Adam Alessio

Adam Alessio

Computational Mathematics, Science, and Engineering (CMSE); Biomedical Engineering (BME) and Radiology; Institute for Quantitative Health Science & Engineering (IQ)
IQ Rm. 1116, BioEngineering Facility
  775 Woodlot Drive
 (517) 432-1708
   aalessio@msu.edu
  www.egr.msu.edu/~aalessio/

About Me

Bio:

Dr. Adam Alessio is a professor in the departments of Computational Mathematics, Science, and Engineering (CMSE), Biomedical Engineering (BME), and Radiology.  His research is focused on non-invasive quantification of disease through advanced imaging algorithms and integrated data analysis.

Dr. Alessio’s research group solves clinically motivated research problems at the intersection of imaging and medical decision-making.  Current efforts center on translational medical research projects for topics including machine learning for quantitative diagnostics, cardiac perfusion estimation, quantitative PET and CT imaging, radiation dose optimization, and system modeling. 

Prior joining MSU, Dr. Alessio was a professor of Radiology at the University of Washington.  He received his Ph.D. in Electrical Engineering at the University of Notre Dame and post-doctoral training in nuclear medicine physics at the University of Washington. He is the author of over 70 peer-reviewed publications, holds 6 patents, and has grant funding from the National Institutes of Health and the medical imaging industry to advance non-invasive cardiac and cancer imaging. 

Research Interests

•     Integrated medical diagnostics

•     Tomographic image reconstruction

•     Hybrid machine learning algorithms

•     Kinetic data analysis

•     Application of AI to healthcare delivery

Selected Publications

[1] Alessio, CW Stearns, S Tong, SG Ross, S Kohlmyer, A Ganin, & PE Kinahan, “Application and Evaluation of a Measured Spatially Variant System Model for PET Image Reconstruction,” IEEE Trans on Medical Imaging, vol. 29:3, pp. 938-949, 2010. 

[2] Alessio, Bassingthwaighte, Glenny, Caldwell, “Validation of an Axially-Distributed Model for Quantification of Myocardial Blood Flow using 13N-Ammonia PET” Journal of Nuclear Cardiology, vol 20:1, pp 64-75, 2013.

[3] Alessio and LR MacDonald, “Quantitative Material Characterization from Multi-Energy Photon Counting CT”, Medical Physics, vol 40:3, 2013.

[4] Perlmutter, Kim, Kinahan, Alessio, “Mixed Confidence Estimation for Iterative CT Reconstruction,” IEEE Trans on Medical Imaging, vol. 35:9, pp 2005-2014, 2016.

[5] Bindschadler, Branch, Alessio, “Quantitative Myocardial Perfusion from Static Cardiac and Dynamic Arterial CT,” Physics in Medicine and Biology, vol 63:10, 2018.

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