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Marvin Doyley - University of Rochester. Rochester, NY, US

Marvin Doyley

Wilson Professor of Electronic Imaging; Professor of Electrical and Computer Engineering; Professor of Biomedical Engineering; Professor of Imaging Sciences | University of Rochester


Marvin Doyley researches novel imaging methods for detecting disease more quickly through imaging

Areas of Expertise (2)

Ultrasound Imaging

Therapeutic and Diagnostic Imaging



Marvin Doyley is the Wilson Professor of Electronic Imaging and is currently chair and professor of the Department of Electrical and Computer Engineering, with joint appointments in the Departments of Biomedical Engineering and Imaging Sciences. His research interests include therapeutic and diagnostic imaging using optical, magnetic resonance, and ultrasound imaging and his research team is concentrated on non-invasive vascular elastography, high-frequency nonlinear ultrasound imaging, and structural and functional imaging of pancreatic and colorectal cancer.

Doyley is a fellow of the IEEE (Institute of Electrical and Electronics Engineers), AIUM (American Institute of Ultrasound in Medicine), and AIMBE (American Institute for Medical and Biological Engineering). He currently serves on the editorial boards of IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, SPIE Journal of Medical Imaging, Physics in Medicine and Biology, and Nature Scientific Reports.

Education (2)

University of London, Institute of Cancer Research: PhdD, Biophysics, 2000

Brunel University: BSc, Applied Physics 1994

Affiliations (11)

  • Member/Fellow, Asia-Pacific Artificial Intelligence Association
  • Member, Electrical and Computer Engineering Department Heads Association
  • Member, Society for Imaging Science and Technology
  • Member/Fellow, American Institute for Medical and Biological Engineering
  • Member, International Society for Magnetic Resonance in Medicine
  • Member, Acoustical Society of America
  • Fellow (2023), American Institute of Ultrasound in Medicine
  • Member, American Association for the Advancement of Science
  • Member, Rochester Center for Biomedical Ultrasound, University of Rochester
  • Member, Society of Photographic Instrumentation Engineers
  • Fellow (2023), Institute of Electrical and Electronics Engineers

Selected Articles (5)

Stiffness pulsation of the human brain detected by non-invasive time-harmonic elastography

Frontiers in Bioengineering and Biotechnology

Marvin Doyley, Tom Meyer, Bernhard Kreft, Judith Bergs, Erik Antes, Matthias S, Anders, Brunhilde Wellge, J├╝rgen Braun, Heiko Tzsch├Ątzsch, and Ingolf Sack


Cerebral pulsation is a vital aspect of cerebral hemodynamics. Changes in arterial pressure in response to cardiac pulsation cause cerebral pulsation, which is related to cerebrovascular compliance and cerebral blood perfusion. Cerebrovascular compliance and blood perfusion influence the mechanical properties of the brain, causing pulsation-induced changes in cerebral stiffness. However, there is currently no imaging technique available that can directly quantify the pulsation of brain stiffness in real time. Therefore, we developed non-invasive ultrasound time-harmonic elastography (THE) technique for the real-time detection of brain stiffness pulsation.

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Mapping estimates of vascular permeability with a clinical indocyanine green fluorescence imaging system in experimental pancreatic adenocarcinoma tumors

Journal of Biomedical Optics

Marvin M. Doyley, Matthew S. Reed, Marien Ochoa, Kenneth M. Tichauer, Ashley Weichmann, and Brian W. Pogue


Pancreatic cancer tumors are known to be avascular, but their neovascular capillaries are still chaotic leaky vessels. Capillary permeability could have significant value for therapy assessment, and its quantification might be possible with macroscopic imaging of indocyanine green (ICG) kinetics in tissue. The capacity of using standard fluorescence surgical systems for ICG kinetic imaging as a probe for capillary leakage was evaluated using a clinical surgical fluorescence imaging system, as interpreted through vascular permeability modeling.

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Artificial intelligence for diffusion MRI-based tissue microstructure estimation in the human brain: an overview

Frontiers in Neurology

Marvin M. Doyley, Abrar Faiyaz, Giovanni Schifitto, and Md Nasir Uddin


Artificial intelligence (AI) has made significant advances in the field of diffusion magnetic resonance imaging (dMRI) and other neuroimaging modalities. These techniques have been applied to various areas such as image reconstruction, denoising, detecting and removing artifacts, segmentation, tissue microstructure modeling, brain connectivity analysis, and diagnosis support. State-of-the-art AI algorithms have the potential to leverage optimization techniques in dMRI to advance sensitivity and inference through biophysical models. While the use of AI in brain microstructures has the potential to revolutionize the way we study the brain and understand brain disorders, we need to be aware of the pitfalls and emerging best practices that can further advance this field.

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Sound speed estimation for distributed aberration correction in laterally varying media

IEEE Transactions on Computational Imaging

Marvin M. Doyley, Rehman Ali, Trevor M. Mitcham, Melanie Singh, Richard R. Bouchard, and Jeremy J. Dahl


Spatial variation in sound speed causes aberration in medical ultrasound imaging. Although our previous work has examined aberration correction in the presence of a spatially varying sound speed, practical implementations were limited to layered media due to the sound speed estimation process involved. Unfortunately, most models of layered media do not capture the lateral variations in sound speed that have the greatest aberrative effect on the image. Building upon a Fourier split-step migration technique from geophysics, this work introduces an iterative sound speed estimation and distributed aberration correction technique that can model and correct for aberrations resulting from laterally varying media.

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Reverberant magnetic resonance elastographic imaging using a single mechanical driver

Physics in Medicine and Biology

Marvin M. Doyley, Diego A. Caban-Rivera, Juvenal Ormachea, Kevin J. Parker, and Curtis L.


Reverberant elastography provides fast and robust estimates of shear modulus; however, its reliance on multiple mechanical drivers hampers clinical utility. In this work, we hypothesize that for constrained organs such as the brain, reverberant elastography can produce accurate magnetic resonance elastograms with a single mechanical driver. To corroborate this hypothesis, we performed studies on healthy volunteers (n= 3); and a constrained calibrated brain phantom containing spherical inclusions with diameters ranging from 4-18 mm. In both studies (i.e. phantom and clinical), imaging was performed at frequencies of 50 and 70 Hz. We used the accuracy and contrast-to-noise ratio performance metrics to evaluate reverberant elastograms relative to those computed using the established subzone inversion method.

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