Knowing When Not to Trust: 3D Flow Matching with Self-Flagging Uncertainty for Post-Surgical Cerebral Blood Flow Prediction

Predicting post-operative cerebral blood flow (CBF) from pre-operative imaging could support surgical planning for Moyamoya patients undergoing revascularization. We present two results built on that observation. First, recast as a data-to-data bridge, conditional flow matching clears the identity baseline for the first time, cutting brain-masked MAE 15.7% below it with a third of the sampling steps and beating it for 95–100% of held-out subjects on each of the four brain-masked metrics. Second, the same trained model doubles as its own trust signal: the score of its velocity field is available in closed form, so a Langevin-corrected SDE samples the model's own probability path and yields a per-voxel uncertainty map at no cost in accuracy and with no tuned noise scale. The map orders error monotonically, its most uncertain decile carrying $2.3\times$ the error of its most confident, and the ordering holds for 40 of 42 held-out subjects. Aggregated to one number per patient it is far weaker, since a subject's error mostly reflects how much that subject changed. We therefore deliver the map rather than a scalar: decision support that shows where a prediction should not be trusted, not an autonomous predictor. All experiments are reproducible and code will be released upon acceptance of paper.