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Mark A. Anastasio
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2020 – today
- 2024
- [j44]Rucha Deshpande
, Mark A. Anastasio
A method for evaluating deep generative models of images for hallucinations in high-order spatial context. Pattern Recognit. Lett. 186: 23-29 (2024) - [j43]Ruiyang Zhao
High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models. IEEE Trans. Biomed. Eng. 71(6): 1969-1979 (2024) - [j42]Luke Lozenski
Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography. IEEE Trans. Computational Imaging 10: 69-82 (2024) - [j41]Luke Lozenski
ProxNF: Neural Field Proximal Training for High-Resolution 4D Dynamic Image Reconstruction. IEEE Trans. Computational Imaging 10: 1368-1383 (2024) - [j40]Sourya Sengupta
A Test Statistic Estimation-Based Approach for Establishing Self-Interpretable CNN-Based Binary Classifiers. IEEE Trans. Medical Imaging 43(5): 1753-1765 (2024) - [j39]Rucha Deshpande
Assessing the Capacity of a Denoising Diffusion Probabilistic Model to Reproduce Spatial Context. IEEE Trans. Medical Imaging 43(10): 3608-3620 (2024) - [j38]Varun A. Kelkar, Rucha Deshpande, Arindam Banerjee, Mark A. Anastasio:
AmbientFlow: Invertible generative models from incomplete, noisy measurements. Trans. Mach. Learn. Res. 2024 (2024) - [i37]Rucha Deshpande, Varun A. Kelkar, Dimitrios S. Gotsis, Prabhat K. C., Rongping Zeng, Kyle J. Myers, Frank J. Brooks, Mark A. Anastasio:
Report on the AAPM Grand Challenge on deep generative modeling for learning medical image statistics. CoRR abs/2405.01822 (2024) - [i36]Zhuchen Shao, Mark A. Anastasio, Hua Li:
Prior-guided Diffusion Model for Cell Segmentation in Quantitative Phase Imaging. CoRR abs/2405.06175 (2024) - [i35]Youzuo Lin, Shihang Feng, James Theiler, Yinpeng Chen, Umberto Villa, Jing Rao, John James Greenhall, Cristian Pantea, Mark A. Anastasio, Brendt Wohlberg:
Physics and Deep Learning in Computational Wave Imaging. CoRR abs/2410.08329 (2024) - [i34]Evan Scope Crafts, Mark A. Anastasio, Umberto Villa:
Optimizing Quantitative Photoacoustic Imaging Systems: The Bayesian Cramér-Rao Bound Approach. CoRR abs/2410.09557 (2024) - 2023
- [j37]Varun A. Kelkar, Dimitrios S. Gotsis, Frank J. Brooks, Prabhat K. C.
Assessing the Ability of Generative Adversarial Networks to Learn Canonical Medical Image Statistics. IEEE Trans. Medical Imaging 42(6): 1799-1808 (2023) - [j36]Weimin Zhou
Ideal Observer Computation by Use of Markov-Chain Monte Carlo With Generative Adversarial Networks. IEEE Trans. Medical Imaging 42(12): 3715-3724 (2023) - [c39]Sourya Sengupta, Michael John Fanous, Hua Li, Mark A. Anastasio:
Semi-supervised contrastive learning for white blood cell segmentation from label-free quantitative phase imaging. Digital and Computational Pathology 2023 - [c38]Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio:
Estimating task-based performance bounds for image reconstruction methods by use of learned-ideal observers. Image Perception, Observer Performance, and Technology Assessment 2023 - [c37]Varun A. Kelkar, Dimitrios S. Gotsis, Rucha Deshpande, Frank J. Brooks, Prabhat K. C., Kyle J. Myers, Rongping Zeng, Mark A. Anastasio:
Evaluating generative stochastic image models using task-based image quality measures. Image Perception, Observer Performance, and Technology Assessment 2023 - [i33]Aldo Badano, Miguel A. Lago, Elena Sizikova, Jana G. Delfino, S. Guan, Mark A. Anastasio, Berkman Sahiner:
The stochastic digital human is now enrolling for in silico imaging trials - Methods and tools for generating digital cohorts. CoRR abs/2301.08719 (2023) - [i32]Sourya Sengupta, Mark A. Anastasio:
Revisiting model self-interpretability in a decision-theoretic way for binary medical image classification. CoRR abs/2303.06876 (2023) - [i31]Weimin Zhou, Umberto Villa, Mark A. Anastasio:
Ideal Observer Computation by Use of Markov-Chain Monte Carlo with Generative Adversarial Networks. CoRR abs/2304.00433 (2023) - [i30]Ruiyang Zhao, Xi Peng, Varun A. Kelkar, Mark A. Anastasio, Fan Lam:
High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models. CoRR abs/2306.08630 (2023) - [i29]Varun A. Kelkar, Rucha Deshpande, Arindam Banerjee, Mark A. Anastasio:
AmbientFlow: Invertible generative models from incomplete, noisy measurements. CoRR abs/2309.04856 (2023) - [i28]Rucha Deshpande, Muzaffer Özbey, Hua Li, Mark A. Anastasio, Frank J. Brooks:
Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context. CoRR abs/2309.10817 (2023) - 2022
- [j35]Luke Lozenski
A Memory-Efficient Self-Supervised Dynamic Image Reconstruction Method Using Neural Fields. IEEE Trans. Computational Imaging 8: 879-892 (2022) - [j34]Kaiyan Li
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. IEEE Trans. Medical Imaging 41(5): 1114-1124 (2022) - [c36]Rucha Deshpande, Mark A. Anastasio, Frank J. Brooks:
Evaluating the capacity of deep generative models to reproduce measurable high-order spatial arrangements in diagnostic images. Image Processing 2022 - [c35]Zong Fan, Varun A. Kelkar, Mark A. Anastasio, Hua Li:
Application of DatasetGAN in medical imaging: preliminary studies. Image Processing 2022 - [c34]Kaiyan Li, Hua Li, Mark A. Anastasio:
A task-informed model training method for deep neural network-based image denoising. Image Perception, Observer Performance, and Technology Assessment 2022 - [c33]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
Analyzing neural networks applied to an anatomical simulation of the breast. Image Perception, Observer Performance, and Technology Assessment 2022 - [c32]Jason L. Granstedt, Fu Li
Learned Hotelling observers for use with multi-modal data. Image Perception, Observer Performance, and Technology Assessment 2022 - [c31]Varun A. Kelkar, Dimitrios S. Gotsis, Frank J. Brooks, Kyle J. Myers, Prabhat K. C., Rongping Zeng, Mark A. Anastasio:
Evaluating procedures for establishing generative adversarial network-based stochastic image models in medical imaging. Image Perception, Observer Performance, and Technology Assessment 2022 - [c30]Sourya Sengupta, Craig K. Abbey, Kaiyan Li, Mark A. Anastasio:
Investigation of adversarial robust training for establishing interpretable CNN-based numerical observers. Image Perception, Observer Performance, and Technology Assessment 2022 - [i27]Sayantan Bhadra, Umberto Villa, Mark A. Anastasio:
Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problems. CoRR abs/2202.05311 (2022) - [i26]Varun A. Kelkar, Mark A. Anastasio:
Prior image-based medical image reconstruction using a style-based generative adversarial network. CoRR abs/2202.08936 (2022) - [i25]Zong Fan, Varun A. Kelkar, Mark A. Anastasio, Hua Li:
Application of DatasetGAN in medical imaging: preliminary studies. CoRR abs/2202.13463 (2022) - [i24]Varun A. Kelkar, Dimitrios S. Gotsis, Frank J. Brooks, Kyle J. Myers, Prabhat K. C., Rongping Zeng, Mark A. Anastasio:
Evaluating Procedures for Establishing Generative Adversarial Network-based Stochastic Image Models in Medical Imaging. CoRR abs/2204.03547 (2022) - [i23]Varun A. Kelkar, Dimitrios S. Gotsis, Frank J. Brooks, Prabhat K. C., Kyle J. Myers, Rongping Zeng, Mark A. Anastasio:
Assessing the ability of generative adversarial networks to learn canonical medical image statistics. CoRR abs/2204.12007 (2022) - [i22]Rucha Deshpande, Ashish Avachat, Frank J. Brooks, Mark A. Anastasio:
Investigating the robustness of a learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast measurements under laboratory conditions. CoRR abs/2211.01372 (2022) - 2021
- [j33]Shenghua He, Kyaw Thu Minn
Deeply-supervised density regression for automatic cell counting in microscopy images. Medical Image Anal. 68: 101892 (2021) - [j32]Kalyan Tripathy, Zachary E. Markow
Decoding visual information from high-density diffuse optical tomography neuroimaging data. NeuroImage 226: 117516 (2021) - [j31]Varun A. Kelkar, Sayantan Bhadra, Mark A. Anastasio
Compressible Latent-Space Invertible Networks for Generative Model-Constrained Image Reconstruction. IEEE Trans. Computational Imaging 7: 209-223 (2021) - [j30]Kaiyan Li
Assessing the Impact of Deep Neural Network-Based Image Denoising on Binary Signal Detection Tasks. IEEE Trans. Medical Imaging 40(9): 2295-2305 (2021) - [j29]Sayantan Bhadra
On Hallucinations in Tomographic Image Reconstruction. IEEE Trans. Medical Imaging 40(11): 3249-3260 (2021) - [c29]Varun A. Kelkar, Mark A. Anastasio:
Prior Image-Constrained Reconstruction using Style-Based Generative Models. ICML 2021: 5367-5377 - [c28]Jason L. Granstedt, Varun A. Kelkar, Weimin Zhou, Mark A. Anastasio:
SlabGAN: a method for generating efficient 3D anisotropic medical volumes using generative adversarial networks. Image Processing 2021 - [c27]Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio:
Supervised learning-based ideal observer approximation for joint detection and estimation tasks. Image Perception, Observer Performance, and Technology Assessment 2021 - [c26]Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio:
Task-based performance evaluation of deep neural network-based image denoising. Image Perception, Observer Performance, and Technology Assessment 2021 - [c25]Sayantan Bhadra, Varun A. Kelkar, Frank J. Brooks, Mark A. Anastasio:
Assessing regularization in tomographic imaging via hallucinations in the null space. Image Perception, Observer Performance, and Technology Assessment 2021 - [c24]Varun A. Kelkar, Xiaohui Zhang
Task-based evaluation of deep image super-resolution in medical imaging. Image Perception, Observer Performance, and Technology Assessment 2021 - [c23]John Paul Phillips, Emil Y. Sidky, Greg Ongie, Weimin Zhou, Juan-Pablo Cruz-Bastida
A hybrid channelized Hotelling observer for estimating the ideal linear observer for total-variation-based image reconstruction. Image Perception, Observer Performance, and Technology Assessment 2021 - [c22]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Jason L. Granstedt, Hua Li, Mark A. Anastasio:
Advancing the AmbientGAN for learning stochastic object models. Image Perception, Observer Performance, and Technology Assessment 2021 - [i21]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Jason L. Granstedt, Hua Li, Mark A. Anastasio:
Advancing the AmbientGAN for learning stochastic object models. CoRR abs/2102.00281 (2021) - [i20]Varun A. Kelkar, Mark A. Anastasio:
Prior Image-Constrained Reconstruction using Style-Based Generative Models. CoRR abs/2102.12525 (2021) - [i19]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio:
Learning stochastic object models from medical imaging measurements by use of advanced AmbientGANs. CoRR abs/2106.14324 (2021) - [i18]Xiaohui Zhang, Varun A. Kelkar, Jason L. Granstedt, Hua Li, Mark A. Anastasio:
Impact of deep learning-based image super-resolution on binary signal detection. CoRR abs/2107.02338 (2021) - [i17]Rucha Deshpande, Mark A. Anastasio, Frank J. Brooks:
A Method for Evaluating the Capacity of Generative Adversarial Networks to Reproduce High-order Spatial Context. CoRR abs/2111.12577 (2021) - 2020
- [j28]Weimin Zhou
Approximating the Ideal Observer for Joint Signal Detection and Localization Tasks by use of Supervised Learning Methods. IEEE Trans. Medical Imaging 39(12): 3992-4000 (2020) - [c21]Jason L. Granstedt, Weimin Zhou, Mark A. Anastasio:
Learning efficient channels with a dual loss autoencoder. Image Perception, Observer Performance, and Technology Assessment 2020: 113160C - [c20]Shenghua He, Weimin Zhou, Hua Li, Mark A. Anastasio:
Learning numerical observers using unsupervised domain adaptation. Image Perception, Observer Performance, and Technology Assessment 2020: 113160W - [c19]Weimin Zhou, Mark A. Anastasio:
Markov-Chain Monte Carlo approximation of the Ideal Observer using generative adversarial networks. Image Perception, Observer Performance, and Technology Assessment 2020: 113160D - [c18]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio:
Progressively-Growing AmbientGANs for learning stochastic object models from imaging measurements. Image Perception, Observer Performance, and Technology Assessment 2020: 113160Q - [i16]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio:
Progressively-Growing AmbientGANs For Learning Stochastic Object Models From Imaging Measurements. CoRR abs/2001.09523 (2020) - [i15]Weimin Zhou, Mark A. Anastasio:
Markov-Chain Monte Carlo Approximation of the Ideal Observer using Generative Adversarial Networks. CoRR abs/2001.09526 (2020) - [i14]Sayantan Bhadra, Weimin Zhou, Mark A. Anastasio:
Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks. CoRR abs/2001.10830 (2020) - [i13]Shenghua He, Weimin Zhou, Hua Li, Mark A. Anastasio:
Learning Numerical Observers using Unsupervised Domain Adaptation. CoRR abs/2002.03763 (2020) - [i12]Jason L. Granstedt, Weimin Zhou, Mark A. Anastasio:
Approximating the Hotelling Observer with Autoencoder-Learned Efficient Channels for Binary Signal Detection Tasks. CoRR abs/2003.02321 (2020) - [i11]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio:
Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs. CoRR abs/2006.00033 (2020) - [i10]Weimin Zhou, Hua Li, Mark A. Anastasio:
Approximating the Ideal Observer for joint signal detection and localization tasks by use of supervised learning methods. CoRR abs/2006.00112 (2020) - [i9]Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Mark A. Anastasio, Hua Li:
Deeply-Supervised Density Regression for Automatic Cell Counting in Microscopy Images. CoRR abs/2011.03683 (2020) - [i8]Sayantan Bhadra, Varun A. Kelkar, Frank J. Brooks, Mark A. Anastasio:
On hallucinations in tomographic image reconstruction. CoRR abs/2012.00646 (2020)
2010 – 2019
- 2019
- [j27]Yang Lou, Seonyeong Park
Analysis of the Use of Unmatched Backward Operators in Iterative Image Reconstruction With Application to Three-Dimensional Optoacoustic Tomography. IEEE Trans. Computational Imaging 5(3): 437-449 (2019) - [j26]Yujia Chen
Reconstruction-Aware Imaging System Ranking by Use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference. IEEE Trans. Medical Imaging 38(5): 1251-1262 (2019) - [j25]Weimin Zhou
Approximating the Ideal Observer and Hotelling Observer for Binary Signal Detection Tasks by Use of Supervised Learning Methods. IEEE Trans. Medical Imaging 38(10): 2456-2468 (2019) - [c17]Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Mark A. Anastasio, Hua Li:
Automatic microscopic cell counting by use of deeply-supervised density regression model. Digital Pathology 2019: 109560L - [c16]Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Hua Li, Mark A. Anastasio:
Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression. Digital Pathology 2019: 1095604 - [c15]Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Mark A. Anastasio:
Learning stochastic object model from noisy imaging measurements using AmbientGANs. Image Perception, Observer Performance, and Technology Assessment 2019: 109520M - [c14]Jason L. Granstedt, Weimin Zhou, Mark A. Anastasio:
Autoencoder embedding of task-specific information. Image Perception, Observer Performance, and Technology Assessment 2019: 1095207 - [c13]Weimin Zhou, Hua Li, Mark A. Anastasio:
Learning the Hotelling observer for SKE detection tasks by use of supervised learning methods. Image Perception, Observer Performance, and Technology Assessment 2019: 1095208 - [c12]Weimin Zhou, Mark A. Anastasio:
Learning the ideal observer for joint detection and localization tasks by use of convolutional neural networks. Image Perception, Observer Performance, and Technology Assessment 2019: 1095209 - [i7]Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Hua Li, Mark A. Anastasio:
Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression. CoRR abs/1903.00388 (2019) - [i6]Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Mark A. Anastasio, Hua Li:
Automatic microscopic cell counting by use of deeply-supervised density regression model. CoRR abs/1903.01084 (2019) - [i5]Joemini Poudel, Yang Lou, Mark A. Anastasio:
A survey of computational frameworks for solving the acoustic inverse problem in three-dimensional photoacoustic computed tomography. CoRR abs/1905.03881 (2019) - [i4]Yujia Chen, Yang Lou, Kun Wang, Matthew A. Kupinski, Mark A. Anastasio:
Reconstruction-Aware Imaging System Ranking by use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference. CoRR abs/1905.05820 (2019) - [i3]Weimin Zhou, Hua Li, Mark A. Anastasio:
Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods. CoRR abs/1905.06330 (2019) - 2018
- [j24]Jian Wu, Thomas R. Mazur, Su Ruan
A deep Boltzmann machine-driven level set method for heart motion tracking using cine MRI images. Medical Image Anal. 47: 68-80 (2018) - [j23]Thomas P. Matthews, Joemini Poudel, Lei Li, Lihong V. Wang, Mark A. Anastasio
Parameterized Joint Reconstruction of the Initial Pressure and Sound Speed Distributions for Photoacoustic Computed Tomography. SIAM J. Imaging Sci. 11(2): 1560-1588 (2018) - [c11]Jian Wu, Su Ruan
Active learning with noise modeling for medical image annotation. ISBI 2018: 298-301 - [c10]Jian Wu, Su Ruan
Heart motion tracking on cine MRI based on a deep Boltzmann machine-driven level set method. ISBI 2018: 1153-1156 - [c9]Shenghua He, Jie Zheng, Akiko Maehara, Gary S. Mintz, Dalin Tang, Mark A. Anastasio, Hua Li:
Convolutional neural network based automatic plaque characterization for intracoronary optical coherence tomography images. Image Processing 2018: 1057432 - [c8]Weimin Zhou, Mark A. Anastasio:
Learning the ideal observer for SKE detection tasks by use of convolutional neural networks. Image Perception, Observer Performance, and Technology Assessment 2018: 1057719 - [i2]Shenghua He, Jie Zheng, Akiko Maehara, Gary S. Mintz, Dalin Tang, Mark A. Anastasio, Hua Li:
Convolutional neural network based automatic plaque characterization from intracoronary optical coherence tomography images. CoRR abs/1807.03613 (2018) - 2017
- [j22]Kenji Mitsuhashi, Joemini Poudel, Thomas P. Matthews, Alejandro Garcia-Uribe, Lihong V. Wang, Mark A. Anastasio:
A Forward-Adjoint Operator Pair Based on the Elastic Wave Equation for Use in Transcranial Photoacoustic Computed Tomography. SIAM J. Imaging Sci. 10(4): 2022-2048 (2017) - [i1]Brendan Kelly, Thomas P. Matthews, Mark A. Anastasio:
Deep Learning-Guided Image Reconstruction from Incomplete Data. CoRR abs/1709.00584 (2017) - 2016
- [j21]Chao Huang, Kun Wang, Robert W. Schoonover, Lihong V. Wang, Mark A. Anastasio:
Joint Reconstruction of Absorbed Optical Energy Density and Sound Speed Distributions in Photoacoustic Computed Tomography: A Numerical Investigation. IEEE Trans. Computational Imaging 2(2): 136-149 (2016) - 2015
- [r1]Kun Wang, Mark A. Anastasio:
Photoacoustic and Thermoacoustic Tomography: Image Formation Principles. Handbook of Mathematical Methods in Imaging 2015: 1081-1116 - 2014
- [j20]Kun Wang, Robert W. Schoonover, Richard Su, Alexander A. Oraevsky
Discrete Imaging Models for Three-Dimensional Optoacoustic Tomography Using Radially Symmetric Expansion Functions. IEEE Trans. Medical Imaging 33(5): 1180-1193 (2014) - [j19]Alex Sawatzky, Qiaofeng Xu, Carsten Oliver Schirra, Mark A. Anastasio:
Proximal ADMM for Multi-Channel Image Reconstruction in Spectral X-ray CT. IEEE Trans. Medical Imaging 33(8): 1657-1668 (2014) - 2013
- [j18]Chao Huang, Kun Wang, Liming Nie, Lihong V. Wang
Full-Wave Iterative Image Reconstruction in Photoacoustic Tomography With Acoustically Inhomogeneous Media. IEEE Trans. Medical Imaging 32(6): 1097-1110 (2013) - [j17]Carsten Oliver Schirra, Ewald Roessl, Thomas Köhler, Bernhard Brendel, Axel Thran, Dipanjan Pan
Statistical Reconstruction of Material Decomposed Data in Spectral CT. IEEE Trans. Medical Imaging 32(7): 1249-1257 (2013) - 2011
- [j16]Kun Wang, Sergey A. Ermilov, Richard Su, Hans-Peter Brecht, Alexander A. Oraevsky
An Imaging Model Incorporating Ultrasonic Transducer Properties for Three-Dimensional Optoacoustic Tomography. IEEE Trans. Medical Imaging 30(2): 203-214 (2011)
2000 – 2009
- 2009
- [j15]Jin Zhang, Mark A. Anastasio, Patrick J. La Rivière, Lihong V. Wang
Effects of Different Imaging Models on Least-Squares Image Reconstruction Accuracy in Photoacoustic Tomography. IEEE Trans. Medical Imaging 28(11): 1781-1790 (2009) - [j14]Daxin Shi, Mark A. Anastasio:
Relationships Between Smooth- and Small-Phase Conditions in X-Ray Phase-Contrast Imaging. IEEE Trans. Medical Imaging 28(12): 1969-1973 (2009) - 2006
- [c7]Miles N. Wernick, Jovan G. Brankov, Dean Chapman, Yongyi Yang, Gocha Khelashvili, Mark A. Anastasio, Zhong Zhong, Christopher Parham, Jun Li, Carol Muehleman:
Progress in multiple-image radiography. Computational Imaging 2006: 60650W - 2005
- [j13]Mark A. Anastasio, Jin Zhang, Xiaochuan Pan, Yu Zou, Geng Ku, Lihong V. Wang
Half-time image reconstruction in thermoacoustic tomography. IEEE Trans. Medical Imaging 24(2): 199-210 (2005) - [j12]Jin Zhang, Mark A. Anastasio, Xiaochuan Pan, Lihong V. Wang
Weighted expectation maximization reconstruction algorithms for thermoacoustic tomography. IEEE Trans. Medical Imaging 24(6): 817-820 (2005) - [j11]Mark A. Anastasio, Jin Zhang, Emil Y. Sidky, Yu Zou, Dan Xia, Xiaochuan Pan:
Feasibility of half-data image reconstruction in 3-D reflectivity tomography with a spherical aperture. IEEE Trans. Medical Imaging 24(9): 1100-1112 (2005) - 2004
- [c6]Miles N. Wernick, Jovan G. Brankov, Dean Chapman, Mark A. Anastasio, Zhong Zhong, Carol Muehleman, Jun Li:
Multiple-Image Computed Tomography. ISBI 2004: 948-951 - 2003
- [j10]Mark A. Anastasio, Xiaochuan Pan:
An improved reconstruction algorithm for 3-D diffraction tomography using spherical-wave sources. IEEE Trans. Biomed. Eng. 50(4): 517-521 (2003) - [j9]Xiaochuan Pan, Yu Zou, Mark A. Anastasio:
Data redundancy and reduced-scan reconstruction in reflectivity tomography. IEEE Trans. Image Process. 12(7): 784-795 (2003) - [c5]Daxin Shi, Mark A. Anastasio, Xiaochuan Pan, Charles A. Pelizzari, Peter Munro:
Investigation of megavoltage local tomography for detecting setup errors in radiation therapy. Image-Guided Procedures 2003 - 2002
- [j8]Mark A. Anastasio, Xiaochuan Pan:
Numerically robust minimal-scan reconstruction algorithms for diffraction tomography via radon transform inversion. Int. J. Imaging Syst. Technol. 12(2): 84-91 (2002) - [j7]Xiaochuan Pan, Mark A. Anastasio:
On a limited-view reconstruction problem in wavefield tomography. IEEE Trans. Medical Imaging 21(4): 413-416 (2002) - [c4]Xiaochuan Pan, Yu Zou, Mark A. Anastasio:
Image reconstruction of reflectivity from short scan data. ISBI 2002: 1027-1030 - 2001
- [j6]Mark A. Anastasio, Xiaochuan Pan, Eric Clarkson:
Comments on the Filtered Backprojection Algorithm, Range Conditions, and the Pseudoinverse Solution. IEEE Trans. Medical Imaging 20(6): 539-542 (2001) - [c3]Mark A. Anastasio, Xiaochuan Pan:
Development and evaluation of minimal-scan reconstruction algorithms for diffraction tomography. Image Processing 2001 - 2000
- [j5]Mark A. Anastasio, Xiaochuan Pan:
A new reconstruction approach for reflection mode diffraction tomography. IEEE Trans. Image Process. 9(7): 1262-1271 (2000) - [c2]Matthew A. Kupinski, Mark A. Anastasio, Maryellen Lissak Giger:
Multiobjective genetic optimization of diagnostic classifiers used in the computerized detection of mass lesions in mammography. Image Processing 2000
1990 – 1999
- 1999
- [j4]Mark A. Anastasio, Xiaochuan Pan:
Investigation of the noise properties of a new class of reconstruction methods in diffraction tomography. Int. J. Imaging Syst. Technol. 10(6): 437-446 (1999) - [j3]Mark A. Anastasio, Xiaochuan Pan, Chien-Min Kao
Multidimensional smoothing using orthogonal expansions. IEEE Signal Process. Lett. 6(4): 91-94 (1999) - [j2]Matthew A. Kupinski, Mark A. Anastasio:
Multiobjective Genetic Optimization of Diagnostic Classifiers with Implications for Generating ROC Curves. IEEE Trans. Medical Imaging 18(8): 675-685 (1999) - 1998
- [j1]Mark A. Anastasio, Matthew A. Kupinski, Robert M. Nishikawa
Optimization and FROC Analysis of Rule-Based Detection Schemes Using a Multiobjective Approach. IEEE Trans. Medical Imaging 17(6): 1089-1093 (1998) - [c1]Mark A. Anastasio, Xiaochuan Pan, Chien-Min Kao:
A General Technique for Smoothing Multi-Dimensional Datasets Utilizing Orthogonal Expansions and Lower Dimensional Smoothers. ICIP (2) 1998: 718-721
Coauthor Index
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