用生成模型预测脑胶质瘤增强影像，提升诊断可靠性。
Conditional Generative Models for Contrast-Enhanced Synthesis of T1w and T1 Maps in Brain MRI
- 采用条件扩散与流匹配模型预测钆剂增强效果。
- 定量T1扫描比加权T1扫描分割更准。
- 适合需要精准肿瘤区域划分的临床研究者。
钆基对比剂（GBCAs）在神经放射学中对肿瘤诊断至关重要。基于胶质母细胞瘤患者注射钆剂前后的脑部MRI数据，本文利用神经网络解决增强预测问题，并提出两项新贡献：首先，研究生成模型（尤其是条件扩散与流匹配）在虚拟增强中的不确定性量化潜力；其次，比较定量MRI获取的T1扫描与传统T1加权扫描的性能差异。相较于灰度值不可比的T1加权扫描，定量T1扫描具有物理意义明确、体素值可比的优势。为评估两种模态在灰度尺度不兼容下的预测表现，本文采用Dice和Jaccard分数评价对比增强区域的分割精度。实验表明，使用T1扫描的模型在分割效果上优于T1加权扫描。
原文摘要 · Abstract (English)
Contrast enhancement by Gadolinium-based contrast agents (GBCAs) is a vital tool for tumor diagnosis in neuroradiology. Based on brain MRI scans of glioblastoma before and after Gadolinium administration, we address enhancement prediction by neural networks with two new contributions. Firstly, we study the potential of generative models, more precisely conditional diffusion and flow matching, for uncertainty quantification in virtual enhancement. Secondly, we examine the performance of T1 scans from quantitive MRI versus T1-weighted scans. In contrast to T1-weighted scans, these scans have the advantage of a physically meaningful and thereby comparable voxel range. To compare network prediction performance of these two modalities with incompatible gray-value scales, we propose to evaluate segmentations of contrast-enhanced regions of interest using Dice and Jaccard scores. Across models, we observe better segmentations with T1 scans than with T1-weighted scans.
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