用于潜在乳腺MRI虚拟对比度增强的预测增强校准
Predictive Enhancement Calibration for Latent Breast MRI Virtual Contrast Enhancement
- The First Affiliated Hospital of Chongqing Medical University（重庆医科大学附属第一医院）
- Chongqing Municipal Health Commission（重庆市卫生健康委员会）
- Chongqing Translational Medicine Center（重庆市转化医学中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对潜在乳腺MRI虚拟对比度增强中自然图像自动编码器与MRI强度尺度冲突的问题，提出PEC方法并结合FLUX模型，在MAMA100队列中提升了所有八个点估计，MSE和LPIPS表现最优。
AI中文摘要：
虚拟对比度增强（VCE）从预对比采集图像合成增强的乳腺MR图像。现代潜在生成器提供强大的图像先验，但它们的有界自然图像自动编码器与MRI的非标准强度尺度存在冲突。我们表明，上限会在生成前改变放射组学保真度，而独立缩放源和目标会产生坐标不一致。我们提出预测增强校准（PEC），该方法在训练期间将每对表示为共享的、案例自适应的坐标，并在推理时从预对比图像预测其不可用的上限端点。我们通过参数高效参考条件将PEC与预训练的FLUX潜在流变换器集成。目标往返首先隔离生成前的表示损失；近匹配的条件模型在可比的训练预算和骨干设置下，比较PEC与固定宽度和单独坐标的性能。在固定的内部MAMA100开发队列中，PEC在这种仅源VCE设置中改善了所有八个点估计，配对证据在MSE和LPIPS方面最强。代码：this https URL
英文摘要：
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
