IEEE Trans Biomed Eng. 2017 Dec;64(12):2858-2871. doi: 10.1109/TBME.2017.2679214. Epub 2017 Mar 8.
The present study investigates the feasibility, accuracy, and precision of 3-D profile extraction of the human skull bone using a custom-designed ultrasound matrix transducer in Pulse-Echo. Due to the attenuative scattering properties of the skull, the backscattered echoes from the inner surface of the skull are severely degraded, attenuated, and at some points overlapped. Furthermore, the speed of sound (SOS) in the skull varies significantly in different zones and also from case to case; if considered constant, it introduces significant error to the profile measurement. A new method for simultaneous estimation of the skull profiles and the sound speed value is presented. The proposed method is a two-folded procedure: first, the arrival times of the backscattered echoes from the skull bone are estimated using multi-lag phase delay (MLPD) and modified space alternating generalized expectation maximization (SAGE) algorithms. Next, these arrival times are fed into an adaptive sound speed estimation algorithm to compute the optimal SOS value and subsequently, the skull bone thickness. For quantitative evaluation, the estimated bone phantom thicknesses were compared with the mechanical measurements. The accuracies of the bone thickness measurements using MLPD and modified SAGE algorithms combined with the adaptive SOS estimation were 7.93% and 4.21%, respectively. These values were 14.44% and 10.75% for the autocorrelation and cross-correlation methods. Additionally, the Bland-Altman plots showed the modified SAGE outperformed the other methods with -0.35 and 0.44 mm limits of agreement. No systematic error that could be related to the skull bone thickness was observed for this method.
本研究旨在探讨使用定制超声矩阵换能器在脉冲回波中对人颅骨 3D 轮廓进行提取的可行性、准确性和精密度。由于颅骨的衰减散射特性,颅骨内表面的背散射回波严重退化、衰减,并且在某些点上重叠。此外,颅骨中的声速(SOS)在不同区域和不同个体之间差异很大;如果将其视为常数,会给轮廓测量带来显著误差。本文提出了一种同时估计颅骨轮廓和声速值的新方法。该方法是一种两阶段的过程:首先,使用多延迟相位延迟(MLPD)和改进的空间交替广义期望最大化(SAGE)算法估计颅骨背散射回波的到达时间。接下来,将这些到达时间输入到自适应声速估计算法中,以计算最佳 SOS 值,随后计算颅骨骨厚度。为了定量评估,将估计的骨体模厚度与机械测量值进行比较。使用 MLPD 和改进的 SAGE 算法与自适应 SOS 估计相结合的骨厚度测量的准确度分别为 7.93%和 4.21%。对于自相关和互相关方法,这些值分别为 14.44%和 10.75%。此外,Bland-Altman 图显示,改进的 SAGE 方法的一致性界限为-0.35 和 0.44mm,优于其他方法。对于这种方法,没有观察到与颅骨厚度相关的系统误差。