- Ph.D., Bioengineering (major) and Electrical Engineering (minor), The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- M.S., Biomedical Engineering (major), Faculty of Engineering, Cairo University, Giza, Egypt
- B.S., Biomedical Engineering, Faculty of Engineering, Cairo University, Giza, Egypt
The purpose behind my research from the beginning of my career has been the use of MRI to come up with and develop non-invasive imaging biomarkers to characterize anatomical as well as physiologically associated changes in diseased human organs.
Our current research in our lab focuses on discovery and development of quantifiable non-invasive MRI biomarkers through MR pulse sequence development and image analysis to characterize tissue changes in the liver and kidney resulting from metabolic disorders. Our long-term goal is to identify quantifiable non-invasive MR biomarkers that are consistently reproducible in characterizing fibrosis and inflammation in the liver and kidney to ultimately replace the need for invasive biopsy procedures. We are also actively applying machine learning techniques in order to accelerate the MR image acquisition and analysis processes to minimize time spent by patients inside the MR scanner.
- Native-resolution myocardial principal Eulerian strain mapping using convolutional neural networks and Tagged Magnetic Resonance Imaging.
- Yassine IA, Ghanem AM, Metwalli NS, Hamimi A, Ouwerkerk R, Matta JR, Solomon MA, Elinoff JM, Gharib AM, Abd-Elmoniem KZ.
- Comput Biol Med (2022 Feb) 141:105041. Abstract/Full Text
- Direct pixel to pixel principal strain mapping from tagging MRI using end to end deep convolutional neural network (DeepStrain).
- Abd-Elmoniem KZ, Yassine IA, Metwalli NS, Hamimi A, Ouwerkerk R, Matta JR, Wessel M, Solomon MA, Elinoff JM, Ghanem AM, Gharib AM.
- Sci Rep (2021 Nov 26) 11:23021. Abstract/Full Text
Research in Plain Language
Our team is trying to come up with and develop quantitative and reproducible non-invasive measures from MRI to improve diagnosis and follow-up experiences for patients with chronic liver and kidney disease. We also look for ways to apply artificial intelligence and machine learning techniques in our work to also improve patient experiences.