Recent Publications

2024

  1. X. Zhang, M. Tan, M. Nabil, R. Shukla, S. Vasavada, S. Anandasabapathy, M.A. Anastasio and E. Petrova, “Deep learning-based image super-resolution of a novel end-expandable optical fiber probe for application in esophageal cancer diagnostics“, Journal of Biomedical Optics, 29(4), 046001 (2024), doi: 10.1117/1.JBO.29.4.046001.
  2. H. K. Huang, J. Kuo, S. Park, U. Villa,  L. V. Wang, and M. A. Anastasio, “A learning-based image reconstruction method for skull-induced aberration compensation in transcranial photoacoustic computed tomography“,  In Photons Plus Ultrasound: Imaging and Sensing 2024. SPIE (2024)
  3. R. M. Cam, S. Park, U. Villa, and M. A. Anastasio, “Investigation of a learned image reconstruction method for three-dimensional quantitative photoacoustic tomography of the breast“, In Photons Plus Ultrasound: Imaging and Sensing 2024. SPIE (2024).
  4. P. Chen, S. Park, R. M. Cam, H. K. Huang, U. Villa, and M. A. Anastasio, ” Learning a semi-analytic reconstruction method for photoacoustic computed tomography with hemispherical measurement geometries“, In Photons Plus Ultrasound: Imaging and Sensing 2024. SPIE (2024)
  5. Y. Shin, M.R. Lowerison, Y. Wang, X. Chen, Q. You, Z. Dong, M.A. Anastasio, and P. Song, “Context-Aware Deep Learning Enables High-Efficacy Localization of High Concentration Microbubbles for Super-Resolution Ultrasound Localization Microscopy”,  Nature Communications, In press (2024)
  6. N. Goswami, N. Winston, W. Choi, N. Z. E. Lai, R. B. Arcanjo, X. Chen, N. Sobh, R. A. Nowak, M. A. Anastasio and G. Popescu, “EVATOM: a novel embryo health assessment tool”, Communications Biology, in press (2024).
  7. S. Subramaniam, M. Akay, M. A. Anastasio et al, “Grand Challenges at the Interface of Engineering and Medicine“,  IEEE Open Journal of Engineering in Medicine and Biology (2024).
  8. K. Li, U. Villa, H. Li, and M. A. Anastasio, “Application of Learned Ideal Observers for Estimating Task-Based Performance Bounds for Computed Imaging Systems”, Journal of Medical Imaging, in press (2024)
  9. R. M. Cam, C. Wang,  W. Thompson, S. A. Ermilov, M. A. Anastasio and U. Villa, “Spatiotemporal image reconstruction to enable high-frame-rate dynamic photoacoustic tomography with rotating-gantry volumetric imagers“, Journal of Biomedical Optics (2024).
  10. S. Sengupta and M. A. Anastasio, “A Test Statistic Estimation-based Approach for Establishing Self-interpretable CNN-based Binary Classifiers“,  IEEE Transactions on Medical Imaging (2024).
  11. L. Lozenski, H. Wang, F. Li, M. Anastasio, B. Wohlberg, Y. Lin, and U. Villa, “Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography“,  IEEE Transactions on Computational Imaging (2024).

2023

  1. G. Jeong, F. Li, U. Villa and M. A. Anastasio, “Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography,” arXiv (2023).
  2. V. A. Kelkar, R. Deshpande, A. Banerjee, and M. Anastasio. “AmbientFlow: Invertible generative models from incomplete, noisy imaging measurements.” In NeurIPS 2023 Workshop on Deep Learning and Inverse Problems. 2023.
  3. J. L. Granstedt, W. Zhou, and M. A. Anastasio. “Approximating the Hotelling observer with autoencoder-learned efficient channels for binary signal detection tasks.” Journal of Medical Imaging 10.5 (2023): 055501-055501.
  4. F. Li, U. Villa, N. Duric and M. A. Anastasio, “A forward model incorporating elevation-focused transducer properties for 3D full-waveform inversion in ultrasound computed tomography,” in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (2023).
  5. W. Zhou, U. Villa and M. A. Anastasio, “Ideal Observer Computation by Use of Markov-Chain Monte Carlo with Generative Adversarial Networks,” in IEEE Transactions on Medical Imaging (2023).
  6. Q. You, M. R. Lowerison, Y. Shin, X. Chen, N. V. C. Sekaran, Z. Dong, D. A. Llano, M. A. Anastasio, and P. Song. “Contrast-free Super-resolution Power Doppler (CS-PD) based on Deep Neural Networks.” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (2023).
  7. S. Park, U. Villa, F. Li, R. M. Cam, A. A. Oraevsky, and M. A. Anastasio. “Stochastic three-dimensional numerical phantoms to enable computational studies in quantitative optoacoustic computed tomography of breast cancer.” Journal of Biomedical Optics 28, no. 6 (2023): 066002-066002.
  8. F. Li, U. Villa, N. Duric, and M. Anastasio. “3D full-waveform inversion in ultrasound computed tomography employing a ring-array.” In Proc. of SPIE Vol, vol. 12470, pp. 124700K-1. 2023.
  9. G. Jeong, F. Li, U. Villa, and M. A. Anastasio. “A deep learning-based image reconstruction method for USCT that employs multimodality inputs.” In Medical Imaging 2023: Ultrasonic Imaging and Tomography, vol. 12470, pp. 105-110. SPIE, 2023.
  10. A. J. Zhai, J. Kuo, M. A. Anastasio, and U. Villa. “Memory-efficient self-supervised learning of null space projection operators.” In Medical Imaging 2023: Physics of Medical Imaging, vol. 12463, pp. 311-317. SPIE, 2023.
  11. R. M. Cam, C. Wang, W. Thompson, S. A. Ermilov, M. A. Anastasio, and U. Villa. “Low-rank matrix estimation-based spatiotemporal image reconstruction from few tomographic measurements per frame for dynamic photoacoustic computed tomography.” In Medical Imaging 2023: Physics of Medical Imaging, vol. 12463, p. 124630R. SPIE, 2023.
  12. R. Deshpande, A. Avachat, F. J. Brooks, and M. A. Anastasio. “Assessing the applicability of a learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast images under benchtop conditions.” In Medical Imaging 2023: Physics of Medical Imaging, vol. 12463, p. 124632T. SPIE, 2023.
  13. S. Sengupta, M. Fanous, H. Li, and M. A. Anastasio. “Semi-supervised contrastive learning for white blood cell segmentation from label-free quantitative phase imaging.” In Medical Imaging 2023: Digital and Computational Pathology, vol. 12471, pp. 90-95. SPIE, 2023.
  14. K.  Li, W. Zhou, H. Li, and M. A. Anastasio. “Estimating task-based performance bounds for image reconstruction methods by use of learned-ideal observers.” In Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment, vol. 12467, pp. 120-125. SPIE, 2023.
  15. V. A. Kelkar, D. S. Gotsis, R. Deshpande, F. J. Brooks, K. C. Prabhat, K. J. Myers, R. Zeng, and M. A. Anastasio. “Evaluating generative stochastic image models using task-based image quality measures.” In Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment, vol. 12467, pp. 304-310. SPIE, 2023.
  16. Z. E. Markow, K. Tripathy, J. W. Trobaugh, A. M. Svoboda, M. L. Schroeder, S. M. Rafferty, E J. Richter, A. T. Eggebrecht, M. A. Anastasio, and J. P. Culver. “Template-and model-based decoding of movie identities with high-density diffuse optical tomography of neural hemodynamics.” In Neural Imaging and Sensing 2023, p. PC1236503. SPIE, 2023.
  17. W. Fehner, M. Fogarty, M. A. Anastasio, and J. P. Culver. “Evaluation of multivariate approaches to functional connectivity mapping with fNIRS.” In Neural Imaging and Sensing 2023, p. PC123650C. SPIE, 2023.
  18. X. Zhang, E. C. Landsness, J. P. Culver, J. M. Lee, and M. A. Anastasio. “Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data“, Proc. SPIE 12365, Neural Imaging and Sensing 2023, 123650B.
  19. S. Park, U. Villa, A. Oraevsky, and M. Anastasio. “Numerical investigation of impact of skin phototype on three-dimensional optoacoustic tomography of the breast.” In Photons Plus Ultrasound: Imaging and Sensing 2023, p. PC123790E. SPIE, 2023.
  20. R. M. Cam, C. Wang, S. Park, W. Thompson, S. A. Ermilov, M. A. Anastasio, and U. Villa. “Dynamic image reconstruction to monitor tumor vascular perfusion in small animals using 3D photoacoustic computed-tomography imagers with rotating gantries.” In Photons Plus Ultrasound: Imaging and Sensing 2023, vol. 12379, pp. 78-83. SPIE, 2023.
  21. M. A. Anastasio “Deep learning and photoacoustic image formation: promises and challenges.” Photons Plus Ultrasound: Imaging and Sensing 2023. SPIE, 2023.
  22. R. Deshpande, A. Avachat, F. J. Brooks, and M. A. Anastasio. “Investigating the robustness of a deep learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast measurements under laboratory conditions.” Physics in Medicine & Biology (2023).
  23. V. A. Kelkar, D. S. Gotsis, F. J. Brooks, K. C. Prabhat, K. J. Myers, R. Zeng, and M. A. Anastasio. “Assessing the ability of generative adversarial networks to learn canonical medical image statistics.” IEEE Transactions on Medical Imaging (2023).
  24. W. Liu, X. Zhang, Y. Wen, M. A. Anastasio, and J. Irudayaraj. “A machine learning approach to elucidating PFOS-induced alterations of repressive epigenetic markers with single-cell imaging.” Environmental Advances (2023): 100344.
  25. C. Xi, M. E. Kandel, S. He, C. Hu, Y. J. Lee, K. Sullivan, G. Tracey, H.J. Chung, H.J. Kong, M. A. Anastasio, and G. Popescu. “Artificial confocal microscopy for deep label-free imaging.” Nature Photonics (2023): 1-9.

2022

  1. M.J. Fanous, S. He, S. Sengupta, K. Tangella, N. Sobh, M. A. Anastasio, and G. Popescu. “White blood cell detection, classification and analysis using phase imaging with computational specificity (PICS).” Scientific Reports (2022).
  2. W. Chen, X. Zhang, H. Miao, MJ. Tang, M. A. Anastasio, J. Culver, JM. Lee, EC. Landsness. “Validation of Deep Learning-based Sleep State Classification“. microPublication Biology (2022).
  3. L. Lozenski, M. A. Anastasio, and U. Villa. “A Memory-Efficient Dynamic Image Reconstruction Method using Neural Fields.” IEEE Transactions on Computational Imaging (2022).
  4. W. Liu, A. Padhi, X. Zhang, J. Narendran, M. A. Anastasio, A. S. Nain, and J. Irudayaraj. “Dynamic Heterochromatin States in Anisotropic Nuclei of Cells on Aligned Nanofibers.” ACS Nano (2022).
  5. V. Kelkar, D. S. Gotsis, F. J. Brooks, P. KC, K. J. Myers, R. Zeng, and M. A. Anastasio. “Assessing the ability of generative adversarial networks to learn canonical medical image statistics.” arXiv preprint arXiv:2204.12007 (2022).
  6. M. E. Zachary, K. Tripathy, J. W. Trobaugh, A. M. Svoboda, M. L. Schroeder, S. M. Rafferty, E. J. Richter, A. T. Eggebrecht, M. A. Anastasio, and J. P. Culver. “Template-based and model-based decoding of movie clip identities from brain hemodynamics with high-density diffuse optical tomography.” In Neural Imaging and Sensing 2022, p. PC119460I. SPIE, 2022.
  7. L. M. Brier, X. Zhang, A. R. Bice, S. H. Gaines, E. C. Landsness, JM Lee, M. A. Anastasio, and J. P. Culver. “A Multivariate Functional Connectivity Approach to Mapping Brain Networks and Imputing Neural Activity in Mice.” Cerebral Cortex 32, no. 8 (2022): 1593-1607.
  8. K. Li, H. Li, and M. A. Anastasio. “A task-informed model training method for deep neural network-based image denoising.” In Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, vol. 12035, pp. 249-255. SPIE, 2022.
  9. R. Deshpande, M. A. Anastasio, and F. J. Brooks. “Evaluating the capacity of deep generative models to reproduce measurable high-order spatial arrangements in diagnostic images.” In Medical Imaging 2022: Image Processing, vol. 12032, pp. 521-526. SPIE, 2022.
  10. S. Sengupta, C. K. Abbey, K. Li, and M. A. Anastasio. “Investigation of adversarial robust training for establishing interpretable CNN-based numerical observers.” In Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, vol. 12035, pp. 275-282. SPIE, 2022.
  11. F. Li, U. Villa, N. Duric, and M. A. Anastasio. “Investigation of an elevation-focused transducer model for three-dimensional full-waveform inversion in ultrasound computed tomography.” In Medical Imaging 2022: Ultrasonic Imaging and Tomography, vol. 12038, pp. 206-214. SPIE, 2022.
  12. L. Lozenski, M. Anastasio, and U. Villa. “Neural fields for dynamic imaging.” In Medical Imaging 2022: Physics of Medical Imaging, vol. 12031, pp. 231-238. SPIE, 2022.
  13. R. M. Cam, U. Villa, and M. A. Anastasio. “A learned filtered backprojection method for use with half-time circular radon transform data.” In Medical Imaging 2022: Physics of Medical Imaging, vol. 12031, pp. 787-792. SPIE, 2022.
  14. C. K. Abbey, S. Sengupta, W. Zhou, A. Badal, R. Zeng, F. W. Samuelson, M. P. Eckstein, K. J. Myers, M. A. Anastasio, and Jovan G. Brankov. “Analyzing neural networks applied to an anatomical simulation of the breast.” In Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, vol. 12035, pp. 16-25. SPIE, 2022.
  15. V. A. Kelkar, D. S. Gotsis, F. J. Brooks, K. J. Myers, K. C. Prabhat, R. Zeng, and M. A. Anastasio. “Evaluating procedures for establishing generative adversarial network-based stochastic image models in medical imaging.” In Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, vol. 12035, pp. 159-164. SPIE, 2022.
  16. J. L. Granstedt, F. Li, U. Villa, and M. A. Anastasio. “Learned Hotelling observers for use with multi-modal data.” In Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, vol. 12035, pp. 262-268. SPIE, 2022.
  17. F. Zong, V. Kelkar, M. A. Anastasio, and H. Li. “Application of DatasetGAN in medical imaging: preliminary studies.” In Medical Imaging 2022: Image Processing, vol. 12032, pp. 452-458. SPIE, 2022.
  18. V. A. Kelkar, and M. A. Anastasio. “Prior image-based medical image reconstruction using a style-based generative adversarial network.” In Medical Imaging 2022: Physics of Medical Imaging, vol. 12031, pp. 244-249. SPIE, 2022.
  19. X. Zhang, E. C. Landsness, J. P. Culver, and M. A. Anastasio. “Identifying functional brain networks from spatial-temporal wide-field calcium imaging data via a recurrent autoencoder.” In Neural Imaging and Sensing 2022, p. PC1194612. SPIE, 2022.
  20. Y. R. He, S. He, M. E. Kandel, Y. J. Lee, C. Hu, N. Sobh, M. A. Anastasio, and G. Popescu. “Cell cycle stage classification using phase imaging with computational specificity.” ACS photonics 9, no. 4 (2022): 1264-1273.
  21. J. Kuo, J. Granstedt, U. Villa, and M. A. Anastasio. “Computing a projection operator onto the null space of a linear imaging operator: tutorial.” JOSA A 39, no. 3 (2022): 470-481.
  22. A. A. Oraevsky, S. A. Ermilov, A. Conjusteau, and M. Anastasio. “Laser Optoacoustic Ultrasonic Imaging System (LOUIS) and Methods of Use.” U.S. Patent Application 17/516,138, filed February 24, 2022.
  23. S. Bhadra, U. Villa, and M.A. Anastasio. “Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problems.” arXiv preprint arXiv:2202.05311 (2022).
  24. C. Hu, S. He, Y. J. Lee, Y. He, E. M. Kong, H. Li, M. A. Anastasio, and G. Popescu. “Live-dead assay on unlabeled cells using phase imaging with computational specificity.” Nature communications 13, no. 1 (2022): 1-8.
  25. X. Zhang, E. C. Landsness, W. Chen, H. Miao, M. Tang, L. M. Brier, J. P. Culver, JM Lee, and M. A. Anastasio. “Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning.” Journal of neuroscience methods 366 (2022): 109421.
  26. S. Park, F. J. Brooks, U. Villa, R. Su, M. A. Anastasio, A. A. Oraevsky, “Normalization of optical fluence distribution for three-dimensional functional optoacoustic tomography of the breast,” J. Biomed. Opt. 27(3) 036001 (16 March 2022).
  27. W.Zhou, S. Bhadra, F.J. Brooks, H. Li, M.A. Anastasio, “Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks,” J. Med. Imag. 9(1), 015503 (2022).

2021

  1. K. Li, W. Zhou, H. Li, and M.A. Anastasio, “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 Transactions on Medical Imaging (2021).
  2. X. Zhang*, V. Kelkar*, J. Granstedt, H. Li, M. A. Anastasio, “Impact of deep learning-based image super-resolution on binary signal detection,” J. Med. Imag. 8(6), 065501 (2021).
  3. L.M. Brier*, X. Zhang*, A.R. Bice, S.H. Gaines, E.C. Landsness, J. Lee, M.A. Anastasio, J.P. Culver, A Multivariate Functional Connectivity Approach to Mapping Brain Networks and Imputing Neural Activity in Mice, Cerebral Cortex, 2021;, bhab282.
  4. F. Li, U. Villa, S. Park, and M.A. Anastasio “Three-dimensional stochastic numerical breast phantoms for enabling virtual imaging trials of ultrasound computed tomography.” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2021. 
  5. J. Geng, X. Zhang, S. Prabhu, S.H. Shahoei, E.R. Nelson, K.S. Swanson, M.A. Anastasio, and A.M. Smith, “3D Microscopy and Deep Learning Reveal the Heterogeneity of Crown-Like Structure Microenvironments in Intact Adipose Tissue,” Science Advances, 7(8), eabe2480.
  6. V. Kelkar*, X. Zhang*, J. Granstedt, H. Li, M.A. Anastasio, “Task-based evaluation of deep image super-resolution in medical imaging.” In Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment, vol. 11599, p. 115990X. International Society for Optics and Photonics, 2021.
  7. A. Sharmila, X. Zhang, M. Anastasio, R. Richards-Kortum, and E.V. Petrova. “An optical, endoscopic brush for high-yield diagnostics in esophageal cancer.” In Endoscopic Microscopy XVI, vol. 11620, p. 116200B. International Society for Optics and Photonics, 2021.
  8. K. Li, W. Zhou, H. Li, and M.A. Anastasio “Assessing the Impact of Deep Neural Network-based Image Denoising on Binary Signal Detection Tasks.” IEEE Transactions on Medical Imaging, vol. 40, no. 9, pp. 2295-2305, Sept. 2021.
  9. S. Bhadra*, V. Kelkar*, F.J. Brooks, and M.A. Anastasio “On hallucinations in tomographic image reconstruction.” IEEE Transactions on Medical Imaging, 2021.
  10. A. Pattyn, Z. Mumm, N. Alijabbari, N. Duric, M. A. Anastasio, M. Mehrmohammadi “Model-based optical and acoustical compensation for photoacoustic tomography of heterogeneous mediums.” Photoacoustics, p.100275, 2021.
  11. K. Li, W. Zhou, H. Li, and M.A. Anastasio “Task-based performance evaluation of deep neural network-based image denoising.” In Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment, vol. 11599, p. 115990L. International Society for Optics and Photonics, 2021.
  12. K. Li, W. Zhou, H. Li, and M.A. Anastasio “Supervised learning-based ideal observer approximation for joint detection and estimation tasks.” In Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment, vol. 11599, p. 115990F. International Society for Optics and Photonics, 2021.
  13. J. Kuo, J. Granstedt, U. Villa, and M. A. Anastasio “Learning a projection operator onto the null space of a linear imaging operator.” In Medical Imaging 2021: Physics of Medical Imaging, vol. 11595, p. 115953X. International Society for Optics and Photonics, 2021.
  14. S. Bhadra, V. Kelkar, F.J. Brooks, and M.A. Anastasio “Assessing regularization in tomographic imaging via hallucinations in the null space.” In Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment, vol. 11599, p. 115990N. International Society for Optics and Photonics, 2021.
  15. F. Li, U. Villa, S. Park, S. He, and M.A. Anastasio “A framework for ultrasound computed tomography virtual imaging trials that employs anatomically realistic numerical breast phantoms.”  In Medical Imaging 2021: Ultrasonic Imaging and Tomography, vol. 11602, p. 116020V. International Society for Optics and Photonics, 2021.
  16. J.P. Phillips, E.Y. Sidky, G. Ongie, W. Zhou, J. Cruz-Bastida, I.S. Reiser, M.A. Anastasio, and X. Pan “A hybrid channelized Hotelling observer for estimating the ideal linear observer for total-variation-based image reconstruction.” In Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment, vol. 11599, p. 115990D. International Society for Optics and Photonics, 2021.
  17. V. Kelkar, S. Bhadra, and M.A. Anastasio “Medical image reconstruction using compressible latent space invertible networks.” In Medical Imaging 2021: Physics of Medical Imaging, vol. 11595, p. 115951S. International Society for Optics and Photonics, 2021.
  18. J. Granstedt, V. Kelkar, W. Zhou, and M. A. Anastasio “SlabGAN: a method for generating efficient 3D anisotropic medical volumes using generative adversarial networks.” In Medical Imaging 2021: Image Processing, vol. 11596, p. 1159617. International Society for Optics and Photonics, 2021.
  19. W. Zhou, S. Bhadra, F.J. Brooks, J. Granstedt, H. Li and M.A. Anastasio “Advancing the AmbientGAN for learning stochastic object models.” In Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment, vol. 11599, p. 115990A. International Society for Optics and Photonics, 2021.
  20. V.A. Kelkar, S. Bhadra, and M.A. Anastasio, “Compressible Latent-Space Invertible Networks for Generative Model-Constrained Image Reconstruction,” IEEE Transactions on Computational Imaging 7 (2021): 209-223
  21. V. A. Kelkar, M.A. Anastasio “Prior image-constrained reconstruction using style-based generative models.” Proceedings of the 38th International Conference on Machine Learning, 2021
  22. M.A. Anastasio, T. Matthews, and B. Kelly “Deep learning-assisted image reconstruction for tomographic imaging.” United States patent application US 16/616,742. 2021 May 20.
  23. B. Shabestri, M.A. Anastasio, B. Fei, F. Leblond “Special Series Guest Editorial: Artificial Intelligence and Machine Learning in Biomedical Optics.” Journal of biomedical optics. 2021 May;26(5).
  24. C. Hu, S. He, Y.J. Lee, Y. He, M.A. Anastasio, and G. Popescu. “Label-free cell viability assay using phase imaging with computational specificity (PICS).” Quantitative Phase Imaging VII, vol. 11653, p. 116531D. International Society for Optics and Photonics, 2021.
  25. Y.R. He, S. He, M. Kandel, Y.J. Lee, N. Sobh, M.A. Anastasio, and G. Popescu. “Cell cycle detection using phase imaging with computational specificity (PICS).” Quantitative Phase Imaging VII 2021 Mar 5 (Vol. 11653, p. 116531R). International Society for Optics and Photonics.
  26. K. Tripathy, Z.E. Markow, A.K. Fishell, A. Sherafati, T.M. Burns-Yocum, M.L. Schroeder, A.M. Svoboda, A.T. Eggebrecht, M.A. Anastasio, B.L. Schlaggar, J.P. Culver. “Decoding visual information from high-density diffuse optical tomography neuroimaging data.” Neuroimage. 2021 Feb 1;226:117516.

2020

1. S. He, W. Zhou, H. Li, and M.A. Anastasio, “Learning Numerical Observers using Unsupervised Domain Adaptation,” Proceedings of SPIE on Medical Imaging 2020.

2.J.L., Granstedt, W. Zhou, and M.A. Anastasio, “Approximating the Hotelling Observer with Autoencoder-Learned Efficient Channels for Binary Signal Detection Tasks.,” Proceedings of SPIE on SPIE Medical Imaging 2020.

3. S. Bhadra, W. Zhou, and M.A. Anastasio, “Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks,” Proceedings of SPIE on Medical Imaging 2020.

4. W. Zhou and M.A. Anastasio, “Markov-Chain Monte Carlo Approximation of the Ideal Observer using Generative Adversarial Networks.,” Proceedings of SPIE on Medical Imaging 2020.

5. W., Zhou, S., Bhadra, F.J., Brooks, H., Li and M.A. Anastasio,”Progressively-Growing AmbientGANs For Learning Stochastic Object Models From Imaging Measurements,” Proceedings of SPIE on Medical Imaging 2020.

6. W. Thompson, A. Yu, D.S. Dumani, J. Cook, M.A. Anastasio, S.Y. Emelianov, and S.A. Ermilov “A preclinical small animal imaging platform combining multi-angle photoacoustic and fluorescence projections into co-registered 3D maps,” Proceedings of SPIE on Photons Plus Ultrasound: Imaging and Sensing 2020.

7. B. Shrestha, F. DeLuna, M.A. Anastasio, J. Ye, and E. Brey, “Photoacoustic Imaging in Tissue Engineering and Regenerative Medicine,” Tissue Engineering (2020).

8. Y. Chen, C.K. Hagen, A. Olivo, and M.A. Anastasio, “A partial-dithering strategy for edge-illumination X-ray phase-contrast tomography enabled by a joint reconstruction method,” Physics in Medicine & Biology (2020).

9. J. Poudel, S. Na, L. Wang, and M.A. Anastasio, “Iterative image reconstruction in transcranial photoacoustic tomography based on the elastic wave equation,” Physics in Medicine and Biology (2020). In press.

10. Y. Chen, W. Zhou, C. Hagen, A. Olivo, M.A. Anastasio, “Comparison of data-acquisition designs for single-shot edge-illumination X-ray phase-contrast tomography,” Optics Express. 2020. In press.

11. K. Minn, Y. Fu, S. He, S. George, M.A. Anastasio, S.A. Morris, and L. Solnica-Krezel, “High-resolution transcriptional and morphogenetic profiling of cells from micropatterned human embryonic stem cell gastruloid cultures. ,” bioRxiv. (2020).

12. A. Adler, H. Ammari, M.A. Anastasio, S.R. Arridge, L. Bar, W. Benger, M. Bertero, B. Borden, JM. Borwein, A.M. Bronstein, and M.M. Bronstein, “Handbook of mathematical methods in imaging,” 2020.

13. K.S. Uddin, M. Zhang, M.A. Anastasio, and Q. Zhu, “Optimal breast cancer diagnostic strategy using combined ultrasound and diffuse optical tomography ,” Biomedical Optics Express. 2020.

14. W. Zhou, H. Li, and M.A. Anastasio “Approximating the Ideal Observer for joint signal detection and localization tasks by use of supervised learning methods ,” IEEE Transactions on Medical Imaging, July 2020.

15. W. Zhou, S. Bhadra, F.J. Brooks, H. Li, and M.A. Anastasio, “Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs,” Submitted to Transactions on Medical Imaging.

16. J. Poudel and M.A. Anastasio, “Joint reconstruction of initial pressure distribution and spatial distribution of acoustic properties of elastic media with application to transcranial photoacoustic tomography,” Inverse Problems (2020).

17. S. He, K.T. Minn, L. Solnica-Krezel, M.A. Anastasio and H. Li, “Deeply-Supervised Density Regression for Automatic Cell Counting in Microscopy Images.,” Medical Image Analysis, p.101892. (2020).

18. K.T. Minn, Y.C. Fu, S. He, S.C. George, M.A. Anastasio, S.A. Morris, and L. Solnica-Krezel, “High-Resolution Transcriptional and Morphogenetic Profiling of Cells from Micropatterned Human Embryonic Stem Cell Gastruloid Cultures.,” eLife (2020).

19. K. Tripathy, Z.E. Markow, A.K. Fishell, A. Sherafati, T.M. Burns-Yocum, M.L. Schroeder, A.M. Svoboda, A.T. Eggebrecht, M.A. Anastasio, B.L. Schlaggar, and J.P. Culver, “Decoding visual information from high-density diffuse optical tomography neuroimaging data.,” NeuroImage (2020).

2019

1. Y. Lou, S. Park, F. Anis, R. Su, A. Oraevesky, and M.A. Anastasio, “Analysis of the use of unmatched backward operators in iterative image reconstruction with application to three-dimensional optoacoustic tomography,” IEEE Transactions on Computational Imaging (2019).

2. J. Poudel, Y. Lou, and M.A. Anastasio, “A survey of computational frameworks for solving the acoustic inverse problem in three-dimensional photoacoustic computed tomography,” Phys. Med. Biol., 2019.

3. W. Zhou, H. Li, and M.A. Anastasio, “Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods,” IEEE Transactions on Medical Imaging, 2019 Apr.

4. S.S. Alshahrani, Y. Yan, N. Alijabbari, A. Pattyn, I, Avrutsky, E. Malyarenko, J Poudel, M.A. Anastasio M, and M. Mehrmohammadi, “All-reflective ring illumination system for photoacoustic tomography,” J. of Biomedical Optics, 24(4), 046004 (2019).

5. J. Wu, C. Lian, S. Ruan, T. Mazur, S. Mutic, M.A. Anastasio, P, Grigsby, P. Vera, and H. Li, “Treatment Outcome Prediction for Cancer Patients Based on Radiomics and Belief Function Theory,” IEEE Transactions on Radiation and Plasma Medical Sciences, 2019 Mar;3(2):216-24.

6. J. Brown, S. Somo, F. Brooks, S. Komarov, W. Zhou, M.A. Anastasio, and E. Brey, “X-Ray CT in Phase Contrast Enhancement Geometry of Alginate Microbeads,” Annals of Biomedical Engineering (2019).

7. F.J. Brooks, S.P. Gunsten, S.K. Vasireddi, S.L. Brody, and M.A. Anastasio, “Quantification of image texture in X‐ray phase‐contrast‐enhanced projection images of in vivo mouse lungs observed at varied inflation pressures“, Physiological Reports (2019).

2018

1. T.P. Matthews, J. Poudel, L. Lei,  L.V. Wang, and M.A. Anastasio, “Parameterized joint reconstruction of the initial pressure and sound speed distributions for photoacoustic computed tomography,” SIAM J. Imaging Sci 11, no. 2 (2018): 1560-1588.

2. Y. Chen and M.A. Anastasio, “Properties of a Joint Reconstruction Method for Edge-Illumination X-Ray Phase-Contrast Tomography,” Sensing and Imaging (2018) 19: 7.

3. S. Dolly, Y. Lou, M.A. Anastasio, and H. Li, “Learning-based Stochastic Object Models for Characterizing Anatomical Variations,” Physics in Medicine and Biology 63, no. 6 (2018): 065004.

4. J. Wu, T. Mazur, Su Ruan, C. Lian, N. Daniel, H. Lashmett, L. Ochoa, I. Zoberi, M. Anastasio, M. Gach, S. Mutic, M. Thomas, H. Li, “A Deep Boltzmann Machines-Driven Level-Set Method for Heart Motion Tracking Using Cine MRI Images,” Medical Image Analysis 47 (2018): 68-80.

5. H. Guan, C.K. Hagen, A. Olivo, and M.A. Anastasio: “Subspace-Based Resolution-Enhancing Image Reconstruction Method for Few-View Differential Phase-Contrast Tomography“, J. Med. Imag. 5(2), 023501 (2018).  

6. A.H. Lumpkin, A.B. Garson, M.A. Anastasio: “First point-spread function and x-ray phase-contrast imaging results with an 88-mm diameter single crystal“, Review of Scientific Instruments. 89 , 073704 (2018).

7. Y. Chen, L. Yang, W. Kun, M.A. Kupinski, and M.A. Anastasio. “Reconstruction-Aware Imaging System Ranking by use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference.” IEEE transactions on medical imaging (2018). In print.

2017

1. K.M.S. Uddin, A. Mostafa,  M.A. Anastasio, Q. Zhu, “Two Step Imaging Reconstruction using Truncated Pseudoinverse as an initial estimate in Ultrasound guided Diffuse optical tomography“, Biomed. Opt. Express 8(12), 5437-5449 (2017).

2. T.P. Matthews, M.A. Anastasio, “Joint reconstruction of the initial pressure and speed of sound distributions from combined photoacoustic and ultrasound tomography measurements“, Inverse Problems (2017).

3. B. Kelly, T.P. Matthews, M.A. Anastasio, “Deep Learning-Guided Image Reconstruction from Incomplete Data”, eprint arXiv:1709.00584

4. A. Zamir, C. Hagen, P. Diemoz, M. Endrizzi, F. Vittoria, Y. Chen, M.A. Anastasio, A. Olivo, “Recent advances in edge illumination tomography“, Journal of Medical Imaging. 2017 Oct;4(4):040901..

5. K. Mitsuhashi, J. Poudel, T.P.Matthews, A. Garcia-Uribe, L.V. Wang and M.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 Nov.

6. T.P. Matthews, K. Wang, C. Li, N. Duric, M.A. Anastasio, “Regularized Dual Averaging Image Reconstruction for Full-Wave Ultrasound Computed Tomography“, IEEE Trans. UFFC, Volume: 64, Issue: 5, 811-825, May 2017.

7. J. Poudel, T.P. Matthews, L. Li, M.A. Anastasio, L.V. Wang; “Mitigation of artifacts due to isolated acoustic heterogeneities in photoacoustic computed tomography using a variable data truncation-based reconstruction method.” J. Biomed. Opt. 0001;22(4):041018.  doi:10.1117/1.JBO.22.4.041018.

8. Y. Lou, W. Zhou, T. P. Matthews, C. M. Appleton, M. A. Anastasio, “Generation of anatomically realistic numerical phantoms for photoacoustic and ultrasonic breast imaging,” J. Biomed. Opt. 22(4), 041015 (2017), doi: 10.1117/1.JBO.22.4.041015.

9. Y. Chen, H. Guan, C. K. Hagen, A. Olivo, and M. A. Anastasio, “Single-shot edge illumination x-ray phase-contrast tomography enabled by joint image reconstruction,” Opt. Lett.  42, 619-622 (2017)

2016

1. H. Li, S. Dolly, H.C Chen, M.A. Anastasio, DA. Low, H.H. Li, J.M. Michalski, W.L. Thorstad, H. Gay, S. Mutic: “A Comparative Study Based on Image Quality and Clinical Task Performance for CT Reconstruction Algorithms in Radiotherapy“,  Journal of Applied Clinical Medical Physics. 2016 Jul 8;17(4).

2. A.A. Appel, V. Ibarra, S.I. Somo, J.C. Larson, A.B. Garson III, H. Guan, J.P. McQuiling, Z. Zhong, M.A. Anastasio, E.C. Opara, E.M. Brey: “Imaging of Hydrogel Microsphere Structure and Foreign Body Response Based on Endogenous X-ray Phase Contrast“, Tissue Engineering, Part C, vol 22 number 11, 2016.

3. Y. Lou, K. Wang, A.A. Oraevsky, M.A.Anastasio: “Impact of non-stationary optical illumination on image reconstruction in optoacoustic tomography“, Journal of the Optical Society of America A, 33(12): 2333-2347, 2016.

4. C. Huang, K. Wang, R. Schoonover, L.V. Wang, and M.A. Anastasio: ” Joint Reconstruction of Absorbed Optical Energy Density and Sound Speed Distributions in Photoacoustic Computed Tomography: A Numerical Investigation “, IEEE Transactions on Computational Imaging, 2.2, 136-149, 2016.

5. Q. Xu, A. Sawatzky, T. Jun, D. Yang, and M.A. Anastasio: ” Accelerated Fast Iterative Shrinkage Thresholding Algorithms for Sparsity-Regularized Cone-Beam CT Image Reconstruction “, Medical Physics, 43; 1849, 2016.

6. H-C Chen, S. Dolly, H. Li, B Fischer-Valuck, J Dempsey, S. Mutic, M.A. Anastasio, and H. Li. ” An Integrated-Model Driven Method for In-treatment Upper Airway Motion Tracking using Cine MRI in Head & Neck Radiation Therapy “, Medical Physics, 43.8, 4700-4710, 2016.

7. S. Dolly, H-C Chen, M.A. Anastasio, S. Mutic, and H. Li: ” Practical Considerations for Noise Power Spectra Estimation for Clinical CT Scanners “, Journal of Applied Clinical Medical Physics, Vol. 17:3, 2016.

8. V. Ibarra, A. Appel, E. Opara, M.A. Anastasio and E.M Brey: ” Evaluation of the Tissue Response to Alginate Encapsulated Islets in an Omentum Pouch Mode “, Journal of Biomedical Materials Research: Part A, Accepted, 2016.

9. L. Li, J. Xia, G. Li, A Garcia-Uribe, Q. Sheng, M.A. Anastasio, and L.V. Wang: ” Label-free Photoacoustic Tomography of Whole Mouse Brain Structures Ex Vivo “, Journal of Neurophotonics, 3.3, 035001-035001. 2016.

2015

1. Q. Sheng, K. Wang, M. P. Matthews, J. Xia, L. Zhu, L. V. Wang, and M. A. Anastasio, “A constrained variable projection reconstruction method for photoacoustic computed tomography without accurate knowledge of transducer responses,”Medical Imaging, IEEE Transactions on, in press, (2015).

2. S. A. Ermilov, R. Su, A. Conjusteau, T. Oruganti, K. Wang, F. Anis, M. A. Anastasio, A. A. Oraevsky, “Three-dimensional optoacoustic and laser-induced ultrasound tomography system for preclinical research“, Ultrasonic Imaging , in press, (2015).

3. H. Guan, Q. Xu, A. B. Garson III, and M. A. Anastasio, “ Boundary-enhancement in propagation-based X-ray phase contrast tomosynthesis improves depth position characterization,” Physics in Medicine and Biology , 60 (8) N151, (2015).

4. K. Wang, T. P. Matthews, F. Anis, C. Li, N. Duric, and M. A. Anastasio. “ Waveform inversion with source encoding for breast sound speed reconstruction in ultrasound computed tomography“, Ultrasonics, Ferroelectrics, and Frequency Control, IEEE Transactions , 62 (3): 475-493, (2015).

5. A. A. Alyssa, J. C. Larson, A. B. Garson, H. Guan, Z. Zhong, B. B. Nguyen, J. P. Fisher, M. A. Anastasio, and E. M. Brey. “ X‐ray phase contrast imaging of calcified tissue and biomaterial structure in bioreactor engineered tissues“, Biotechnology and bioengineering , 112(3): 612-620, (2015).

6. H. Chen, J. Tan, S. Dolly, J. Kavanaugh, M. A. Anastasio, D. A. Low, H. Li et al. “ Automated contouring error detection based on supervised geometric attribute distribution models for radiation therapy: A general strategy “, Medical physics, 42(2): 1048-1059, (2015).

2014

1. J. Xia; W. Chen; K. Maslov; M. A. Anastasio; L. V. Wang, “ Retrospective respiration-gated whole-body photoacoustic computed tomography of mice,” J. Biomed. Opt. 19 (1), 016003 (2014).

2. K. Mitsuhashi, K. Wang, and M.A. Anastasio, “Investigation of the far-field approximation for modeling a transducer’s spatial impulse response in photoacoustic computed tomography,” Photoacoustics, Volume 2, Issue 1, Pages 21–32 (2014).

3. C.O. Schirra, B. Brendel. M.A. Anastasio, and E. Roessl, “Spectral CT: a technology primer for contrast agent development,” Contrast Media & Molecular Imaging, Volume 9, Issue 1, pages 62–70, (2014).

4. K. Wang, R.W. Schoonover Su, A.A. Oraevsky, M.A. Anastasio, “Discrete Imaging Models for Three-Dimensional Optoacoustic Tomography using Radially Symmetric Expansion Functions,” Medical Imaging, IEEE Transactions on, vol.33, no.5, pp.1180,1193, (2014).

5. Q. Xu, A. Sawatzky, E. Roessl, M.A. Anastasio. and C.O. Schirra, “Sparsity-regularized image reconstruction of decomposed K-edge data in spectral CT,” Phys. Med. Biol., 59 N65 (2014).

6. K. Wang, J. Xia, C. Li, L.V. Wang,a and M.A. Anastasio, “Fast spatiotemporal image reconstruction based on low-rank matrix estimation for dynamic photoacoustic computed tomography,” J. Biomed. Opt., 19(5), 056007 (2014).

7. A. Sawatzky, Q. Xu, C.O. Schirra, and M.A. Anastasio, “Proximal ADMM for Multi-Channel Image Reconstruction in Spectral X-ray CT,” Medical Imaging, IEEE Transactions on, vol.33, no.8, pp.1657,1668, (2014).

8. H. Li, L. Yu, M.A. Anastasio, H. Chen, J. Tan, H. Gay, J.M. Michalski, D.A. Low and S. Mutic, “Automatic CT simulation optimization for radiation therapy: A general strategy,” Med. Phys. 41, 031913 (2014);

9. G. Li, J. Xia, K. Wang, K. Maslov, M.A. Anastasio, L. V. Wang ,“Tripling the detection view of high-frequency linear-array-based photoacoustic computed tomography by using two planar acoustic reflectors”, Quantitative Imaging in Medicine and Surgery
, In press, 2014.

2013

1. C.O. Schirra, E. Roessl, T. Koehler, B. Brendel, A. Thran, D. Pan, M.A. Anastasio, R. Proksa, “Statistical Reconstruction of Material Decomposed Data in Spectral CT,” IEEE Trans Med Imaging 32(7):1249-57 (2013)

2. A.A. Appel, M.A. Anastasio, J.C. Larson, E.M. Brey, “Imaging challenges in biomaterials and tissue engineering,” Biomaterials 34(28):6615-30 (2013)

3. A.A. Appel, C-Y Chou, J.C. Larson, Z. Zhong, F.J. Schoen, C.M. Johnston, E.M. Brey and M.A. Anastasio, “An initial evaluation of analyser-based phase-contrast X-ray imaging of carotid plaque microstructure,” Br J Radiol 86(1021):20120318 (2013).

4. K. Wang, C. Huang, Y Kao, C-Y Chou, A.A. Oraevsky, M.A. Anastasio, “Accelerating image reconstruction in three-dimensional optoacoustic tomography on graphics processing units ,” Med. Phys. 40, 023301 (2013).

5. C. Huang, K. Wang, L. Nie, L.V. Wang, M.A. Anastasio, “Full-Wave Iterative Image Reconstruction in Photoacoustic Tomography With Acoustically Inhomogeneous Media ,” Medical Imaging, IEEE Transactions on vol.32, no.6, pp.1097,1110 (2013).

6. A.B. Garson, E.W. Izaguirre, S.G. Price, M.A. Anastasio, “Characterization of speckle in lung images acquired with a benchtop in-line x-ray phase-contrast system,” Phys Med Biol. 58(12):4237-53 (2013).

7. J. Xia, C. Huang, K. Maslov, M.A. Anastasio, and L.V. Wang, “Enhancement of photoacoustic tomography by ultrasonic computed tomography based on optical excitation of elements of a full-ring transducer array,” Optics Letters, Vol. 38, Issue 16, pp. 3140-3143 (2013)

2012

1. A.A. Appel, C-Y Chou, J.C. Larson, Z. Zhong, F.J. Schoen, C.M. Johnston, E.M. Brey and M.A. Anastasio, “Analyzer-based phase-contrast x-ray imaging of carotid plaque microstructure,” The American Journal of Surgery Volume 204, Issue 5 (2012).

2. A.M. Zysk, J.G. Brankov, M.N. Wernick and M.A. Anastasio, “Adaptation of a clustered lumpy background model for task-based image quality assessment in x-ray phase-contrast mammography,” Med. Phys. 39, 906 (2012).

3. A.M. Zysk, R.W. Schoonover, Q. Xu, and M.A. Anastasio, “A framework for computing the spatial coherence effects of polycapillary x-ray optics,” Opt. Express 20, 3975–3982 (2012).

4. C. Huang, L. Nie, R.W. Schoonover, L.V. Wang, M.A. Anastasio, “Photoacoustic computed tomography correcting for heterogeneity and attenuation,” J. Biomed. Opt., 17, 061211 (2012).

5. R.W. Schoonover, L.V. Wang, M.A. Anastasio, “A numerical investigation of the effects of shear waves in transcranial photoacoustic tomography with a planar geometry,” J. Biomed. Opt. 17, 061215 (2012).

6. J.S. Sandu, R.W. Schoonover, J.I. Weber, J. Tawiah, V. Kunin, M.A. Anastasio, “Transducer field imaging using Acoustography,” Adv. Acoust. Vib., 275875 (2012).

7. C. Huang, L. Nie, R.W. Schoonover, Z. Guo, C.O. Schirra, M.A. Anastasio, L.V. Wang, “Aberration correction for transcranial photoacoustic tomography of primates employing adjunct data,” J. Biomed. Opt. 17, 066016 (2012).

8. A.M. Zysk, A.B. Garson, Q. Xu, E.M. Brey, W. Zhou, J.G. Brankov, M.N. Wernick, J.R. Kuszak, and M.A. Anastasio, “Nondestructive volumetric imaging of tissue microstructure with benchtop x-ray phase-contrast tomography and critical point drying,” Biomedical Optics Express, 3, 1924-1932 (2012).

9. Q. Xu, E.Y. Sidky, X. Pan, M. Stampanoni, P. Modregger, and M.A. Anastasio, “Investigation of discrete imaging models and iterative image reconstruction in differential X-ray phase-contrast tomography,” Optics Express, 20, 10724-10749 (2012).

10. J. Xia, M.R. Chatni, K.I. Maslov, Z. Guo, K. Wang, M.A. Anastasio and L.V. Wang “Whole-body ring-shaped confocal photoacoustic computed tomography of small animals in vivo,” J. Biomed. Opt. 17, 050506 (2012).

11. K. Wang, R. Su, A.A. Oraevsky and M.A. Anastasio, “Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography,” Phys. Med. Biol., 57, 5399 (2012).

12. Alyssa A. Appel, Cheng-Ying Chou, Howard P. Greisler, Jeffery C. Larson, Sunil Vasireddi, Zhong Zhong, Mark A. Anastasio, Eric M. Brey, “Analyzer-based phase-contrast x-ray imaging of carotid plaque microstructure,” Br J Radiol, 204(5) 631-636 (2012).

13. A. Tamhane, K. Arfanakis, M.A. Anastasio, X. Guo, M. Vannier, J. Gao, “Rapid PROPELLER- MRI: A combination of iterative reconstruction and under-sampling,” J. Magn. Reson. Imaging, 36, 1241-1247, (2012).

14. Liming Nie, Xin Cai, Konstantin Maslov, Alejandro Garcia-Uribe, Mark A. Anastasio, Lihong V. Wang, “Photoacoustic tomography through a whole adult human skull with a photon recycler,” J. Biomed. Opt., 17(11), 110506 (Nov 02, 2012).

15. Kun Wang and Mark A Anastasio, “A simple Fourier transform-based reconstruction formula for photoacoustic computed tomography with a circular or spherical measurement geometry ,” Phys. Med. Biol., 57 N493, (2012).

2011

1. M. Roumeliotis, R.Z. Stodilka, M.A. Anastasio, E. Ng and J.L. Carson, “Singular value decomposition analysis of a photoacoustic imaging system and 3D imaging at 0.7 FPS,” Optics Express, 19, 13405-13417 (2011).

2. R.W. Schoonover, M.A. Anastasio, “Image reconstruction in photoacoustic tomography involving layered acoustic media,” J. Opt. Soc. Am. A, 28, 1114–1120 (2011).

3. R.W. Schoonover, M.A. Anastasio, “Compensation of shear waves in photoacoustic tomography with layered acoustic media,” J. Opt. Soc. Am. A, 28, 2091–2099 (2011).

4. A. Appel, M.A. Anastasio, and E.B. Brey, “Potential for Imaging Engineered Tissues with X-ray Phase Contrast,” Tissue Engineering Part B: Reviews, 17, 321-330 (2011).

5. K. Wang, S.A. Ermilov, R. Su, H.P. Brecht, A.A. Oraevsky M.A. Anastasio, “An imaging model incorporating ultrasonic transducer properties for three-dimensional optoacoustic tomography” Medical Imaging, IEEE Transactions on, 30, 203-214, (2011).

2010

1. E.Y. Sidky, M.A. Anastasio, and X. Pan, “Image reconstruction exploiting object sparsity in boundary-enhanced X-ray phase-contrast tomography” Optics Express, 18, 10404-10422 (2010).

2. M. Roumeliotis, R.Z. Stodilka, M.A. Anastasio, G. Chaudhary, H. Al-Aabed, E. Ng, A. Immucci and J.L. Carson, “Analysis of a photoacoustic imaging system by the crosstalk matrix and singular value decomposition” Optics Express, 18, 11406-11417 (2010).

3. M.A. Anastasio, C.Y. Chou, A.M. Zysk, and J.G. Brankov, “Analysis of ideal observer signal detectability in phase-contrast imaging employing linear shift-invariant optical systems,” J. Opt. Soc. Am. A, 27, 2648-2659 (2010).

4. A.M. Zysk, R.W. Schoonover, P.S. Carney, M.A. Anastasio, “Transport of intensity and spectrum for partially coherent fields,” Opt. Lett., 35, 2239–2241 (2010).

5. E.Y. Sidky, M. A. Anastasio, and X. Pan, “Image Reconstruction Exploiting Object Sparsity in Boundary-Enhanced Phase-Contrast Tomography,” Optics Express, 18, pp. 10404-10422 (2010).

6. E. M. Brey, A. Appel, Y-C. Chiu, Z. Zhong, and M. A. Anastasio, “X-Ray Imaging of Poly(ethylene glycol) Hydrogels Without Contrast Agents,” Tissue Engineering  Part C: Methods, 16, 1597-1600 (2010).

Book Chapters:

1. Patrick La Rivière, Jin Zhang, and Mark A. Anastasio, “Image Reconstruction in Optoacoustic Tomography Accounting for Frequency-Dependent Attenuation,” Photoacoustic Imaging and Spectroscopy, edited by Lihong V . Wang, (CRC Press, 2009), pp. 145–154.

2. K. Wang and M. A. Anastasio, “Photoacoustic and thermoacoustic tomography: image formation principles,” Handbook of Mathematical Methods in Imaging, edited by Otmar Scherzer, (Springer, 2011), pp. 781-815.