用CTA生成缺血性卒中灌注图，预测神经功能缺损。
Deep generative computed perfusion-deficit mapping of ischaemic stroke
- 基于深度生成模型从CTA推断灌注缺陷区域。
- 在1393例患者中复现已知病变-症状关联，发现新神经依赖关系。
- 仅用超急性期影像即可建模功能解剖关系，适合临床早期干预研究。
局灶性缺血性卒中的功能缺损源于关键血管闭塞下游的灌注障碍。尽管传统上以组织损伤来预测临床缺损，但上游灌注紊乱模式可提供更早的定位信号。本研究利用广泛应用于临床的常规CT血管造影（CTA）数据，分析1393例急性缺血性卒中患者的计算灌注图，采用深度生成推断方法定位NIHSS各子项的神经基础。结果表明，该方法在不依赖病灶信息的情况下复现了已知的病变-症状关联，并揭示了新的神经依赖关系。高保真的解剖映射表明，基于超急性期CTA的计算灌注图具有重要的临床与科研价值，可在干预前窗口内构建高度表达的功能解剖模型。
原文摘要 · Abstract (English)
Focal deficits in ischaemic stroke result from impaired perfusion downstream of a critical vascular occlusion. While parenchymal lesions are traditionally used to predict clinical deficits, the underlying pattern of disrupted perfusion provides information upstream of the lesion, potentially yielding earlier predictive and localizing signals. Such perfusion maps can be derived from routine CT angiography (CTA) widely deployed in clinical practice. Analysing computed perfusion maps from 1,393 CTA-imaged-patients with acute ischaemic stroke, we use deep generative inference to localise neural substrates of NIHSS sub-scores. We show that our approach replicates known lesion-deficit relations without knowledge of the lesion itself and reveals novel neural dependents. The high achieved anatomical fidelity suggests acute CTA-derived computed perfusion maps may be of substantial clinical-and-scientific value in rich phenotyping of acute stroke. Using only hyperacute imaging, deep generative inference could power highly expressive models of functional anatomical relations in ischaemic stroke within the pre-interventional window.
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