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基于智能手机的三维扫描的快速患者特定有限元网格。

Rapid patient-specific FEM meshes from 3D smart-phone based scans.

机构信息

Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America.

Department of Neurology, Beth Israel Deaconess Medical Center (BIDMC), Boston, MA 02215, United States of America.

出版信息

Physiol Meas. 2024 Feb 28;45(2):025008. doi: 10.1088/1361-6579/ad26d2.

Abstract

The objective of this study was to describe and evaluate a smart-phone based method to rapidly generate subject-specific finite element method (FEM) meshes. More accurate FEM meshes should lead to more accurate thoracic electrical impedance tomography (EIT) images.The method was evaluated on an iPhonethat utilized an app called Heges, to obtain 3D scans (colored, surface triangulations), a custom belt, and custom open-source software developed to produce the subject-specific meshes. The approach was quantitatively validated via mannequin and volunteer tests using an infrared tracker as the gold standard, and qualitatively assessed in a series of tidal-breathing EIT images recorded from 9 subjects.The subject-specific meshes can be generated in as little as 6.3 min, which requires on average 3.4 min of user interaction. The mannequin tests yielded high levels of precision and accuracy at 3.2 ± 0.4 mm and 4.0 ± 0.3 mm root mean square error (RMSE), respectively. Errors on volunteers were only slightly larger (5.2 ± 2.1 mm RMSE precision and 7.7 ± 2.9 mm RMSE accuracy), illustrating the practical RMSE of the method.Easy-to-generate, subject-specific meshes could be utilized in the thoracic EIT community, potentially reducing geometric-based artifacts and improving the clinical utility of EIT.

摘要

本研究的目的是描述和评估一种基于智能手机的方法,以快速生成针对特定个体的有限元方法 (FEM) 网格。更精确的 FEM 网格应生成更准确的胸部电阻抗断层成像 (EIT) 图像。该方法在 iPhone 上进行了评估,该 iPhone 使用名为 Heges 的应用程序来获取 3D 扫描(彩色、表面三角剖分)、定制腰带和定制的开源软件,以生成针对特定个体的网格。该方法通过使用红外跟踪器作为金标准的模拟人和志愿者测试进行了定量验证,并在从 9 名志愿者记录的一系列潮式呼吸 EIT 图像中进行了定性评估。针对特定个体的网格可以在短短 6.3 分钟内生成,平均需要 3.4 分钟的用户交互。模拟人测试的精度和准确度分别达到了 3.2 ± 0.4 毫米和 4.0 ± 0.3 毫米均方根误差 (RMSE)。志愿者的误差仅略大一些(RMSE 精度为 5.2 ± 2.1 毫米,RMSE 准确度为 7.7 ± 2.9 毫米),说明了该方法的实际 RMSE。易于生成的针对特定个体的网格可以在胸部 EIT 社区中使用,可能会减少基于几何形状的伪影,并提高 EIT 的临床实用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a97b/10901069/0727f3a9f07a/pmeaad26d2f1_lr.jpg

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