Petr Jordan

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In this work we present an inverse finite-element modeling framework for constitutive modeling and parameter estimation of soft tissues using full-field volumetric deformation data obtained from 3D ultrasound. The finite-element model is coupled to full-field visual measurements by regularization springs attached at nodal locations. The free ends of the(More)
We present a modular framework for mechanically regularized nonrigid image registration of 3D ultrasound and for identification of tissue mechanical parameters. Mechanically regularized deformation fields are computed from sparsely estimated local displacements. We enforce image-based local motion estimates by applying concentrated forces at mesh nodes of a(More)
The recent advent of real-time 3-D ultrasound (3DUS) imaging enables a variety of surgical procedures to be performed within the beating heart. Implementation of these procedures is hampered by the difficulty of manipulating tissue guided by the distorted, low resolution 3DUS images and the dexterity constraints imposed by the confined intracardiac space.(More)
Constitutive models of the nonlinear, viscoelastic response of soft tissue under large strains typical of medical manipulations is required for accurate diagnostic and simulation purposes. We have modified a constitutive model used to describe cartilage and cervix to characterize the large strain mechanical behavior of breast tissue across pathologies(More)
We describe a modeling methodology intended as a preliminary step in the identification of appropriate constitutive frameworks for the time-dependent response of biological tissues. The modeling approach comprises a customizable rheological network of viscous and elastic elements governed by user-defined 1D constitutive relationships. The model parameters(More)
In this work we present an inverse finite-element modeling framework for constitutive modeling and parameter estimation of soft tissues using full-field volumetric deformation data obtained from 3D ultrasound. The finite-element model is coupled to full-field visual measurements by regularization springs attached at nodal locations. The free ends of the(More)
Traditionally, the Horn & Schunck algorithm [2] is implemented with a first-order motion smoothness constraint. The corresponding functional to be minimized is Φ(u, v, w) = Ω ∂I ∂x u + ∂I ∂y v + ∂I ∂z w + ∂I ∂t 2 + αE s dxdydz, (1) where E s is the regularization term defined as E s = ∂u ∂x 2 + ∂u ∂y 2 + ∂u ∂z 2 + ∂v ∂x 2 + ∂v ∂y 2 + ∂v ∂z 2 + ∂w ∂x 2 + ∂w(More)
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