A General Technique for Smoothing Multi-Dimensional Datasets Utilizing Orthogonal Expansions and Lower Dimensional Smoothers. - Mark A. Anastasio; Xiaochuan Pan; Chien-Min Kao (ICIP (2), 1998)

Optimization and FROC Analysis of Rule-Based Detection Schemes Using a Multiobjective Approach. - Mark A. Anastasio; Matthew A. Kupinski; Robert M. Nishikawa (IEEE Trans. Medical Imaging, 1998)

Investigation of the noise properties of a new class of reconstruction methods in diffraction tomography. - Mark A. Anastasio; Xiaochuan Pan (Int. J. Imaging Syst. Technol., 1999)

Multidimensional smoothing using orthogonal expansions. - Mark A. Anastasio; Xiaochuan Pan; Chien-Min Kao (IEEE Signal Process. Lett., 1999)

Multiobjective Genetic Optimization of Diagnostic Classifiers with Implications for Generating ROC Curves. - Matthew A. Kupinski; Mark A. Anastasio (IEEE Trans. Medical Imaging, 1999)

Multiobjective genetic optimization of diagnostic classifiers used in the computerized detection of mass lesions in mammography. - Matthew A. Kupinski; Mark A. Anastasio; Maryellen Lissak Giger (Image Processing, 2000)

A new reconstruction approach for reflection mode diffraction tomography. - Mark A. Anastasio; Xiaochuan Pan (IEEE Trans. Image Process., 2000)

Development and evaluation of minimal-scan reconstruction algorithms for diffraction tomography. - Mark A. Anastasio; Xiaochuan Pan (Image Processing, 2001)

Comments on the Filtered Backprojection Algorithm, Range Conditions, and the Pseudoinverse Solution. - Mark A. Anastasio; Xiaochuan Pan; Eric Clarkson (IEEE Trans. Medical Imaging, 2001)

Image reconstruction of reflectivity from short scan data. - Xiaochuan Pan; Yu Zou; Mark A. Anastasio (ISBI, 2002)

Numerically robust minimal-scan reconstruction algorithms for diffraction tomography via radon transform inversion. - Mark A. Anastasio; Xiaochuan Pan (Int. J. Imaging Syst. Technol., 2002)

On a limited-view reconstruction problem in wavefield tomography. - Xiaochuan Pan; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2002)

Investigation of megavoltage local tomography for detecting setup errors in radiation therapy. - Daxin Shi; Mark A. Anastasio; Xiaochuan Pan; Charles A. Pelizzari; Peter Munro (Image-Guided Procedures, 2003)

An improved reconstruction algorithm for 3-D diffraction tomography using spherical-wave sources. - Mark A. Anastasio; Xiaochuan Pan (IEEE Trans. Biomed. Eng., 2003)

Data redundancy and reduced-scan reconstruction in reflectivity tomography. - Xiaochuan Pan; Yu Zou; Mark A. Anastasio (IEEE Trans. Image Process., 2003)

Multiple-Image Computed Tomography. - Miles N. Wernick; Jovan G. Brankov; Dean Chapman; Mark A. Anastasio; Zhong Zhong; Carol Muehleman; Jun Li (ISBI, 2004)

Half-time image reconstruction in thermoacoustic tomography. - Mark A. Anastasio; Jin Zhang; Xiaochuan Pan; Yu Zou; Geng Ku; Lihong V. Wang (IEEE Trans. Medical Imaging, 2005)

Feasibility of half-data image reconstruction in 3-D reflectivity tomography with a spherical aperture. - Mark A. Anastasio; Jin Zhang; Emil Y. Sidky; Yu Zou; Dan Xia; Xiaochuan Pan (IEEE Trans. Medical Imaging, 2005)

Weighted expectation maximization reconstruction algorithms for thermoacoustic tomography. - Jin Zhang; Mark A. Anastasio; Xiaochuan Pan; Lihong V. Wang (IEEE Trans. Medical Imaging, 2005)

Progress in multiple-image radiography. - Miles N. Wernick; Jovan G. Brankov; Dean Chapman; Yongyi Yang; Gocha Khelashvili; Mark A. Anastasio; Zhong Zhong; Christopher Parham; Jun Li; Carol Muehleman (Computational Imaging, 2006)

Relationships Between Smooth- and Small-Phase Conditions in X-Ray Phase-Contrast Imaging. - Daxin Shi; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2009)

Effects of Different Imaging Models on Least-Squares Image Reconstruction Accuracy in Photoacoustic Tomography. - Jin Zhang; Mark A. Anastasio; Patrick J. La Rivière; Lihong V. Wang (IEEE Trans. Medical Imaging, 2009)

An Imaging Model Incorporating Ultrasonic Transducer Properties for Three-Dimensional Optoacoustic Tomography. - Kun Wang 020; Sergey A. Ermilov; Richard Su; Hans-Peter Brecht; Alexander A. Oraevsky; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2011)

Full-Wave Iterative Image Reconstruction in Photoacoustic Tomography With Acoustically Inhomogeneous Media. - Chao Huang 016; Kun Wang 020; Liming Nie; Lihong V. Wang; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2013)

Statistical Reconstruction of Material Decomposed Data in Spectral CT. - Carsten Oliver Schirra; Ewald Roessl; Thomas Köhler 003; Bernhard Brendel; Axel Thran; Dipanjan Pan; Mark A. Anastasio; Roland Proksa (IEEE Trans. Medical Imaging, 2013)

Proximal ADMM for Multi-Channel Image Reconstruction in Spectral X-ray CT. - Alex Sawatzky; Qiaofeng Xu; Carsten Oliver Schirra; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2014)

Discrete Imaging Models for Three-Dimensional Optoacoustic Tomography Using Radially Symmetric Expansion Functions. - Kun Wang 020; Robert W. Schoonover; Richard Su; Alexander A. Oraevsky; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2014)

Photoacoustic and Thermoacoustic Tomography: Image Formation Principles. - Kun Wang 020; Mark A. Anastasio (Handbook of Mathematical Methods in Imaging, 2015)

Joint Reconstruction of Absorbed Optical Energy Density and Sound Speed Distributions in Photoacoustic Computed Tomography: A Numerical Investigation. - Chao Huang 016; Kun Wang 020; Robert W. Schoonover; Lihong V. Wang; Mark A. Anastasio (IEEE Trans. Computational Imaging, 2016)

Deep Learning-Guided Image Reconstruction from Incomplete Data. - Brendan Kelly; Thomas P. Matthews; Mark A. Anastasio (CoRR, 2017)

A Forward-Adjoint Operator Pair Based on the Elastic Wave Equation for Use in Transcranial Photoacoustic Computed Tomography. - Kenji Mitsuhashi; Joemini Poudel; Thomas P. Matthews; Alejandro Garcia-Uribe; Lihong V. Wang; Mark A. Anastasio (SIAM J. Imaging Sci., 2017)

Active learning with noise modeling for medical image annotation. - Jian Wu 009; Su Ruan; Chunfeng Lian; Sasa Mutic; Mark A. Anastasio; Hua Li 003 (ISBI, 2018)

Heart motion tracking on cine MRI based on a deep Boltzmann machine-driven level set method. - Jian Wu 009; Su Ruan; Thomas R. Mazur; Nalini Daniel; Hilary Lashmett; Laura Ochoa; Imran Zoberi; Chunfeng Lian; H. Michael Gach; Sasa Mutic; Maria Thomas; Mark A. Anastasio; et al. (ISBI, 2018)

Convolutional neural network based automatic plaque characterization for intracoronary optical coherence tomography images. - Shenghua He; Jie Zheng; Akiko Maehara; Gary S. Mintz; Dalin Tang; Mark A. Anastasio; Hua Li 003 (Image Processing, 2018)

Learning the ideal observer for SKE detection tasks by use of convolutional neural networks. - Weimin Zhou; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2018)

Convolutional neural network based automatic plaque characterization from intracoronary optical coherence tomography images. - Shenghua He; Jie Zheng; Akiko Maehara; Gary S. Mintz; Dalin Tang; Mark A. Anastasio; Hua Li 003 (CoRR, 2018)

A deep Boltzmann machine-driven level set method for heart motion tracking using cine MRI images. - Jian Wu 009; Thomas R. Mazur; Su Ruan; Chunfeng Lian; Nalini Daniel; Hilary Lashmett; Laura Ochoa; Imran Zoberi; Mark A. Anastasio; H. Michael Gach; Sasa Mutic; Maria Thomas; et al. (Medical Image Anal., 2018)

Parameterized Joint Reconstruction of the Initial Pressure and Sound Speed Distributions for Photoacoustic Computed Tomography. - Thomas P. Matthews; Joemini Poudel; Lei Li 024; Lihong V. Wang; Mark A. Anastasio (SIAM J. Imaging Sci., 2018)

Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression. - Shenghua He; Kyaw Thu Minn; Lilianna Solnica-Krezel; Hua Li 003; Mark A. Anastasio (Digital Pathology, 2019)

Automatic microscopic cell counting by use of deeply-supervised density regression model. - Shenghua He; Kyaw Thu Minn; Lilianna Solnica-Krezel; Mark A. Anastasio; Hua Li 003 (Digital Pathology, 2019)

Autoencoder embedding of task-specific information. - Jason L. Granstedt; Weimin Zhou; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2019)

Learning the Hotelling observer for SKE detection tasks by use of supervised learning methods. - Weimin Zhou; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2019)

Learning the ideal observer for joint detection and localization tasks by use of convolutional neural networks. - Weimin Zhou; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2019)

Learning stochastic object model from noisy imaging measurements using AmbientGANs. - Weimin Zhou; Sayantan Bhadra; Frank J. Brooks; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2019)

A survey of computational frameworks for solving the acoustic inverse problem in three-dimensional photoacoustic computed tomography. - Joemini Poudel; Yang Lou; Mark A. Anastasio (CoRR, 2019)

Analysis of the Use of Unmatched Backward Operators in Iterative Image Reconstruction With Application to Three-Dimensional Optoacoustic Tomography. - Yang Lou; Seonyeong Park; Fatima Anis; Richard Su; Alexander A. Oraevsky; Mark A. Anastasio (IEEE Trans. Computational Imaging, 2019)

Reconstruction-Aware Imaging System Ranking by Use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference. - Yujia Chen 003; Yang Lou; Kun Wang 020; Matthew A. Kupinski; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2019)

Approximating the Ideal Observer and Hotelling Observer for Binary Signal Detection Tasks by Use of Supervised Learning Methods. - Weimin Zhou; Hua Li 003; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2019)

Learning efficient channels with a dual loss autoencoder. - Jason L. Granstedt; Weimin Zhou; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2020)

Learning numerical observers using unsupervised domain adaptation. - Shenghua He; Weimin Zhou; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2020)

Markov-Chain Monte Carlo approximation of the Ideal Observer using generative adversarial networks. - Weimin Zhou; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2020)

Progressively-Growing AmbientGANs for learning stochastic object models from imaging measurements. - Weimin Zhou; Sayantan Bhadra; Frank J. Brooks; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2020)

Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks. - Sayantan Bhadra; Weimin Zhou; Mark A. Anastasio (CoRR, 2020)

Approximating the Hotelling Observer with Autoencoder-Learned Efficient Channels for Binary Signal Detection Tasks. - Jason L. Granstedt; Weimin Zhou; Mark A. Anastasio (CoRR, 2020)

Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs. - Weimin Zhou; Sayantan Bhadra; Frank J. Brooks; Hua Li 003; Mark A. Anastasio (CoRR, 2020)

Approximating the Ideal Observer for Joint Signal Detection and Localization Tasks by use of Supervised Learning Methods. - Weimin Zhou; Hua Li 003; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2020)

Prior Image-Constrained Reconstruction using Style-Based Generative Models. - Varun A. Kelkar; Mark A. Anastasio (ICML, 2021)

SlabGAN: a method for generating efficient 3D anisotropic medical volumes using generative adversarial networks. - Jason L. Granstedt; Varun A. Kelkar; Weimin Zhou; Mark A. Anastasio (Image Processing, 2021)

Supervised learning-based ideal observer approximation for joint detection and estimation tasks. - Kaiyan Li 002; Weimin Zhou; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2021)

Task-based performance evaluation of deep neural network-based image denoising. - Kaiyan Li 002; Weimin Zhou; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2021)

Assessing regularization in tomographic imaging via hallucinations in the null space. - Sayantan Bhadra; Varun A. Kelkar; Frank J. Brooks; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2021)

Task-based evaluation of deep image super-resolution in medical imaging. - Varun A. Kelkar; Xiaohui Zhang 012; Jason L. Granstedt; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2021)

A hybrid channelized Hotelling observer for estimating the ideal linear observer for total-variation-based image reconstruction. - John Paul Phillips; Emil Y. Sidky; Greg Ongie; Weimin Zhou; Juan-Pablo Cruz-Bastida; Ingrid S. Reiser; Mark A. Anastasio; Xiaochuan Pan (Image Perception, Observer Performance, and Technology Assessment, 2021)

Advancing the AmbientGAN for learning stochastic object models. - Weimin Zhou; Sayantan Bhadra; Frank J. Brooks; Jason L. Granstedt; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2021)

Learning stochastic object models from medical imaging measurements by use of advanced AmbientGANs. - Weimin Zhou; Sayantan Bhadra; Frank J. Brooks; Hua Li 003; Mark A. Anastasio (CoRR, 2021)

Impact of deep learning-based image super-resolution on binary signal detection. - Xiaohui Zhang 012; Varun A. Kelkar; Jason L. Granstedt; Hua Li 003; Mark A. Anastasio (CoRR, 2021)

A Method for Evaluating the Capacity of Generative Adversarial Networks to Reproduce High-order Spatial Context. - Rucha Deshpande; Mark A. Anastasio; Frank J. Brooks (CoRR, 2021)

Deeply-supervised density regression for automatic cell counting in microscopy images. - Shenghua He; Kyaw Thu Minn; Lilianna Solnica-Krezel; Mark A. Anastasio; Hua Li 003 (Medical Image Anal., 2021)

Decoding visual information from high-density diffuse optical tomography neuroimaging data. - Kalyan Tripathy; Zachary E. Markow; Andrew K. Fishell; Arefeh Sherafati; Tracy M. Burns-Yocum; Mariel L. Schroeder; Alexandra M. Svoboda; Adam T. Eggebrecht; Mark A. Anastasio; Bradley L. Schlaggar; Joseph P. Culver (NeuroImage, 2021)

Compressible Latent-Space Invertible Networks for Generative Model-Constrained Image Reconstruction. - Varun A. Kelkar; Sayantan Bhadra; Mark A. Anastasio (IEEE Trans. Computational Imaging, 2021)

Assessing the Impact of Deep Neural Network-Based Image Denoising on Binary Signal Detection Tasks. - Kaiyan Li 002; Weimin Zhou; Hua Li 003; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2021)

On Hallucinations in Tomographic Image Reconstruction. - Sayantan Bhadra; Varun A. Kelkar; Frank J. Brooks; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2021)

Evaluating the capacity of deep generative models to reproduce measurable high-order spatial arrangements in diagnostic images. - Rucha Deshpande; Mark A. Anastasio; Frank J. Brooks (Image Processing, 2022)

Application of DatasetGAN in medical imaging: preliminary studies. - Zong Fan; Varun A. Kelkar; Mark A. Anastasio; Hua Li 003 (Image Processing, 2022)

A task-informed model training method for deep neural network-based image denoising. - Kaiyan Li 002; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2022)

Analyzing neural networks applied to an anatomical simulation of the breast. - Craig K. Abbey; Sourya Sengupta; Weimin Zhou; Andreu Badal; Rongping Zeng; Frank W. Samuelson; Miguel P. Eckstein; Kyle J. Myers; Mark A. Anastasio; Jovan G. Brankov (Image Perception, Observer Performance, and Technology Assessment, 2022)

Learned Hotelling observers for use with multi-modal data. - Jason L. Granstedt; Fu Li; Umberto Villa; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2022)

Evaluating procedures for establishing generative adversarial network-based stochastic image models in medical imaging. - Varun A. Kelkar; Dimitrios S. Gotsis; Frank J. Brooks; Kyle J. Myers; Prabhat K. C.; Rongping Zeng; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2022)

Investigation of adversarial robust training for establishing interpretable CNN-based numerical observers. - Sourya Sengupta; Craig K. Abbey; Kaiyan Li 002; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2022)

Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problems. - Sayantan Bhadra; Umberto Villa; Mark A. Anastasio (CoRR, 2022)

Prior image-based medical image reconstruction using a style-based generative adversarial network. - Varun A. Kelkar; Mark A. Anastasio (CoRR, 2022)

Investigating the robustness of a learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast measurements under laboratory conditions. - Rucha Deshpande; Ashish Avachat; Frank J. Brooks; Mark A. Anastasio (CoRR, 2022)

A Memory-Efficient Self-Supervised Dynamic Image Reconstruction Method Using Neural Fields. - Luke Lozenski; Mark A. Anastasio; Umberto Villa (IEEE Trans. Computational Imaging, 2022)

A Hybrid Approach for Approximating the Ideal Observer for Joint Signal Detection and Estimation Tasks by Use of Supervised Learning and Markov-Chain Monte Carlo Methods. - Kaiyan Li 002; Weimin Zhou; Hua Li 003; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2022)

Semi-supervised contrastive learning for white blood cell segmentation from label-free quantitative phase imaging. - Sourya Sengupta; Michael John Fanous; Hua Li 003; Mark A. Anastasio (Digital and Computational Pathology, 2023)

Estimating task-based performance bounds for image reconstruction methods by use of learned-ideal observers. - Kaiyan Li 002; Weimin Zhou; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2023)

Evaluating generative stochastic image models using task-based image quality measures. - Varun A. Kelkar; Dimitrios S. Gotsis; Rucha Deshpande; Frank J. Brooks; Prabhat K. C.; Kyle J. Myers; Rongping Zeng; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2023)

The stochastic digital human is now enrolling for in silico imaging trials - Methods and tools for generating digital cohorts. - Aldo Badano; Miguel A. Lago; Elena Sizikova; Jana G. Delfino; S. Guan; Mark A. Anastasio; Berkman Sahiner (CoRR, 2023)

Revisiting model self-interpretability in a decision-theoretic way for binary medical image classification. - Sourya Sengupta; Mark A. Anastasio (CoRR, 2023)

Assessing the Ability of Generative Adversarial Networks to Learn Canonical Medical Image Statistics. - Varun A. Kelkar; Dimitrios S. Gotsis; Frank J. Brooks; Prabhat K. C.; Kyle J. Myers; Rongping Zeng; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2023)

Ideal Observer Computation by Use of Markov-Chain Monte Carlo With Generative Adversarial Networks. - Weimin Zhou; Umberto Villa; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2023)

Report on the AAPM Grand Challenge on deep generative modeling for learning medical image statistics. - Rucha Deshpande; Varun A. Kelkar; Dimitrios S. Gotsis; Prabhat K. C.; Rongping Zeng; Kyle J. Myers; Frank J. Brooks; Mark A. Anastasio (CoRR, 2024)

Prior-guided Diffusion Model for Cell Segmentation in Quantitative Phase Imaging. - Zhuchen Shao; Mark A. Anastasio; Hua Li 003 (CoRR, 2024)

Physics and Deep Learning in Computational Wave Imaging. - Youzuo Lin; Shihang Feng; James Theiler; Yinpeng Chen; Umberto Villa; Jing Rao; John James Greenhall; Cristian Pantea; Mark A. Anastasio; Brendt Wohlberg (CoRR, 2024)

Optimizing Quantitative Photoacoustic Imaging Systems: The Bayesian Cramér-Rao Bound Approach. - Evan Scope Crafts; Mark A. Anastasio; Umberto Villa (CoRR, 2024)

Learning a Filtered Backprojection Reconstruction Method for Photoacoustic Computed Tomography with Hemispherical Measurement Geometries. - Panpan Chen; Seonyeong Park; Refik Mert Çam; Hsuan-Kai Huang; Alexander A. Oraevsky; Umberto Villa; Mark A. Anastasio (CoRR, 2024)

A method for evaluating deep generative models of images for hallucinations in high-order spatial context. - Rucha Deshpande; Mark A. Anastasio; Frank J. Brooks (Pattern Recognit. Lett., 2024)

ProxNF: Neural Field Proximal Training for High-Resolution 4D Dynamic Image Reconstruction. - Luke Lozenski; Refik Mert Çam; Mark D. Pagel; Mark A. Anastasio; Umberto Villa (IEEE Trans. Computational Imaging, 2024)

Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography. - Luke Lozenski; Hanchen Wang 003; Fu Li; Mark A. Anastasio; Brendt Wohlberg; Youzuo Lin; Umberto Villa (IEEE Trans. Computational Imaging, 2024)

AmbientFlow: Invertible generative models from incomplete, noisy measurements. - Varun A. Kelkar; Rucha Deshpande; Arindam Banerjee 001; Mark A. Anastasio (Trans. Mach. Learn. Res., 2024)

A Test Statistic Estimation-Based Approach for Establishing Self-Interpretable CNN-Based Binary Classifiers. - Sourya Sengupta; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2024)

High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models. - Ruiyang Zhao; Xi Peng 004; Varun A. Kelkar; Mark A. Anastasio; Fan Lam (IEEE Trans. Biomed. Eng., 2024)

Assessing the Capacity of a Denoising Diffusion Probabilistic Model to Reproduce Spatial Context. - Rucha Deshpande; Muzaffer Özbey; Hua Li 003; Mark A. Anastasio; Frank J. Brooks (IEEE Trans. Medical Imaging, 2024)

Full-Wave Image Reconstruction in Transcranial Photoacoustic Computed Tomography Using a Finite Element Method. - Yilin Luo; Hsuan-Kai Huang; Karteekeya Sastry; Peng Hu; Xin Tong; Joseph Kuo; Yousuf Abo Rahama; Shuai Na; Umberto Villa; Mark A. Anastasio; Lihong V. Wang (IEEE Trans. Medical Imaging, 2025)