A robust numerical solution to reconstruct a globally relative shear modulus distribution from strain measurements.
To noninvasively quantify tissue elasticity for differentiating malignancy of soft tissue, we previously proposed a two-dimensional (2-D) mechanical inverse problem in which simultaneous partial differential equations (PDE's) represented the target distribution globally of relative shear moduli with respect to reference shear moduli such that the relative values could be determined from strain distributions obtained by conventional ultrasound (US) or nuclear magnetic resonance (NMR) imaging-based analysis. Here, we further consider the analytic solution in the region of interest, subsequently demonstrating that the problem is inevitably ill-conditioned in real-world applications, i.e., noise in measurement data and improper configurations of mechanical sources/reference regions make it impossible to guarantee the existence of a stable and unique target global distribution. Next, based on clarification of the inherent problematic conditions, we describe a newly developed numerical-based implicit-integration approach that novelly incorporates a computationally efficient regularization method designed to solve this differential inverse problem using just low-pass filtered spectra derived from strain measurements. To evaluate method effectiveness, reconstructions of the global distribution are carried out using intentionally created ill-conditioned models. The resultant reconstructions indicate the robust solution is highly suitable, while also showing it has high potential to be applied in the development of an effective yet versatile diagnostic tool for quantifying the distribution of elasticity in various soft tissues.
Sumi
C
C
Department of Electrical and Electronics Engineering, Faculty of Science and Technology, Sophia University, Tokyo, Japan. csumi@kiyoshi.ee.sophia.ac.jp
Nakayama
K
K
D001696:Biomechanical Phenomena; D003627:Data Interpretation, Statistical; D004548:Elasticity; D007091:Image Processing, Computer-Assisted / Q000379:methods*