Medical Imaging Scientist
I use physics, engineering, and AI to improve MRI in the study of human physiology in health and disease.
About me
Research Interests
Research Interests
- BSc in Biomedical Engineering, Cairo University
- PhD in Electrical and Computer Engineering, Johns Hopkins University
- Diplomat of the American Board of Medical Physics (ABMP)
- Certified MR Safety Expert by the American Board of Magnetic Resonance Safety, MRSE(MRSC)
- Research interests include quantitative parametric MRI and MR spectroscopy and their applications in the study of human physiology
- Special interest in imaging of multiple sclerosis and neurological diseases
- Broad and deep experience in medical image analysis and AI/ML
Research Interests
Research Interests
Research Interests
My research utilized MRI/MRS to advance knowledge in multiple applications including Heart failure, Multiple sclerosis, Alzheimer's disease, Stroke, ACL grafts
Contributed to developing several imaging invocations such as:
- AI-powered segmentation of brain tissue and lesions in MS.
- AI-powered detection of brain abnormalities in Amyloid-targeting treatment in AD
- Detection and quantification of diffusely abnormal white matter in MS
- Functional imaging of dural lymphatic vessels in MS
- AI-based imaging biomarkers of disease activity in MS
- Measuring energy budget in the human heart combining MRI and MRS
- First measurement of of the diffusion of phosphocreatine in human muscle
- Fast image-based acceleration of MRS
- Personalized MRI pulse sequences
- Sensitive detection of brain and spinal cord lesions in MS
- Novel, fast and accurate image reconstruction form non-uniform k-space data
- Complex-valued frequency-domain MR spectral analysis (CircleFit)
- Constructive averaging for SNR boosting in MRS
- Accurate measurement of reaction rates in the human heart
- Quantitative MRI/MRS in stroke
Research Highlights
Functional assessment of dural lymphatics in multiple sclerosis
Selected Publications
- Boudreau M, Karakuzu A, Cohen‐Adad J, Bozkurt E, Carr M, Castellaro M, Concha L, Doneva M, Dual SA, Ensworth A, Foias A. Repeat it without me: Crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge. Magnetic Resonance in Medicine. 2024.
- Sima DM, Vân Phan T, Van Eyndhoven S, Vercruyssen S, Magalhães R, Liseune A, Brys A, Frenyo P, Terzopoulos V, Maes C, Guo J. Artificial Intelligence Assistive Software Tool for Automated Detection and Quantification of Amyloid-Related Imaging Abnormalities. JAMA Network Open. 2024 Feb 5;7(2):e2355800-.
- Broomé S, Belachew S, Caba B, Cavedo E, Dubois J, Gabelle A, Gabr R, Gafson A, Ioannidou D, Jiang X, Jin Y, Oxtoby N, Pang M, Paragios N, Huijbers W. Evaluation of machine learning models that predict Alzheimer’s disease progression in observational studies and randomized clinical trials. #P007. 16th Clinical Trials on Alzheimer’s Disease (CTAD), 2023.
- Gabr R, Caba B, Elliott C, Jiang X, Huijbers W, Tagge I, Arnold DL, Gafson A, Belachew S. Predicting multiple sclerosis slowly expanding lesions using cross-sectional radiomics and machine learning. Joint ECTRIMS/ACTRIMS meeting, P560/115. 2023.
- Spinat Q, Caba B, Ioannidou D, Gafson A, Teboul O, Jiang X, Lepetit V, Arnold D, Gabr R, Cavedo E, Elliott C. Deep Learning Reconstruction of Lesion-Free Representations of the Brain in Multiple Sclerosis Patients (P11-3.006). 2023
- Gabr R, Lincoln J, Hasan K, Kramer L. Contrast-Enhanced Dynamic MRI of Brain Lymphatic Vessels: A Feasibility Study. In MEDICAL PHYSICS 2022 Jun 1 (Vol. 49, No. 6, pp. E641-E641). 2022.
- Gabr RE, Edalati M, Hu L, Chandler A, Zhang W, Narayana PA. Time-segmented and contrast-neutral motion correction using an end-to-end deep learning approach. #0964, ISMRM 2022.
- Musall B, Kamali A, Lincoln J, Ly V, Luo X, Narayana P, Gabr R. Weakly Supervised Convolutional Neural Networks for Segmentation of Diffusely Abnormal White Matter in Multiple Sclerosis. MEDICAL PHYSICS 49 (6), E379-E379. 2022.
- Gabr RE, Lincoln JA, Kamali A, Arevalo O, Zhang X, Sun X, Hasan KM, Narayana PA. Sensitive detection of infratentorial and upper cervical cord lesions in multiple sclerosis with combined 3D FLAIR and T2- weighted (FLAIR3) imaging. American Journal of Neuroradiology. Nov 1;41(11):2062-7. 2020.
- Gabr RE, Coronado I, Robinson M, Sujit SJ, Datta S, Sun X, Allen WJ, Lublin FD, Wolinsky JS, Narayana PA. Brain and lesion segmentation in multiple sclerosis using fully-convolutional neural networks: A large-scale study. Multiple Sclerosis Journal. 2020 Sep;26(10):1217-26.
- Narayana PA, Coronado I, Sujit SJ, Sun X, Wolinsky JS, Gabr RE. Are multi-contrast magnetic resonance images necessary for segmenting multiple sclerosis brains? A large cohort study based on deep learning. Magnetic Resonance Imaging. 2019. In press.
- Narayana PA, Coronado I, Sujit SJ, Wolinsky JS, Lublin FD, Gabr RE. Deep Learning for Predicting Enhancing Lesions in Multiple Sclerosis from Non-contrast MRI. Radiology. 2019. 294(2), 398-404. 2020.
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