Directory

Mark Anastasio's directory photo.

Mark Anastasio

Professor

Primary Affiliation

Computational Imaging

Affiliations

Status Part-time Faculty

Home Department of Bioengineering

Phone 333-1867

Email maa@illinois.edu

Address

  • Biography

    Mark A. Anastasio is the Donald Biggar Willett Professor in Engineering. He serves as head of the Department of Bioengineering. 

  • Honors

    2017: Elected as SPIE Fellow

    2015: Elected as AIMBE Fellow 

     

  • Research

    Prof. Mark Anastasio is interested in researching computational and theoretical image science and pursuing the advancement of emerging imaging methods. Much of his research is aimed at the development of new imaging technologies, including photoacoustic tomography (PACT), ultrasound imaging tomography (USCT), and x-ray contrast imaging. Prof. Anastasio also conducts research related to the use of machine learning in imaging science. Topics include image reconstruction using machine learning and machine learning-based numerical observers, as well as computational methods for neuroimaging and the use of deep learning for image analysis.

  • 2021

    • Jason L. Granstedt, Varun A. Kelkar, Weimin Zhou, Anastasio M.A.: 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. DOI: 10.1117/12.2581380
    • Kaiyan Li, Weimin Zhou, Hua Li, Anastasio M.A.: 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. DOI: 10.1117/12.2582327
    • Kaiyan Li, Weimin Zhou, Hua Li, Anastasio M.A.: 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. DOI: 10.1117/12.2582324
    • 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, 2021. DOI: 10.1117/12.2581295
    • Varun A. Kelkar, Sayantan Bhadra, Anastasio M.A.: 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. DOI: 10.1117/12.2581295
    • Varun A. Kelkar, Xiaohui Zhang, Jason Granstedt, Hua Li, Anastasio M.A.: Task-based evaluation of deep image super-resolution in medical imaging, SPIE Medical Imaging, 2021. DOI: 10.1117/12.2582011

    2020

    • Bhadra, S, Zhou, W & Anastasio, MA 2020, Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks. in G-H Chen & H Bosmans (eds), Medical Imaging 2020: Physics of Medical Imaging., 113120V, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 11312, SPIE, Medical Imaging 2020: Physics of Medical Imaging, Houston, United States, 2/16/20. DOI: 10.1117/12.2549750
    • Chen, Y, Hagen, CK, Olivo, A & Anastasio, MA 2020, 'A partial-dithering strategy for edge-illumination X-ray phase-contrast tomography enabled by a joint reconstruction method', Physics in medicine and biology, vol. 65, no. 10, 105007. DOI: 10.1088/1361-6560/ab66e2
    • Chen, Y, Zhou, W, Hagen, CK, Olivo, A & Anastasio, MA 2020, 'Comparison of data-acquisition designs for single-shot edge-illumination X-ray phase-contrast tomography', Optics Express, vol. 28, no. 1, pp. 1-19. DOI: 10.1364/OE.28.000001
    • Granstedt, JL, Zhou, W & Anastasio, MA 2020, Learning efficient channels with a dual loss autoencoder. in FW Samuelson & S Taylor-Phillips (eds), Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment., 113160C, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 11316, SPIE, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, Houston, United States, 2/19/20. DOI: 10.1117/12.254936
    • He, S, Zhou, W, Li, H & Anastasio, MA 2020, Learning numerical observers using unsupervised domain adaptation. in FW Samuelson & S Taylor-Phillips (eds), Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment., 113160W, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 11316, SPIE, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, Houston, United States, 2/19/20. DOI: 10.1117/12.2549812
    • Poudel, J & Anastasio, MA 2020, 'Joint reconstruction of initial pressure distribution and spatial distribution of acoustic properties of elastic media with application to transcranial photoacoustic tomography', Inverse Problems, vol. 36, no. 12, 124007. DOI: 10.1088/1361-6420/abc7ce
    • Poudel, J, Na, S, Wang, LV & Anastasio, MA 2020, 'Iterative image reconstruction in transcranial photoacoustic tomography based on the elastic wave equation', Physics in medicine and biology, vol. 65, no. 5, 055009. DOI: 10.1088/1361-6560/ab6b46
    • Zhou W, Bhadra, S, Brooks, FJ, Li, H & Anastasio, MA 2020, Progressively-Growing AmbientGANs for learning stochastic object models from imaging measurements. in FW Samuelson & S Taylor-Phillips (eds), Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment., 113160Q, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 11316, SPIE, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, Houston, United States, 2/19/20. DOI: 10.1117/12.2549610
    • Zhou W, Li, H & Anastasio, MA 2020, 'Approximating the Ideal Observer for Joint Signal Detection and Localization Tasks by use of Supervised Learning Methods', IEEE Transactions on Medical Imaging, vol. 39, no. 12, 9139307, pp. 3992-4000. DOI: 10.1109/TMI.2020.3009022
    • Zhou, W & Anastasio, MA 2020, Markov-Chain Monte Carlo approximation of the Ideal Observer using generative adversarial networks. in FW Samuelson & S Taylor-Phillips (eds), Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment., 113160D, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 11316, SPIE, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, Houston, United States, 2/19/20. DOI: 10.1117/12.2549732

    2019

    • Brooks, FJ, Gunsten, SP, Vasireddi, SK, Brody, SL & Anastasio, MA 2019, 'Quantification of image texture in X-ray phase-contrast-enhanced projection images of in vivo mouse lungs observed at varied inflation pressures', Physiological Reports, vol. 7, no. 16, e14208. DOI: 10.14814/phy2.14208
    • Poudel, J, Lou, Y & Anastasio, MA 2019, 'A survey of computational frameworks for solving the acoustic inverse problem in three-dimensional photoacoustic computed tomography', Physics in medicine and biology, vol. 64, no. 14, 14TR01. DOI: 10.1088/1361-6560/ab2017
    • Zhou W, Li, H & Anastasio, MA 2019, 'Approximating the Ideal Observer and Hotelling Observer for Binary Signal Detection Tasks by Use of Supervised Learning Methods', IEEE Transactions on Medical Imaging, vol. 38, no. 10, 8691467, pp. 2456-2468. DOI: 10.1109/TMI.2019.2911211

    2018

    • Chen Y and M.A. Anastasio, “Properties of a Joint Reconstruction Method for Edge-Illumination X-Ray Phase-Contrast Tomography,” Sensing and Imaging (2018) 19: 7. DOI: 10.1007/s11220-018-0186-y
    • Chen Y, 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). DOI: 10.0.4.85/TMI.2018.2880870
    • Guan H, 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). DOI: 10.1117/1.JMI.5.2.023501
    • Lumpkin AH, 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). DOI: 10.1063/1.5027499
    • Matthews TP, 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. DOI: 10.1137/17M1153649

    2017

    • Chen Y, 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). DOI: 10.1364/OL.42.000619
    • Lou Y, 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.
    • Matthews TP, 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. DOI: 10.1109/TUFFC.2017.2682061
    • Matthews TP, M.A. Anastasio, “Joint reconstruction of the initial pressure and speed of sound distributions from combined photoacoustic and ultrasound tomography measurements“, Inverse Problems (2017). DOI: 10.1088/1361-6420/aa9384
    • Mitsuhashi K, 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. DOI: 10.1137/16M1107619