Center of Information Technology and Medical Engineering, University Hospital of Heidelberg, Im Neuenheimer Feld 130.1/130.3, Heidelberg, 69117, Baden-Württemberg, Germany.
Innovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Semmelweisstr. 14, Leipzig, 04103, Saxony, Germany.
Int J Comput Assist Radiol Surg. 2022 Sep;17(9):1707-1716. doi: 10.1007/s11548-022-02593-4. Epub 2022 Mar 31.
For the visualization of pulmonary ventilation with Electrical Impedance Tomography (EIT) most devices use standard reconstruction models, featuring common thorax dimensions and predetermined electrode locations. Any discrepancies between the available model and the patient in terms of body shape and electrode position lead to incorrectly displayed impedance distributions. This work addresses that problem by presenting and evaluating a method for 3D model generation of the thorax and any affixed electrodes based on handheld video-footage.
Therefore, a process was developed, providing users with the ability to capture a patient's chest and the attached electrodes via smartphone. Once data is collected, extracted images are used to generate a 3D model with a structure from motion approach and locate electrodes with ArUco markers. For the evaluation of the developed method, multiple tests were performed in laboratory environments, which were compared with manually created reference models and differences quantified based on mean distance, standard deviation, and maximum distance.
The implemented workflow allows for automated model reconstruction based on videos or selected images captured with a handheld device. It generates sparse point clouds from which a surface mesh is reconstructed and returns relative coordinates of any identified ArUco marker. The average value for the mean distance error of two model generations was 5.4 mm while the mean standard deviation was 6.0 mm. The average runtime of twelve reconstructions was 5:17 min, with a minimal runtime of 3:22 min and a maximal runtime of 7:29 min.
The presented methods and results show that model reconstruction of a patient's thorax and applied electrodes at an emergency site is feasible with already available devices. This is a first step toward the automated generation of patient-specific reconstruction models for Electrical Impedance Tomography based on images recorded with handheld devices.
为了实现肺部通气的可视化，采用电阻抗断层成像（EIT）的大多数设备都使用标准的重建模型，其特点是具有常见的胸腔尺寸和预设的电极位置。如果可用模型与患者在体型和电极位置方面存在差异，则会导致显示的阻抗分布不正确。这项工作通过提出并评估一种基于手持视频的方法来解决这个问题，该方法可生成胸部和任何附着电极的 3D 模型。
因此，开发了一种流程，使患者能够通过智能手机捕获胸部和附着的电极。一旦收集到数据，提取的图像将用于使用运动结构方法生成 3D 模型，并使用 ArUco 标记定位电极。为了评估所开发的方法，在实验室环境中进行了多次测试，将其与手动创建的参考模型进行比较，并根据平均距离、标准差和最大距离进行量化差异。
所实现的工作流程允许基于视频或使用手持设备捕获的选定图像自动进行模型重建。它从稀疏点云中生成表面网格，并返回任何识别的 ArUco 标记的相对坐标。两代模型的平均距离误差的平均值为 5.4mm，平均标准差为 6.0mm。十二次重建的平均运行时间为 5:17 分钟，最短运行时间为 3:22 分钟，最长运行时间为 7:29 分钟。
所提出的方法和结果表明，在急救现场使用现有设备对患者的胸部和应用电极进行模型重建是可行的。这是朝着使用手持设备记录的图像自动生成基于电阻抗断层成像的患者特定重建模型迈出的第一步。
