title: An information-based machine learning approach to elasticity imaging.
abstract: An information-based technique is described for applications in mechanical property imaging of soft biological media under quasi-static loads. We adapted the Autoprogressive method that was originally developed for civil engineering applications for this purpose. The Autoprogressive method is a computational technique that combines knowledge of object shape and a sparse distribution of force and displacement measurements with finite-element analyses and artificial neural networks to estimate a complete set of stress and strain vectors. Elasticity imaging parameters are then computed from estimated stresses and strains. We introduce the technique using ultrasonic pulse-echo measurements in simple gelatin imaging phantoms having linear-elastic properties so that conventional finite-element modeling can be used to validate results. The Autoprogressive algorithm does not require any assumptions about the material properties and can, in principle, be used to image media with arbitrary properties. We show that by selecting a few well-chosen force-displacement measurements that are appropriately applied during training and establish convergence, we can estimate all nontrivial stress and strain vectors throughout an object and accurately estimate an elastic modulus at high spatial resolution. This new method of modeling the mechanical properties of tissue-like materials introduces a unique method of solving the inverse problem and is the first technique for imaging stress without assuming the underlying constitutive model.
authors.affiliation: Department of Bioengineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA. hoerig2@illinois.edu.
authors.forename: Cameron
authors.initials: C
authors.lastname: Hoerig
authors.affiliation: Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
authors.forename: Jamshid
authors.initials: J
authors.lastname: Ghaboussi
authors.affiliation: Department of Bioengineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
authors.forename: Michael F
authors.initials: MF
authors.lastname: Insana
mesh_terms: D000465:Algorithms; D001696:Biomechanical Phenomena; D004548:Elasticity; D020342:Finite Element Analysis; D006801:Humans; D000069550:Machine Learning; D008954:Models, Biological; D019047:Phantoms, Imaging; D013314:Stress, Mechanical
keywords: Constitutive modeling; Finite-element analysis; Mechanical properties; Neural networks; Ultrasonic speckle tracking