Cerebrovascular disease is a leading cause of death globally. Prevention and early intervention are known to be the most effective forms of its management. Non-invasive imaging methods hold great promises for early stratification, but at present lack the sensitivity for personalized prognosis. Resting-state functional magnetic resonance imaging (rs-fMRI), a powerful tool previously used for mapping neural activity, is available in most hospitals. Here we show that rs-fMRI can be used to map cerebral hemodynamic function and delineate impairment. By exploiting time variations in breathing pattern during rs-fMRI, deep learning enables reproducible mapping of cerebrovascular reactivity (CVR) and bolus arrive time (BAT) of the human brain using resting-state CO2 fluctuations as a natural 'contrast media'. The deep-learning network was trained with CVR and BAT maps obtained with a reference method of CO2-inhalation MRI, which included data from young and older healthy subjects and patients with Moyamoya disease and brain tumors. We demonstrate the performance of deep-learning cerebrovascular mapping in the detection of vascular abnormalities, evaluation of revascularization effects, and vascular alterations in normal aging. In addition, cerebrovascular maps obtained with the proposed method exhibited excellent reproducibility in both healthy volunteers and stroke patients. Deep-learning resting-state vascular imaging has the potential to become a useful tool in clinical cerebrovascular imaging.
翻译:脑血管疾病是全球范围内死亡的一个主要原因。 预防和早期干预是其管理的最有效形式。 非侵入成像方法为早期分层提供了巨大的希望,但目前缺乏对个性化预感的敏感性。 大部分医院都提供恢复状态功能性磁共振成像(rs-fMRI),这是以前用于绘制神经活动图的强大工具。 我们在这里显示rs-fMRI可以用来绘制脑血管运动功能和损伤图解。 通过利用rs-fMRI的呼吸模式中的时间变化,深层次学习能够复制脑血管反应(CVR)和肉丸的临床潜在映射,但目前缺乏对个性化预测的敏感性。 使用正态CO2波动作为自然“连续媒体”的修复状态功能性磁共振动成像(rs-fMRI)成像(r-fMRI)成像(r-fMRI)成像术和BAT地图的参考方法,其中包括来自年轻和老年健康对象和患有Moyamology疾病和脑肿瘤肿瘤的病人的数据。 我们展示了在深度血管变动图解中的机变动中,在深度变动中,在深度变造图中显示了中,我们变动的大脑的大脑的大脑变动中增加了的机方法分析结果中增加了的演化结果的演化结果的演进效果。