Galazis Christoforos, Shepperd Samuel, Brouwer Emma J P, Queiros Sandro, Alskaf Ebraham, Anjari Mustafa, Chiribiri Amedeo, Lee Jack, Bharath Anil A, Varela Marta
IEEE Trans Med Imaging. 2025 May;44(5):2056-2067. doi: 10.1109/TMI.2025.3526364. Epub 2025 May 2.
The functional analysis of the left atrium (LA) is important for evaluating cardiac health and understanding diseases like atrial fibrillation. Cine MRI is ideally placed for the detailed 3D characterization of LA motion and deformation but is lacking appropriate acquisition and analysis tools. Here, we propose tools for the Analysis of Left Atrial Displacements and DeformatIons using online learning neural Networks (Aladdin) and present a technical feasibility study on how Aladdin can characterize 3D LA function globally and regionally. Aladdin includes an online segmentation and image registration network, and a strain calculation pipeline tailored to the LA. We create maps of LA Displacement Vector Field (DVF) magnitude and LA principal strain values from images of 10 healthy volunteers and 8 patients with cardiovascular disease (CVD), of which 2 had large left ventricular ejection fraction (LVEF) impairment. We additionally create an atlas of these biomarkers using the data from the healthy volunteers. Results showed that Aladdin can accurately track the LA wall across the cardiac cycle and characterize its motion and deformation. Global LA function markers assessed with Aladdin agree well with estimates from 2D Cine MRI. A more marked active contraction phase was observed in the healthy cohort, while the CVD $\text {LVEF}_{\downarrow } $ group showed overall reduced LA function. Aladdin is uniquely able to identify LA regions with abnormal deformation metrics that may indicate focal pathology. We expect Aladdin to have important clinical applications as it can non-invasively characterize atrial pathophysiology. All source code and data are available at: https://github.com/cgalaz01/aladdin_cmr_la.
左心房（LA）的功能分析对于评估心脏健康和理解诸如心房颤动等疾病至关重要。电影磁共振成像（Cine MRI）非常适合对LA的运动和变形进行详细的三维表征，但缺乏合适的采集和分析工具。在此，我们提出了使用在线学习神经网络分析左心房位移和变形的工具（Aladdin），并对Aladdin如何从全局和区域层面表征三维LA功能进行了技术可行性研究。Aladdin包括一个在线分割和图像配准网络，以及一个针对LA量身定制的应变计算流程。我们从10名健康志愿者和8名心血管疾病（CVD）患者的图像中创建了LA位移矢量场（DVF）大小和LA主应变值的图谱，其中2名患者左心室射血分数（LVEF）严重受损。我们还利用健康志愿者的数据创建了这些生物标志物的图谱。结果表明，Aladdin能够在心动周期中准确跟踪LA壁，并表征其运动和变形。用Aladdin评估的全局LA功能标志物与二维Cine MRI的估计值非常吻合。在健康队列中观察到更明显的主动收缩期，而CVD的LVEF降低组显示LA功能总体下降。Aladdin能够独特地识别出具有异常变形指标的LA区域，这些指标可能表明局部病变。我们预计Aladdin将具有重要的临床应用价值，因为它可以无创地表征心房病理生理学。所有源代码和数据可在以下网址获取：https://github.com/cgalaz01/aladdin_cmr_la 。
