Honarvar M, Rohling R, Salcudean S E
Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada.
Phys Med Biol. 2016 Apr 21;61(8):3026-48. doi: 10.1088/0031-9155/61/8/3026. Epub 2016 Mar 22.
As part of tissue elasticity imaging or elastography, an inverse problem needs to be solved to find the elasticity distribution from the measured displacements. The finite element method (FEM) is a common method for solving the inverse problem in dynamic elastography. This problem has been solved with both direct and iterative FEM schemes. Each of these methods has its own advantages and disadvantages which are examined in this paper. Choosing the data resolution and the excitation frequency are critical for achieving the best estimation of the tissue elasticity in FEM methods. In this paper we investigate the performance of both direct and iterative FEMs for different ranges of excitation frequency. A new form of iterative method is suggested here which requires a lower mesh density compared to the original form. Also two forms of the direct method are compared in this paper: one using the exact fit for derivatives calculation and the other using the least squares fit. We also perform a study on the spatial resolution of these methods using simulations. The comparison is also validated using a phantom experiment. The results suggest that the direct method with least squares fit is more robust to noise compared to other methods but has slightly lower resolution results. For example, for the homogenous region with 20 dB noise added to the data, the RMS error for the direct method with least squares fit is approximately half of the iterative method. It was observed that the ratio of voxel size to the wavelength should be within a specific range for the results to be reliable. For example for the direct method with least squares fit, for the case of 20 dB noise level, this ratio should be between 0.1 to 0.2. On balance, considering the much higher computational cost of the iterative method, the dependency of the iterative method on the initial guess, and the greater robustness of the direct method to noise, we suggest using the direct method with least squares fit for linear elasticity cases.
作为组织弹性成像或弹性图的一部分,需要解决一个反问题,以便从测量的位移中找到弹性分布。有限元法(FEM)是解决动态弹性成像中反问题的常用方法。这个问题已经通过直接和迭代有限元方案解决。本文研究了这些方法各自的优缺点。选择数据分辨率和激励频率对于在有限元方法中实现对组织弹性的最佳估计至关重要。在本文中,我们研究了直接和迭代有限元法在不同激励频率范围内的性能。本文提出了一种新的迭代方法形式,与原始形式相比,它需要更低的网格密度。本文还比较了两种直接方法的形式:一种使用精确拟合进行导数计算,另一种使用最小二乘拟合。我们还使用模拟对这些方法的空间分辨率进行了研究。通过体模实验对比较结果进行了验证。结果表明,与其他方法相比,采用最小二乘拟合的直接方法对噪声更具鲁棒性,但分辨率结果略低。例如,对于在数据中添加了20 dB噪声的均匀区域,采用最小二乘拟合的直接方法的均方根误差约为迭代方法的一半。据观察,体素大小与波长的比值应在特定范围内,结果才可靠。例如,对于采用最小二乘拟合的直接方法,在20 dB噪声水平的情况下,该比值应在0.1至0.2之间。权衡考虑,鉴于迭代方法的计算成本高得多、对初始猜测的依赖性以及直接方法对噪声的更强鲁棒性,我们建议对于线性弹性情况使用采用最小二乘拟合的直接方法。