Focused cardiac ultrasound (FoCUS) is a valuable point-of-care method for evaluating cardiovascular structures and function, but its scope is limited by equipment and operator’s experience, resulting in primarily qualitative 2D exams. This study presents a novel framework to automatically estimate the 3D spatial relationship between standard FoCUS views. The proposed framework uses a multi-view U-Net-like fully convolutional neural network to regress line-based heatmaps representing the most likely areas of intersection between input images. The lines that best fit the regressed heatmaps are then extracted, and a system of nonlinear equations based on the intersection between view triplets is created and solved to determine the relative 3D pose between all input images. The feasibility and accuracy of the proposed pipeline were validated using a novel realistic FoCUS dataset, demonstrating promising results. Interestingly, as shown in preliminary experiments, the estimation of the 2D images’ relative poses enables the application of 3D image analysis methods and paves the way for 3D quantitative assessments in FoCUS examinations.

中文翻译：

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聚焦心脏超声中的自动多视图位姿估计

聚焦心脏超声 (FoCUS) 是评估心血管结构和功能的一种有价值的现场检查方法，但其范围受到设备和操作员经验的限制，导致主要是定性的 2D 检查。本研究提出了一种新颖的框架来自动估计标准 FoCUS 视图之间的 3D 空间关系。所提出的框架使用多视图 U-Net 式全卷积神经网络来回归基于线的热图，表示输入图像之间最可能的交叉区域。然后提取最适合回归热图的线，创建并求解基于视图三元组之间的交集的非线性方程组，以确定所有输入图像之间的相对 3D 位姿。使用新颖的真实 FoCUS 数据集验证了所提出的管道的可行性和准确性，展示了有希望的结果。有趣的是，如初步实验所示，2D 图像相对位姿的估计使得 3D 图像分析方法的应用成为可能，并为 FoCUS 检查中的 3D 定量评估铺平了道路。