Recently a new adjoint equation based iterative method was proposed for evaluating the spatial distribution of the elastic modulus of tissue based on the knowledge of its displacement field under a deformation. In this method the original problem was reformulated as a minimization problem, and a gradient-based optimization algorithm was used to solve it. Significant computational savings were realized by utilizing the solution of the adjoint elasticity equations in calculating the gradient. In this paper, we examine the performance of this method with regard to measures which we believe will impact its eventual clinical use. In particular, we evaluate its abilities to (1) resolve geometrically the complex regions of elevated stiffness; (2) to handle noise levels inherent in typical instrumentation; and (3) to generate three-dimensional elasticity images. For our tests we utilize both synthetic and experimental displacement data, and consider both qualitative and quantitative measures of performance. We conclude that the method is robust and accurate, and a good candidate for clinical application because of its computational speed and efficiency.
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http://dx.doi.org/10.1088/0031-9155/49/13/013 | DOI Listing |
Sci Rep
December 2024
Department of Biomedical Engineering, Tufts University, 4 Colby Street, Medford, MA, 02155, USA.
We propose an overview of the Rytov approximation in diffuse optics of biological tissues, for the inverse and forward problems. First, we show a physical interpretation of the Rytov approximation as a type of partial pathlength (named fluence rate partial pathlength) which is distinct from the usual partial pathlength for reflectance measurements. Second, we study the accuracy of the Rytov approximation for the calculation of Jacobians considering absorption perturbations and reflectance measurements.
View Article and Find Full Text PDFNeural Netw
November 2024
Université de Lorraine, CNRS, Institut Elie Cartan de Lorraine, Inria, BP 7023954506 Vandœuvre-lès-Nancy Cedex, France; Institut Universitaire de France (IUF), France. Electronic address:
In this work, we explore the numerical solution of geometric shape optimization problems using neural network-based approaches. This involves minimizing a numerical criterion that includes solving a partial differential equation with respect to a domain, often under geometric constraints like a constant volume. We successfully develop a proof of concept using a flexible and parallelizable methodology to tackle these problems.
View Article and Find Full Text PDFInverse Probl
December 2024
Oden Institute for Computational Engineering and Sciences, The University of Texas, Austin, TX 78712, United States of America.
PLoS One
October 2024
Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany.
Estimating parameters of dynamic models from experimental data is a challenging, and often computationally-demanding task. It requires a large number of model simulations and objective function gradient computations, if gradient-based optimization is used. In many cases, steady-state computation is a part of model simulation, either due to steady-state data or an assumption that the system is at steady state at the initial time point.
View Article and Find Full Text PDFIEEE Trans Med Imaging
September 2024
Transcranial photoacoustic computed tomography presents challenges in human brain imaging due to skull-induced acoustic aberration. Existing full-wave image reconstruction methods rely on a unified elastic wave equation for skull shear and longitudinal wave propagation, therefore demanding substantial computational resources. We propose an efficient discrete imaging model based on finite element discretization.
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