数字图像相关弹性成像中基于梯度的弹性属性重建
Gradient Based Elastic Property Reconstruction in Digital Image Correlation Elastography.
作者信息
Brazy Alexandre, Houten Elijah Van
机构信息
Department of Mechanical Engineering, University of Sherbrooke, Sherbrooke, Quebec J1L 2R1, Canada.
出版信息
J Med Phys. 2020 Jul-Sep;45(3):168-174. doi: 10.4103/jmp.JMP_99_19. Epub 2020 Oct 13.
AIM
A Conjugate Gradient implementation of the Digital Image Correlation Elastography method is presented.
METHOD
The gradient is calculated using the adjoint method, requiring only two forward solutions regardless of the number of mechanical properties to reconstruct. A power-law based multi-frequency viscoelastic model is used to relate the reconstructed mechanical properties and the Digital Image Correlation surface displacement measurements.
RESULT
The method is tested against harmonic surface motion fields generated through numerical simulation and measured on a silicon phantom.
CONCLUSION
Reconstruction results show the ability of the gradient reconstruction method to detect stiff internal inclusions in simulated and phantom data.
目的
提出数字图像相关弹性成像方法的共轭梯度实现。
方法
使用伴随方法计算梯度，无论要重建的力学特性数量多少，仅需两个正向解。基于幂律的多频粘弹性模型用于关联重建的力学特性和数字图像相关表面位移测量值。
结果
该方法针对通过数值模拟生成并在硅仿体上测量的谐波表面运动场进行了测试。
结论
重建结果表明梯度重建方法能够在模拟数据和仿体数据中检测到坚硬的内部夹杂物。
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