On the soft tissue ultrasound elastography using FEM based inversion approach

弹性模量 弹性成像 有限元法 流离失所(心理学) 反问题 刚度 模数 迭代法 计算机科学 算法 数学 数学分析 材料科学 声学 超声波 物理 几何学 热力学 复合材料 心理治疗师 心理学
作者
Seyed Shahab Eshaghinia,Afshin Taghvaeipour,M.M. Aghdam,Hassan Rivaz
出处
期刊:Proceedings Of The Institution Of Mechanical Engineers, Part H: Journal Of Engineering In Medicine [SAGE Publishing]
卷期号:238 (3): 271-287
标识
DOI:10.1177/09544119231224674
摘要

Elastography is a medical imaging modality that enables visualization of tissue stiffness. It involves quasi-static or harmonic mechanical stimulation of the tissue to generate a displacement field which is used as input in an inversion algorithm to reconstruct tissue elastic modulus. This paper considers quasi-static stimulation and presents a novel inversion technique for elastic modulus reconstruction. The technique follows an inverse finite element framework. Reconstructed elastic modulus maps produced in this technique do not depend on the initial guess, while it is computationally less involved than iterative reconstruction approaches. The method was first evaluated using simulated data (in-silico) where modulus reconstruction’s sensitivity to displacement noise and elastic modulus was assessed. To demonstrate the method’s performance, displacement fields of two tissue mimicking phantoms determined using three different motion tracking techniques were used as input to the developed elastography method to reconstruct the distribution of relative elastic modulus of the inclusion to background tissue. In the next stage, the relative elastic modulus of three clinical cases pertaining to liver cancer patient were determined. The obtained results demonstrate reasonably high elastic modulus reconstruction accuracy in comparison with similar direct methods. Also it is associated with reduced computational cost in comparison with iterative techniques, which suffer from convergence and uniqueness issues, following the same formulation concept. Moreover, in comparison with other methods which need initial guess, the presented method does not require initial guess while it is easy to understand and implement.
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