Fluorescence diffuse optical tomography using a multi-view continuous-wave and non-contact measurement system and an algorithm incorporating the lp (0 < p ≤ 1) sparsity regularization reconstructs a localized fluorescent target in a small animal. The measurement system provides a total of 25 fluorescence surface 2D-images of an object, which are acquired by a CCD camera from five different angles of view with excitation from five different angles. Fluorescence surface emissions from five different angles of view are simultaneously imaged on the CCD sensor, thus leading to fast acquisition of the 25 images within three minutes. The distributions of the fluorophore are reconstructed by solving the inverse problem based on the photon diffusion equations. In the reconstruction process incorporating the lp sparsity regularization, the regularization term is reformulated as a differentiable function for gradient-based non-linear optimization. Numerical simulations and phantom experiments show that the use of the lp sparsity regularization improves the localization of the target and quantitativeness of the fluorophore concentration. A mouse experiment demonstrates that a localized fluorescent target in a mouse is successfully reconstructed.
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http://dx.doi.org/10.1364/BOE.5.001839 | DOI Listing |
Adv Model Simul Eng Sci
January 2025
Department of Mechanical and Process Engineering, Institute for Mechanical Systems, ETH Zürich, Zürich, 8092 Switzerland.
We extend (EUCLID Efficient Unsupervised Constitutive Law Identification and Discovery)-a data-driven framework for automated material model discovery-to pressure-sensitive plasticity models, encompassing arbitrarily shaped yield surfaces with convexity constraints and non-associated flow rules. The method only requires full-field displacement and boundary force data from one single experiment and delivers constitutive laws as interpretable mathematical expressions. We construct a material model library for pressure-sensitive plasticity models with non-associated flow rules in four steps: (1) a Fourier series describes an arbitrary yield surface shape in the deviatoric stress plane; (2) a pressure-sensitive term in the yield function defines the shape of the shear failure surface and determines plastic deformation under tension; (3) a compression cap term determines plastic deformation under compression; (4) a non-associated flow rule may be adopted to avoid the excessive dilatancy induced by plastic deformations.
View Article and Find Full Text PDFQuant Imaging Med Surg
January 2025
Henan Key Laboratory of Imaging and Intelligent Processing, Information Engineering University, Zhengzhou, China.
Background: Photon-counting computed tomography (CT) is an advanced imaging technique that enables multi-energy imaging from a single scan. However, the limited photon count assigned to narrow energy bins leads to increased quantum noise in the reconstructed spectral images. To address this issue, leveraging the prior information in the spectral images is essential.
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
December 2024
High Energy Accelerator Research Organization, Tsukuba, Ibaraki, 305-0801, Japan.
Purpose: In this paper, we describe an algebraic reconstruction algorithm with a total variation regularization (ART + TV) based on the Superimposed Wavefront Imaging of Diffraction-enhanced X-rays (SWIDeX) method to effectively reduce the number of projections required for differential phase-contrast CT reconstruction.
Methods: SWIDeX is a technique that uses a Laue-case Si analyzer with closely spaced scintillator to generate second derivative phase-contrast images with high contrast of a subject. When the projections obtained by this technique are reconstructed, a Laplacian phase-contrast tomographic image with higher sparsity than the original physical distribution of the subject can be obtained.
Quant Imaging Med Surg
December 2024
Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China.
Mamm Genome
December 2024
Department of Animal Science, Faculty of Animal and Aquatic Science, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.
Using dense genomic markers opens up new opportunities and challenges for breeding programs. The need to penalize marker-specific regression coefficients becomes particularly important when dense markers are available. Therefore, fitting the marker effects to observations using a regularization technique, such as Bayesian LASSO (BL) regression, is of great interesting.
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