arXiv subjects

Hossein S. Aghamiry

Publications and source records attributed to Hossein S. Aghamiry.

At least 19 recordsLinked to original sources

arXiv2026

Ultra-wideband MRE of the human liver and spleen for viscoelastic model identification in hepatic inflammation

Magnetic resonance elastography (MRE) is established for noninvasive assessment of liver fibrosis. Conventional abdominal MRE is typically limited to 40-60 Hz. Lower frequencies remain largely unexplored, particularly with regard to hepatic inflammation. We developed ultra-wideband MRE covering 5-80 Hz to investigate frequency-resolved viscoelastic dispersion of the liver and spleen and to identify biomechanical markers of hepatic inflammation. Following phantom validation, nine healthy volunteers and nine patients with inflammatory liver disease were examined at 12 frequencies. Spatiotemporal phase unwrapping and frequency-adaptive wavefield preprocessing enabled reconstruction of shear wave speed (SWS), penetration rate (PR), and loss angle ($ϕ$). Six rheological models were evaluated. The largest inflammation-associated changes were observed at frequencies below 20 Hz: $ϕ$ increased by 63% (p<0.001), PR decreased by 37% (p=0.003), and SWS increased by 8% (p=0.008), indicating predominantly dissipative, rather than stiffness-related, changes and a shift toward fluid-like behavior with minor stiffness changes in the lower frequency regime. The rheological springpot model with serial dashpot provided the best fit and revealed distinct dispersion functions for liver and spleen. In patients, springpot elastic modulus increased (101%, p=0.001), while viscosity and springpot power-law exponent decreased (52%, p=0.002 and 58%, p<0.001) suggesting a shift from soft-fluid to stiff-solid liver properties. Ultra-wideband MRE revealed that inflammatory liver disease is associated with property shifts toward stronger dissipation and fluid-like behavior at low frequencies while displaying solid-like behavior at higher frequencies. Ultra-low frequency MRE may provide a diagnostic window into inflammation-associated liver viscoelasticity without full rheological modeling.

physics.med-ph↗

arXiv2026

ARGUS: Accelerated, Robust, General, and Unsupervised Cell Tracking Solutions

Background and Objective: Quantitative analysis of cell dynamics is central to modern biological research, providing critical insights into immune cell interactions, disease progression, and drug mechanisms. Automated cell tracking in time-lapse microscopy remains challenging due to noise, morphological variations, overlapping cells, and dynamic events such as divisions and fusions. Methods: We present ARGUS, a framework for Accelerated, Robust, General, and Unsupervised Cell Tracking Solutions. ARGUS combines adaptive cell detection, dense Farneback optical-flow prediction, frame-to-frame linear assignment, and a sequence-level tracklet-refinement step that reconnects trajectory fragments across short temporal gaps. Results: On publicly available Cell Tracking Challenge datasets, ARGUS achieved detection accuracy of 0.905-0.971 and tracking accuracy of 0.897-0.964, with runtimes within 1 minute (5-6 seconds for 3 frames). Conclusions: ARGUS is a modular, interpretable framework that can be adapted to different imaging modalities and biological applications without training data or GPU infrastructure. The implementation is publicly available at https://github.com/Gitinc/argus

cs.CV↗

arXiv2026

In Vivo Quantification of Glioma-Induced Solid Stress Using MR Elastography and Deformable Image Registration

Solid stress is increasingly being recognized as a key driver of tumor progression and aggressiveness, yet it has not been directly measured in patients so far. Here, we combine multifrequency magnetic resonance elastography with 3D magnetic resonance imaging (MRI)-based diffeomorphic deformable image registration network analysis to noninvasively quantify glioma-induced solid stress. In both a mouse model and patients, we identified spatially heterogeneous deformation patterns extending well beyond tumor margins. While deformation magnitude was not found to correlate with tumor size or clinical outcome, excess solid stress - defined as the product of peritumoral volumetric strain and stiffness differential between unaffected brain and peritumoral tissue - was inversely associated with patient survival, highlighting its potential as a quantitative, imaging-derived biomarker. To our knowledge, this study provides the first direct quantification of mechanical stress in patients with glioma.

physics.med-ph↗

arXiv2026

ILPU: Iterative Laplace-Based Phase Unwrapping via Bi-Level Optimization

Phase unwrapping is an essential preprocessing step for phase-based MRI applications, including susceptibility mapping, field mapping, thermometry, and MR elastography. We present Iterative Laplace-Based Phase Unwrapping (ILPU), a bi-level optimization algorithm. In this method, a lower-level solver recovers a continuous phase increment from an incremental Poisson equation using the discrete cosine transform (DCT), while an upper-level solver refines an integer offset map through quality-guided spatial regularization and a restricted local search. This coupling enables robust unwrapping in low-SNR regions through adaptive smoothness penalties and quality-weighted regularization. We evaluated ILPU on 2D and 3D brain MRI phase images against manually unwrapped reference data, using standard Laplace unwrapping, Flynn, and SEGUE as comparison methods. In 2D, ILPU achieves accuracy comparable to SEGUE. In 3D, ILPU attains a relative error of 2.12% compared with 67.59% for SEGUE and 81.02% for Laplace, demonstrating a clear advantage in volumetric unwrapping. The algorithm has O(N log N) complexity per iteration through DCT-based Laplacian estimation and is numerically faster than both Flynn and SEGUE while preserving superior accuracy. These results indicate that the bi-level optimization framework provides a robust and computationally efficient solution for phase unwrapping in MRI.

math.OC↗

arXiv2025

In Vivo Wideband MR Elastography for Assessing Age-Related Viscoelastic Changes of the Human Brain

Magnetic Resonance Elastography (MRE) noninvasively maps brain biomechanics and is highly sensitive to alterations associated with aging and neurodegenerative disease. Most implementations use a single frequency or a narrow frequency band, limiting the analysis of frequency-dependent viscoelastic parameters. We developed a dual-actuator wideband MRE (5-50 Hz) protocol and acquired wavefields at 13 frequencies in 24 healthy adults (young: 23-39 years; older: 50-63 years). Shear wave speed (SWS) maps were generated as a proxy for stiffness, and SWS dispersion was modeled using Newtonian, Kelvin-Voigt, and power-law rheological models. Whole-brain stiffness declined with age, with the strongest effect observed at low frequencies (5-16 Hz: -0.24%/year; p=0.019) compared with mid (20-35 Hz: -0.12%/year; p=0.030) and high frequencies (40-50 Hz: -0.10%/year; p=0.165). Compared to older brains, younger adults showed 14.3% higher baseline stiffness in the power-law model (p=0.001) and 8.5-9.0% higher viscosity according to the Newtonian and Kelvin-Voigt model (p<0.05). White and cortical gray matter exhibited similar age-related decreases, while deep gray matter showed an increase in the power-law exponent (+0.001/year; p=0.036), suggesting a transition toward more fluid-like properties associated with aging. Wideband MRE revealed frequency-dependent and region-specific biomechanical alterations with aging, with the strongest effects observed at low frequencies. Extending brain MRE into the low frequency regime potentially enhances sensitivity to solid-fluid interactions. Therefore, low frequency MRE may serve as an early biomechanical marker of microstructural brain changes due to aging and neurodegeneration.

physics.med-ph↗

arXiv2024

Robust elastic full-waveform inversion using an alternating direction method of multipliers with reconstructed wavefields

Elastic full-waveform inversion (EFWI) is a process used to estimate subsurface properties by fitting seismic data while satisfying wave propagation physics. The problem is formulated as a least-squares data fitting minimization problem with two sets of constraints: Partial-differential equation (PDE) constraints governing elastic wave propagation and physical model constraints implementing prior information. The alternating direction method of multipliers is used to solve the problem, resulting in an iterative algorithm with well-conditioned subproblems. Although wavefield reconstruction is the most challenging part of the iteration, sparse linear algebra techniques can be used for moderate-sized problems and frequency domain formulations. The Hessian matrix is blocky with diagonal blocks, making model updates fast. Gradient ascent is used to update Lagrange multipliers by summing PDE violations. Various numerical examples are used to investigate algorithmic components, including model parameterizations, physical model constraints, the role of the Hessian matrix in suppressing interparameter cross-talk, computational efficiency with the source sketching method, and the effect of noise and near-surface effects.

math.NA↗

arXiv2023

A practical implementation of data-space Hessian in the time-domain extended-source full-waveform inversion

Full-waveform inversion (FWI) with extended sources first computes wavefields with data-driven source extensions, such that the simulated data in inaccurate velocity models match the observed counterpart well enough to prevent cycle skipping. Then, the source extensions are minimized to update the model parameters. This two-step workflow is iterated until both data and sources are matched. It was recently shown that the source extensions are the least-squares solutions of the recorded scattered data fitting problem. As a result, they are computed by propagating backward in time the deblurred FWI data residuals, where the deblurring operator is the inverse of the damped data-domain Hessian of the scattering-source estimation problem. Estimating the deblurred data residuals is the main computational bottleneck of time-domain extended-source FWI (ES-FWI). To mitigate this issue, we first estimate them when the inverse of the data-domain Hessians is approximated by matching filters in Fourier and short-time Fourier domains. Second, we refine them with conjugate-gradient iterations when necessary. Computing the matching filters and performing one conjugate-gradient iteration each require two simulations per source. Therefore, it is critical to design some workflows that minimize this computational burden. We implement time-domain ES-FWI with the augmented Lagrangian method. Moreover, we further extend its linear regime with a multiscale frequency continuation approach, which is combined with grid coarsening to mitigate the computational burden and regularize the inversion. Finally, we use total-variation regularization to deal with large-contrast reconstruction. We present synthetic cases where different inversion workflows carried out with data-domain Hessians of variable accuracy were assessed with the aim at converging toward accurate solutions while minimizing computational cost.

physics.geo-ph↗

arXiv2023

Full waveform inversion beyond the Born approximation: A tutorial review

Full Waveform Inversion can be made immune to cycle skipping by matching the recorded data arbitrarily well from inaccurate subsurface models. To achieve this goal, the simulated wavefields can be computed in an extended search space as the solution of an overdetermined problem aiming at jointly satisfying the wave equation and fitting the data in a least-squares sense. Simply put, the wavefields are computed by solving the wave equation in the inaccurate background model with a feedback term to the data added to the physical source in the right-hand side. Then, the subsurface parameters are updated by canceling out these additional source terms, sometimes called unwisely wave-equation errors, to push the background model toward the true model in the left-hand side wave-equation operator. Although many studies were devoted to these approaches with promising numerical results, their governing physical principles and their relationships with classical FWI don't seem to be understood well yet. The goal of this tutorial is to review these principles in the theoretical framework of inverse scattering theory whose governing forward equation is the Lippmann-Schwinger equation. From this equation, we show how the data-assimilated wavefields embed an approximation of the scattered field generated by the sought model perturbation and how they modify the sensitivity kernel of classical FWI beyond the Born approximation. We also clarify how the approximation with which these wavefields approximate the unknown true wavefields is accounted for in the adjoint source of the parameter estimation problem. The theory is finally illustrated with numerical examples. Understanding the physical principles governing these methods is a necessary prerequisite to assessing their potential and limits and designing relevant heuristics to manage the latter.

physics.geo-ph↗

arXiv2022

Wave simulation in non-smooth media by PINN with quadratic neural network and PML condition

Frequency-domain simulation of seismic waves plays an important role in seismic inversion, but it remains challenging in large models. The recently proposed physics-informed neural network (PINN), as an effective deep learning method, has achieved successful applications in solving a wide range of partial differential equations (PDEs), and there is still room for improvement on this front. For example, PINN can lead to inaccurate solutions when PDE coefficients are non-smooth and describe structurally-complex media. In this paper, we solve the acoustic and visco-acoustic scattered-field wave equation in the frequency domain with PINN instead of the wave equation to remove source singularity. We first illustrate that non-smooth velocity models lead to inaccurate wavefields when no boundary conditions are implemented in the loss function. Then, we add the perfectly matched layer (PML) conditions in the loss function of PINN and design a quadratic neural network to overcome the detrimental effects of non-smooth models in PINN. We show that PML and quadratic neurons improve the results as well as attenuation and discuss the reason for this improvement. We also illustrate that a network trained during a wavefield simulation can be used to pre-train the neural network of another wavefield simulation after PDE-coefficient alteration and improve the convergence speed accordingly. This pre-training strategy should find application in iterative full waveform inversion (FWI) and time-lag target-oriented imaging when the model perturbation between two consecutive iterations or two consecutive experiments can be small.

physics.geo-ph↗

arXiv2022

On the connection between WRI and FWI: Analysis of the nonlinear term in the Hessian matrix

Implementation of the standard full waveform inversion (FWI) poses difficulties as the initial model offsets from the true model. The wavefield reconstruction inversion (WRI) was proposed to mitigate these difficulties by relaxing the wave-equation constraint. In this abstract, working on the nonlinear term in the Hessian matrix of FWI, we develop a new approximate Hessian as an Augmented Gauss-Newton (AGN) Hessian including second-order derivative information. Moreover, we establish an intimate connection between an updating formula which results from approximate solve of the Newton's method with the AGN Hessian on the FWI problem and the WRI method. Our analysis opens new perspectives for developing efficient algorithms for FWI based on the Newton's method and highlights the importance of the nonlinear term in the Hessian matrix, which is ignored in most cases.

math.OC↗

arXiv2022

Localized Wavefield Inversion (LWI): an Adaptation of Multi-Block ADMM for Localized FWI

Full-waveform inversion (FWI) is a high-resolution and computationally intensive imaging technique to reconstruct unknown parameters in the computational model in which the waves propagate; however, an accurate model of only part of this medium is required for some applications. To decrease the computational burden of such problems, target-oriented FWI was proposed where the redatumed data on the part of the medium or localized solvers for the wave equation are used. On the other hand, the classical formulation of FWI suffers from non-linearity and ill-posedness, which makes FWI sensitive to the initial model, the low-frequency content of the data, and limited illumination. In this study, we propose a localized version of the alternating direction method of multipliers (ADMM)-based FWI method, which was proposed to solve these problems in classical FWI. In our localized FWI or LWI, the medium is decomposed into a few subdomains, where some of them are updated, and the others are kept fixed based on an adaptation of multi-block ADMM, which is a powerful algorithm for solving inverse problems with decomposition and block separability. Numerical tests on the Marmousi model for a time-lapse application confirm the computational efficiency and robustness against background velocity model errors.

math.OC↗

arXiv2022

Large-scale highly-accurate extended full waveform inversion using convergent Born series

Full-waveform inversion (FWI) is a seismic imaging method that provides quantitative inference about subsurface properties with a wavelength-scale resolution. Its frequency-domain formulation is computationally efficient when processing only a few discrete frequencies. However, classical FWI, which is formulated on the reduced-parameter space, requires starting the inversion with a sufficiently-accurate initial model and low frequency to prevent being stuck in local minima due to cycle skipping. FWI with extended search space has been proposed to mitigate this issue. It contains two main steps: first, data-assimilated (DA) wavefields are computed by allowing for wave-equation errors to match the data at receivers closely. Then, subsurface parameters are estimated from these wavefields by minimizing the wave-equation errors. The DA wavefields are the least-squares solution of an overdetermined system gathering the wave and observation equations. The numerical bandwidth of the resulting normal-equation system is two times that of the wave-equation system, which can be a limiting factor for 3D large-scale applications. Therefore, computing highly accurate DA wavefields at a reasonable computational cost is an issue in extended FWI. This issue is addressed here by rewriting the normal system such that its solution can be computed by solving the time-harmonic wave equation several times in sequence. Moreover, the computational burden of multi-right-hand side (RHS) simulations is mitigated with a sketching method. Finally, we solve the time-harmonic wave equation with the convergent Born series method, which conciliates accuracy and computational efficiency. Application of the new extended FWI algorithm on the salt benchmark shows that it reconstructs at a reasonable cost subsurface models that are similar to those obtained with the classical extended FWI.

math.OC↗

arXiv2021

Multipliers waveform inversion

The full-waveform inversion (FWI) addresses the computation and characterization of subsurface model parameters by matching predicted data to observed seismograms in the frame of nonlinear optimization. We formulate FWI as a nonlinearly constrained optimization problem, for which a regularization term is minimized subject to the nonlinear data matching constraint. Unlike FWI which is based on the penalty function, the method of multipliers solves the resulting optimization problems by using the augmented Lagrangian function; and leads to a two-step recursive algorithm. The primal step requires solving an unconstrained minimization problem like the traditional FWI with a difference that the data are replaced by the Lagrange multipliers. The dual step involves an update of the Lagrange multipliers. The overall performance of the algorithm is improved considering that this multiplier method does not require an exact solution of these primal-dual subproblems. In fact, convergence is attained when only one step of a gradient-based method is taken on both subproblems. The proposed algorithm greatly improves the overall performance of FWI such as convergence from inaccurate starting models and robustness with respect to the determination of the step length. Furthermore, it can be performed by the existing FWI engines with minimal change. We only have to replace the observed data at each iteration with the multipliers, thus all the nice properties of the traditional FWI algorithms are kept. Numerical experiments confirm that the multipliers waveform inversion can converge to a solution of the inverse problem in the absence of low-frequency data from an inaccurate initial model even with a constant step size.

math.OC↗

arXiv2021

Accurate 3D frequency-domain seismic wave modeling with the wavelength-adaptive 27-point finite-difference stencil: a tool for full waveform inversion

Efficient frequency-domain Full Waveform Inversion (FWI) of long-offset/wide-azimuth node data can be designed with a few discrete frequencies. However, 3D frequency-domain seismic modeling remains challenging since it requires solving a large and sparse linear indefinite system per frequency. When such systems are solved with direct methods or hybrid direct/iterative solvers, based upon domain decomposition preconditioner, finite-difference stencils on regular Cartesian grids should be designed to conciliate compactness and accuracy, the former being necessary to mitigate the fill-in induced by the Lower-Upper (LU) factorization. Compactness is classically implemented by combining several second-order accurate stencils covering the eight cells surrounding the collocation point, leading to the so-called 27-point stencil. Accuracy is obtained by applying optimal weights on the different stiffness and consistent mass matrices such that numerical dispersion is jointly minimized for several number of grid points per wavelength ($G$). However, with this approach, the same weights are used at each collocation point, leading to suboptimal accuracy in heterogeneous media. In this study, we propose a straightforward recipe to improve the accuracy of the 27-point stencil. First, we finely tabulate the values of $G$ covering the range of wavelengths spanned by the subsurface model and the frequency. Then, we estimate with a classical dispersion analysis in homogeneous media the corresponding table of optimal weights that minimize dispersion for each $G$ treated separately. We however apply a Tikhonov regularization to guarantee smooth variation of the weights with $G$. Finally, we build the impedance matrix by selecting the optimal weights at each collocation point according to the local wavelength, hence leading to a wavelength-adaptive stencil.

cs.CE↗

arXiv2021

Randomized source sketching for full waveform inversion

Partial differential equation (PDE) constrained optimization problems such as seismic full waveform inversion (FWI) frequently arise in the geoscience and related fields. For such problems, many observations are usually gathered by multiple sources, which form the right-hand-sides of the PDE constraint. Solving the inverse problem with such massive data sets is computationally demanding, in particular when dealing with large number of model parameters. This paper proposes a novel randomized source sketching method for the efficient resolution of multisource PDE constrained optimization problems. We first formulate the different source-encoding strategies used in seismic imaging into a unified framework based on a randomized sketching. To this end, the source dimension of the problem is projected in a smaller domain by a suitably defined projection matrix that gathers the physical sources in super-sources through a weighted summation. This reduction in the number of physical sources decreases significantly the number of PDE solves while suitable sparsity-promoting regularization can efficiently mitigate the footprint of the cross-talk noise to maintain the convergence speed of the algorithm. We implement the randomized sketching method in an extended search-space formulation of frequency-domain FWI, which relies on the alternating direction method of multipliers (ADMM). Numerical examples carried out with a series of well documented 2D benchmarks demonstrate that the randomized sketching algorithm reduces the cost of large-scale problems by at least one order of magnitude compared to the original deterministic algorithm.

physics.geo-ph↗

arXiv2021

Efficient extended-search space full-waveform inversion with unknown source signatures

Full waveform inversion (FWI) requires an accurate estimation of source signatures. Due to the coupling between the source signatures and the subsurface model, small errors in the former can translate into large errors in the latter. When direct methods are used to solve the forward problem, classical frequency-domain FWI efficiently processes multiple sources for source signature and wavefield estimations once a single Lower-Upper (LU) decomposition of the wave-equation operator has been performed. However, this efficient FWI formulation is based on the exact solution of the wave equation and hence is highly sensitive to the inaccuracy of the velocity model due to the cycle skipping pathology. Recent extended-space FWI variants tackle this sensitivity issue through a relaxation of the wave equation combined with data assimilation, allowing the wavefields to closely match the data from the first inversion iteration. Then, the subsurface parameters are updated by minimizing the wave-equation violations. When the wavefields and the source signatures are jointly estimated with this approach, the extended wave equation operator becomes source dependent, hence making direct methods ineffective. In this paper, we propose a simple method to bypass this issue and estimate source signatures efficiently during extended FWI. The proposed method replaces each source with a blended source during each data-assimilated wavefield reconstruction to make the extended wave equation operator source independent. Besides computational efficiency, the additional degrees of freedom introduced by spatially distributing the sources allows for a better signature estimation at the physical location when the velocity model is rough. Numerical tests on the Marmousi II and 2004 BP salt synthetic models confirm the efficiency and the robustness of the proposed method.

math.OC↗

arXiv2021

ADMM-based full-waveform inversion for microseismic imaging

Full waveform inversion (FWI) is beginning to be used to characterize weak seismic events at different scales, an example of which is microseismic event (MSE) characterization. However, FWI with unknown sources is a severely underdetermined optimization problem, and hence requires strong prior information about the sources and/or the velocity model. The frequency-domain wavefield reconstruction inversion method (WRI) has shown promising results to mitigate the nonlinearity of the FWI objective function that is generated by cycle-skipping. WRI relies on the reconstruction of data-assimilated wavefields, which approach the true wavefields near the receivers, a helpful feature when the source is added as an additional optimization variable. We present an adaptation of a recently proposed version of WRI based on the alternating direction method of multipliers (ADMM) that first finds the location of the MSEs and then reconstructs the wavefields and the source signatures jointly. Finally, the subsurface model is updated to focus the MSEs at their true location. The method does not require prior knowledge of the number of MSEs. The inversion is stabilized by sparsifying regularizations separately tailored to the source location and velocity model subproblems. The method is tested on the Marmousi model using one MSE and two clusters of MSEs with two different initial velocity models, an accurate one and a rough one, as well as with added noise. In all cases, the method accurately locates the MSEs and recovers their source signatures.

math.OC↗

arXiv2020

Extended full waveform inversion in the time domain by the augmented Lagrangian method

Extended full-waveform inversion (FWI) has shown promising results for accurate estimation of subsurface parameters when the initial models are not sufficiently accurate. Frequency-domain applications have shown that the augmented Lagrangian (AL) method solves the inverse problem accurately with a minimal effect of the penalty parameter choice. Applying this method in the time domain, however, is limited by two main factors: (1) The challenge of data-assimilated wavefield reconstruction due to the lack of an explicit time-stepping and (2) The need to store the Lagrange multipliers, which is not feasible for the field-scale problems. We show that these wavefields are efficiently determined from the associated data (projection of the wavefields onto the receivers space) by using explicit time stepping. Accordingly, based on the augmented Lagrangian, a new algorithm is proposed which performs in "data space" (a lower dimensional subspace of the full space) in which the wavefield reconstruction step is replaced by reconstruction of the associated data, thus requiring optimization in a lower dimensional space (convenient for handling the Lagrange multipliers). We show that this new algorithm can be implemented efficiently in the time domain with existing solvers for the FWI and at a cost comparable to that of the FWI while benefiting from the robustness of the extended FWI formulation. The results obtained by numerical examples show high-performance of the proposed method for large scale time-domain FWI.

math.NA↗