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基于确定性光线追踪的颅骨光声衰减和频散建模:实现实时像差校正。

Skull's Photoacoustic Attenuation and Dispersion Modeling with Deterministic Ray-Tracing: Towards Real-Time Aberration Correction.

机构信息

Department of Biomedical Engineering, Islamic Azad University, Science and Research Branch, Tehran 1477893855, Iran.

Department of Biomedical Engineering, Iran University of Science and Technology, Tehran 1684613114, Iran.

出版信息

Sensors (Basel). 2019 Jan 16;19(2):345. doi: 10.3390/s19020345.

Abstract

Although transcranial photoacoustic imaging has been previously investigated by several groups, there are many unknowns about the distorting effects of the skull due to the impedance mismatch between the skull and underlying layers. The current computational methods based on finite-element modeling are slow, especially in the cases where fine grids are defined for a large 3-D volume. We develop a very fast modeling/simulation framework based on deterministic ray-tracing. The framework considers a multilayer model of the medium, taking into account the frequency-dependent attenuation and dispersion effects that occur in wave reflection, refraction, and mode conversion at the skull surface. The speed of the proposed framework is evaluated. We validate the accuracy of the framework using numerical phantoms and compare its results to k-Wave simulation results. Analytical validation is also performed based on the longitudinal and shear wave transmission coefficients. We then simulated, using our method, the major skull-distorting effects including amplitude attenuation, time-domain signal broadening, and time shift, and confirmed the findings by comparing them to several ex vivo experimental results. It is expected that the proposed method speeds up modeling and quantification of skull tissue and allows the development of transcranial photoacoustic brain imaging.

摘要

尽管已经有几个小组对颅外光声成像进行了研究,但由于颅骨与下面各层之间的阻抗不匹配,颅骨的失真效应仍有许多未知之处。目前基于有限元建模的计算方法速度较慢,尤其是在需要为大型 3D 体积定义精细网格的情况下。我们开发了一种基于确定性射线追踪的非常快速的建模/模拟框架。该框架考虑了介质的多层模型,考虑了颅骨表面发生的波反射、折射和模式转换过程中频率相关的衰减和色散效应。评估了所提出框架的速度。我们使用数值体模验证了框架的准确性,并将其结果与 k-Wave 模拟结果进行了比较。还基于纵波和横波传输系数进行了分析验证。然后,我们使用我们的方法模拟了主要的颅骨失真效应,包括幅度衰减、时域信号展宽和时移,并通过与几个离体实验结果进行比较,验证了这些结果。预计该方法将加快颅骨组织的建模和量化,并允许开发颅外光声脑成像。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95cd/6359310/65ccca6f1c52/sensors-19-00345-g001.jpg

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