Predictive Enhancement Calibration for Latent Breast MRI Virtual Contrast Enhancement
This work improves virtual contrast enhancement for breast MRI, a domain-specific application, by addressing a known bottleneck in latent generators.
The paper addresses the issue of latent generators' bounded autoencoders conflicting with MRI intensity scales in virtual contrast enhancement, proposing Predictive Enhancement Calibration (PEC) to align coordinates and improve fidelity. PEC improves all eight point estimates on the MAMA100 cohort, with strongest paired evidence for MSE and LPIPS.
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