mlsanityData to 14 Oct 2026

Predictive Enhancement Calibration for Latent Breast MRI Virtual Contrast Enhancement

arXiv 2608.03612 ↗First posted 4 Aug 2026eess.IV cs.CVcited by 0

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Abstract (arXiv)

Virtual contrast enhancement (VCE) synthesizes enhanced breast MR images from pre-contrast acquisitions. Modern latent generators offer strong image priors, but their bounded natural-image autoencoders conflict with the non-canonical intensity scale of MRI. We show that the upper bound can alter radiomic fidelity before generation, while scaling source and target independently creates a coordinate inconsistency. We propose Predictive Enhancement Calibration (PEC), which represents each pair in a shared, case-adaptive coordinate during training and predicts its unavailable upper endpoint from the pre-contrast image at inference. We integrate PEC with a pretrained FLUX latent flow transformer via parameter-efficient reference conditioning. Target round trips first isolate representation loss before generation; near-matched conditional models then compare PEC with fixed-wide and separate coordinates under comparable training budgets and backbone settings. On the fixed internal MAMA100 development cohort, PEC improves all eight point estimates in this source-only VCE setting, with paired evidence strongest for MSE and LPIPS.noindentCode: https://github.com/tanlei0/pec-breast-mri-vce

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References · 14

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
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  3. 2022Scalable Diffusion Models with Transformerscited by 6,054
  4. 2022Flow Matching for Generative Modelingcited by 5,702
  5. 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,181
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  8. 2016Image biomarker standardisation initiativecited by 63
  9. 2018Evaluating the Impact of Intensity Normalization on MR Image Synthesiscited by 26
  10. 2025MedVAE: Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoderscited by 22
  11. 2024Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasetscited by 19
  12. 2024Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Modelscited by 11
2 more
  1. 2024Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networkscited by 6
  2. 2025Comparing Conditional Diffusion Models for Synthesizing Contrast-Enhanced Breast MRI from Pre-Contrast Imagescited by 3

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