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Related Experiment Videos

Modelling Stochastic Sensor Noise via Mask-Conditioned Diffusion for Data Augmentation in Low-SNR LGE-CMR.

Sofia Fernandes1,2, Carla Barros3, Adriano Pinto3

  • 1Department of Industrial Electronics, School of Engineering, University of Minho, Campus de Azurém, 4800-058 Guimarães, Portugal.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

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This summary is machine-generated.

We developed a diffusion model to generate synthetic cardiac MRI images, improving automated fibrosis detection in low-quality scans. This method enhances scar segmentation accuracy, outperforming traditional generative models.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Cardiovascular Disease Research

Background:

  • Late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) is crucial for quantifying myocardial fibrosis.
  • Automated scar segmentation in LGE-CMR is hindered by limited expert annotations and poor image quality (low signal-to-noise ratio, artifacts).
  • Existing methods struggle with the inherent noise and texture characteristics of low-SNR LGE imaging.

Purpose of the Study:

  • To investigate if a diffusion model can replicate LGE-CMR texture characteristics.
  • To determine if synthetic LGE-CMR data generated by diffusion models can improve automated fibrosis segmentation in data-limited scenarios.
  • To compare the effectiveness of diffusion-based augmentation against Generative Adversarial Network (GAN)-based augmentation.
Keywords:
LGE-CMRdata augmentationdiffusion modelsmyocardial fibrosissignal reconstructionstochastic noise modelling

Related Experiment Videos

Main Methods:

  • Introduced a mask-conditioned denoising diffusion probabilistic model (DDPM) to synthesize 2D LGE-CMR slices from label maps.
  • Employed synthetic images for training-set augmentation in the nnU-Net v2 segmentation framework.
  • Benchmarked against exemplar-guided image synthesis (CoCosNet-v2) and evaluated noise-fidelity characteristics.

Main Results:

  • Augmenting a real dataset with 300 diffusion-generated cases significantly improved scar Dice coefficient (+56.7%) and recall on a held-out test set.
  • Diffusion-based augmentation consistently outperformed GAN-based augmentation for comparable training budgets.
  • The DDPM demonstrated superior fidelity in reproducing scanner-specific noise statistics compared to the GAN baseline.

Conclusions:

  • Diffusion models can effectively generate realistic LGE-CMR images, addressing challenges of limited annotations and low image quality.
  • Synthetic data from DDPMs substantially enhances automated fibrosis segmentation performance, particularly in annotation-limited settings.
  • The DDPM offers a more mechanistically sound approach to data augmentation for LGE-CMR analysis compared to GANs.