Department of Biomedical Engineering, School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei 230009, China.
Institute of Biomedical Technologies, Auckland University of Technology, Auckland 1001, New Zealand.
Sensors (Basel). 2018 Nov 9;18(11):3852. doi: 10.3390/s18113852.
The authors recently developed a two-dimensional (2D) holographic electromagnetic induction imaging (HEI) for biomedical imaging applications. However, this method was unable to detect small inclusions accurately. For example, only one of two inclusions can be detected in the reconstructed image if the two inclusions were located at the same XY plane but in different Z-directions. This paper provides a theoretical framework of three-dimensional (3D) HEI to accurately and effectively detect inclusions embedded in a biological object. A numerical system, including a realistic head phantom, a 16-element excitation sensor array, a 16-element receiving sensor array, and image processing model has been developed to evaluate the effectiveness of the proposed method for detecting small stroke. The achieved 3D HEI images have been compared with 2D HEI images. Simulation results show that the 3D HEI method can accurately and effectively identify small inclusions even when two inclusions are located at the same XY plane but in different Z-directions. This preliminary study shows that the proposed method has the potential to develop a useful imaging tool for the diagnosis of neurological diseases and injuries in the future.
作者最近开发了一种用于生物医学成像应用的二维（2D）全息电磁感应成像（HEI）。然而，该方法无法准确检测小的夹杂。例如，如果两个夹杂位于相同的 XY 平面但在不同的 Z 方向，则在重建图像中只能检测到两个夹杂中的一个。本文提供了一种三维（3D）HEI 的理论框架，以准确有效地检测嵌入生物物体中的夹杂。已经开发了一个数值系统，包括一个现实的头部体模、一个 16 元素激励传感器阵列、一个 16 元素接收传感器阵列和图像处理模型，以评估该方法用于检测小中风的有效性。已经比较了获得的 3D HEI 图像和 2D HEI 图像。模拟结果表明，即使两个夹杂位于相同的 XY 平面但在不同的 Z 方向，3D HEI 方法也可以准确有效地识别小夹杂。这项初步研究表明，该方法有可能在未来开发出一种用于诊断神经疾病和损伤的有用成像工具。
