Predictive Enhancement Calibration for Latent Breast MRI Virtual Contrast Enhancement

2026-08-04

AI Summary

This work addresses radiomic distortion and spatial misalignment in synthetic contrast-enhanced breast MRI, which arise from generator intensity upper-bound constraints and independent intensity scaling between source and target images. To resolve these issues, the authors propose a Predictive Enhancement Calibration (PEC) method that establishes a case-adaptive shared coordinate system and predicts the missing enhancement upper bound directly from pre-contrast images during inference. PEC leverages a pretrained FLUX latent flow model for efficient conditional generation, incorporating parameter-efficient reference conditioning, target round-trip reconstruction, and a unified coordinate strategy within a single training framework. Evaluated on the MAMA100 cohort under a source-only setting, PEC significantly improves all eight assessment metrics, with the most pronounced gains observed in MSE and LPIPS.

Abstract

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.\noindent\textbf{Code:} https://github.com/tanlei0/pec-breast-mri-vce

Problem

• Virtual Contrast Enhancement
• Breast MRI
• Latent Generative Models
• Radiomic Fidelity
• Intensity Scaling

Innovation

• Virtual Contrast Enhancement
• Predictive Enhancement Calibration
• Latent Diffusion Model
• Breast MRI
• Radiomic Fidelity