🤖 AI Summary
研究通过直接回归与流匹配方法预测脑MRI中造影剂增强情况，以减少多次扫描需求。直接回归法在预测准确性及成本上优于流匹配法。
📝 Abstract
Intrathecal contrast-enhanced MRI tracks how a cerebrospinal-fluid tracer spreads through the brain, but requires repeated scans over 24--48\,h. Forecasting enhancement from a pre-contrast scan could one day help select patients for intrathecal drug treatment and plan their dose. We ask how much of the enhancement at a given time can be predicted from the pre-contrast scan and the elapsed time alone. Training on 104 patients, we compared direct image-to-image regression (I2I) with conditional flow matching (CFM) using the same 3D U-Net and protocol, and tested on 23 held-out patients and 51 external patients with normal pressure hydrocephalus. All models were measured against simply copying the pre-contrast scan. I2I removed about 60\% of this copying error internally and 25\% externally, outperformed CFM on both test sets (mean absolute error 0.036 vs.\ 0.058 and 0.056 vs.\ 0.063), and predicted each volume in a single forward pass, whereas CFM ran its network ten times. CFM uncertainty located errors but was poorly calibrated. Much of tracer enhancement is thus predictable from anatomy and timing, and direct regression is the more accurate and cheaper choice at this data scale.