Mechanics Based Tomography: A Preliminary Feasibility Study
AbstractWe present a non-destructive approach to sense inclusion objects embedded in a solid medium remotely from force sensors applied to the medium and boundary displacements that could be measured via a digital image correlation system using a set of cameras. We provide a rationale and strategy to uniquely identify the heterogeneous sample composition based on stiffness (here, shear modulus) maps. The feasibility of this inversion scheme is tested with simulated experiments that could have clinical relevance in diagnostic imaging (e.g., tumor detection) or could be applied to engineering materials. No assumptions are made on the shape or stiffness quantity of the inclusions. We observe that the novel inversion method using solely boundary displacements and force measurements performs well in recovering the heterogeneous material/tissue composition that consists of one and two stiff inclusions embedded in a softer background material. Furthermore, the target shear modulus value for the stiffer inclusion region is underestimated and the inclusion size is overestimated when incomplete boundary displacements on some part of the boundary are utilized. For displacements measured on the entire boundary, the shear modulus reconstruction improves significantly. Additionally, we observe that with increasing number of displacement data sets utilized in solving the inverse problem, the quality of the mapped shear moduli improves. We also analyze the sensitivity of the shear modulus maps on the noise level varied between 0.1% and 5% white Gaussian noise in the boundary displacements, force and corresponding displacement indentation. Finally, a sensitivity analysis of the recovered shear moduli to the depth, stiffness and the shape of the stiff inclusion is performed. We conclude that this approach has potential as a novel imaging modality and refer to it as Mechanics Based Tomography (MBT). View Full-Text
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Mei, Y.; Wang, S.; Shen, X.; Rabke, S.; Goenezen, S. Mechanics Based Tomography: A Preliminary Feasibility Study. Sensors 2017, 17, 1075.
Mei Y, Wang S, Shen X, Rabke S, Goenezen S. Mechanics Based Tomography: A Preliminary Feasibility Study. Sensors. 2017; 17(5):1075.Chicago/Turabian Style
Mei, Yue; Wang, Sicheng; Shen, Xin; Rabke, Stephen; Goenezen, Sevan. 2017. "Mechanics Based Tomography: A Preliminary Feasibility Study." Sensors 17, no. 5: 1075.
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