MR Biophysics Lab, Buffalo Neuroimaging Analysis Center

Biological specificity in MRI Clinical translation of iron MRI

Ilyes holds a Master’s degree in Medical Physics from Columbia University and a Bachelor’s from Loyola University (New Orleans). He is currently a PhD student in the Medical Physics program at UB. Ilyes has developed a post-processing tool, BIOPHYSICSS-DL, that utilizes machine learning to determine and test biophysical models of quantitative MRI metrics. His innovative approach has the potential to provide further insight into the causes and progression of neurodegenerative diseases such as Multiple Sclerosis.Upon graduation, Ilyes hopes to pursue clinical work as a medical physicist, as well as continuing his research.

In his free time, Ilyes enjoys travelling, writing science-fiction, creating tabletop games, and reading up on mythology. These activities provide him with a creative outlet to help him refresh outside of the lab.

Conventional χ-separation compared to self-calibrated method (BIOPHYSICSS-DL) with histological validation

Benslimane I, Grabner G, Hametner S, Jochmann T, Zivadinov R, Schweser F

Proc Intl Soc Mag Reson Med, Toronto, ON, Canada, 2023

Myelin, Iron, and Free Water Content Quantification Through BIOPHYSICSS Deep Learning.

Benslimane I., Grabner G., Hametner S., Jochmann T., Zivadinov R., Schweser F.

Proc Intl Soc Mag Reson Med, Toronto, ON, Canada, 2023

Improved self-calibrated signal model for χ-separation using single-subject (N=1) physics-constrained deep learning with histological validation.

Benslimane I, Grabner G, Hametner S, Jochmann T, Zivadinov R, Schweser F

Artificial Intelligence in Clinical Imaging (AICI) Forum, Graz, Austria, 2022

Beyond qMRI: Biological tissue properties forom single-subject unsupervised deep learning with theoretical signal constraints

Benslimane I, Grabner G, Hametner S, Jochmann T, Zivadinov R, Schweser F

Proc Intl Soc Mag Reson Med, 2022, p. 370