IEEE Trans Med Imaging. 2020 Dec;39(12):4113-4123. doi: 10.1109/TMI.2020.3012932. Epub 2020 Nov 30.
With each heartbeat, periodic variations in arterial blood pressure are transmitted along the vasculature, resulting in localized deformations of the arterial wall and its surrounding tissue. Quantification of such motions may help understand various cerebrovascular conditions, yet it has proven technically challenging thus far. We introduce a new image processing algorithm called amplified Flow (aFlow) which allows to study the coupled brain-blood flow motion by combining the amplification of cine and 4D flow MRI. By incorporating a modal analysis technique known as dynamic mode decomposition into the algorithm, aFlow is able to capture the characteristics of transient events present in the brain and arterial wall deformation. Validating aFlow, we tested it on phantom simulations mimicking arterial walls motion and observed that aFlow displays almost twice higher SNR than its predecessor amplified MRI (aMRI). We then applied aFlow to 4D flow and cine MRI datasets of 5 healthy subjects, finding high correlations between blood flow velocity and tissue deformation in selected brain regions, with correlation values r = 0.61 , 0.59, 0.52 for the pons, frontal and occipital lobe ( ). Finally, we explored the potential diagnostic applicability of aFlow by studying intracranial aneurysm dynamics, which seems to be indicative of rupture risk. In two patients, aFlow successfully visualized the imperceptible aneurysm wall motion, additionally quantifying the increase in the high frequency wall displacement after a one-year follow-up period (20%, 76%). These preliminary data suggest that aFlow may provide a novel imaging biomarker for the assessment of aneurysms evolution, with important potential diagnostic implications.
随着每次心跳,动脉血压的周期性变化沿着脉管系统传递,导致动脉壁及其周围组织的局部变形。对这种运动的定量分析可能有助于了解各种脑血管状况,但迄今为止,这在技术上一直具有挑战性。我们引入了一种新的图像处理算法,称为放大流(aFlow),该算法通过结合电影和 4D 流动 MRI 的放大,允许研究大脑与血流运动的耦合。通过将一种称为动态模态分解的模态分析技术纳入算法中,aFlow 能够捕获大脑和动脉壁变形中存在的瞬态事件的特征。为了验证 aFlow,我们在模拟动脉壁运动的体模模拟中对其进行了测试,结果表明 aFlow 的 SNR 比其前身放大 MRI(aMRI)高近两倍。然后,我们将 aFlow 应用于 5 名健康受试者的 4D 流动和电影 MRI 数据集,在选定的大脑区域中发现血流速度与组织变形之间具有很高的相关性,在脑桥、额叶和枕叶中相关系数 r = 0.61 ,0.59 ,0.52 ( )。最后,我们通过研究颅内动脉瘤动力学来探索 aFlow 的潜在诊断适用性,这似乎表明了破裂风险。在两名患者中,aFlow 成功地可视化了不可察觉的动脉瘤壁运动,并且在一年的随访期后(20%,76%),还定量地测量了高频壁位移的增加。这些初步数据表明,aFlow 可能为评估动脉瘤演变提供一种新的成像生物标志物,具有重要的潜在诊断意义。