-09-03T14:18:22Z)
• Diffusing the Blind Spot: Uterine MRI Synthesis with Diffusion Models [7.262119921589195]
  We introduce a novel diffusion-based framework for uterine MRI synthesis.<n>Our approach generates anatomically coherent, high fidelity synthetic images that closely mimic real scans.<n>A blinded expert evaluation validates the clinical realism of our synthetic images.
  arXiv  Detail & Related papers  (2025-08-11T12:18:23Z)
• CoCoLIT: ControlNet-Conditioned Latent Image Translation for MRI to Amyloid PET Synthesis [2.333160549379721]
  High dimensionality and structural complexity of 3D neuroimaging data pose challenges for MRI-to-PET translation.<n>We present CoCoLIT, a diffusion-based latent generative framework that incorporates three main innovations.<n>We evaluate CoCoLIT's performance on publicly available datasets and find that our model significantly outperforms state-of-the-art methods on both image-based and amyloid-related metrics.
  arXiv  Detail & Related papers  (2025-08-02T09:58:30Z)
• FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis [6.475175425060296]
  We propose a novel unified synthesis model, the Frequency-guided and Coarse-to-fine Unified Diffusion Model (FgC2F-UDiff)
  arXiv  Detail & Related papers  (2025-01-07T04:42:45Z)
• Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis [43.82741134285203]
  We present a physics-informed generative model capable of synthesizing a variable number of brain MRI modalities. Our approach utilizes latent diffusion models and a two-step generative process. Experiments demonstrate the efficacy of this approach in generating unseen MR contrasts and preserving physical plausibility.
  arXiv  Detail & Related papers  (2024-09-20T14:21:34Z)
• A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds [49.34500499203579]
  We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics. We generate high-quality synthetic fMRI data based on user-supplied demographics.
  arXiv  Detail & Related papers  (2024-05-13T17:49:20Z)
• Fairness-Aware Data Augmentation for Cardiac MRI using Text-Conditioned Diffusion Models [1.6581402323174208]
  We propose a method to alleviate imbalances inherent in datasets through the generation of synthetic data.<n>We adopt ControlNet based on a denoising diffusion probabilistic model to condition on text assembled from patient metadata and cardiac geometry.<n>Our experiments demonstrate the effectiveness of the proposed approach in mitigating dataset imbalances.
  arXiv  Detail & Related papers  (2024-03-28T15:41:43Z)
• Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis [55.03872260158717]
  Resting-state MRI functional (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis. Many methods have been proposed to reduce fMRI heterogeneity between source and target domains. But acquiring source data is challenging due to concerns and/or data storage burdens in multi-site studies. We design a source-free collaborative domain adaptation framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible.
  arXiv  Detail & Related papers  (2023-08-24T01:30:18Z)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)

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.