Jeff Fessler receives Distinguished Faculty Achievement Award

Prof. Fessler has revolutionized medical imaging with groundbreaking mathematical models and algorithms that improve both safety and quality.

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Jeffrey Fessler, a world-renowned leader in medical image reconstruction, has been selected to receive a 2015 Distinguished Faculty Achievement Award from the U-M Rackham Graduate School. This award honors senior faculty who have consistently demonstrated outstanding achievements in scholarly research, have a sustained pattern of high quality teaching and mentoring of students and junior colleagues, and have contributed constructively to the University community through service and other professional activities that have brought distinction to themselves and to the University of Michigan.

Prof. Fessler has revolutionized the theory and practice of medical imaging with his group’s groundbreaking mathematical models and algorithms that significantly improve both patient safety and image quality. He has produced major improvements in the theory, design and clinical use of scanners in three of the principal clinical scanner modalities: radionuclide imaging (PET/SPECT), magnetic resonance imaging (MRI), and X-ray computer tomograhy (X-ray CT).

As increased use of X-ray CT has raised concerns about lifetime medical radiation exposure, Fessler worked with GE on the first commercial image reconstruction method for positron emission tomography in the late 1990s. He also collaborated with other U-M scientists on an algorithm for single-photon emission computed tomography that has benefited thousands of cardiac patients. His research has directly benefited patients by lowering radiation dosages and improving medical diagnoses. His group is currently working to reduce image reconstruction time to help make low-dose CT scans a viable screening tool for lung cancer and to reduce scan time in magnetic resonance imaging.

An outstanding educator, Prof. Fessler has received the student-voted (primarily by undergraduate students) Eta Kappa Nu Teacher of the Year award as well as the Rackham Distinguished Graduate Mentor Award. In addition, he has received teaching awards from the Department of Biomedical Engineering and the College of Engineering. He was instrumental in introducing signal and image processing into the curriculum at Michigan, and teaches a broad array of classes, from introductory to advanced graduate courses. In addition, he has written the most comprehensive book currently available on the topic of iterative image reconstruction, which is distributed electronically free of charge.

Prof. Fessler holds eight U.S. patents and is author or co-author of about 375 journal and conference papers. Twenty-seven of these papers have been cited more than 100 times according to Google Scholar. Twelve have won awards from leading journals and conferences. A champion of open research, he was among the first to provide his software and data sets online, enabling others to replicate his group’s findings. His early adoption of reproducible research principles has become a model for the community.

He received the 2013 Edward J. Hoffman Medical Imaging Scientist Award, which is one of the highest awards in the field of medical imaging science, “for contributions to the theory and application of statistical image reconstruction methods in nuclear medicine, X-ray CT, and magnetic resonance imaging.” He is a Fellow of IEEE.

In addition to his home department, Electrical Engineering and Computer Science, Prof. Fessler holds courtesy appointments in Biomedical Engineering and Radiology. He is currently Associate Editor of IEEE Transactions on Computational Imaging, and has served the medical imaging community in many capacities throughout his career.

Prof. Fessler will receive his award at a special ceremony October 5, 2015.


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This article was adapted from: Thirty faculty members honored for scholarship, service, The Record, October 5, 2015

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Honors and Awards; Jeffrey Fessler; Medical Imaging; Profile; Signal & Image Processing and Machine Learning