Yassine Toufique

Description

Dr. Yassine Toufique is an accomplished scientist, educator, and innovator in the fields of biomedical physics, digital engineering, and artificial intelligence in healthcare. He currently holds the position of Assistant Professor of AI and Digital Engineering at Long Island University (LIU) in New York, and serves as a Scientific Researcher at the Center of Excellence in Biomedical Simulation, a strategic partnership with Dassault Systèmes. His work bridges the domains of computational modeling, medical image processing, and patient-specific digital twins, with the overarching goal of transforming clinical practice through predictive simulation and intelligent systems.

Dr. Toufique earned his PhD in Medical Physics and Biomedical Imaging from Mohammed V University in Rabat, Morocco, where he specialized in Monte Carlo simulations for clinical PET systems and developed adaptive filtering algorithms for medical images. His academic excellence has been recognized with several distinctions, including a merit scholarship from the IFIC institute in Valencia, Spain, and top-ranking honors during his undergraduate and master’s studies.

With a research career spanning over a decade across international institutions—including Texas A&M University at Qatar, IFIC Valencia, and LIU New York—Dr. Toufique has been instrumental in leading interdisciplinary simulation projects such as the Living Heart and Living Esophagus. These initiatives integrate finite element modeling, AI, and multi-scale physiological data to build clinically-relevant digital twins aimed at enhancing diagnosis, treatment planning, and personalized care.

He is also the co-inventor of an international patent (PCT/QA2020/050007, June 2, 2020) titled “Method and System for Non-Invasive Measurement of Arterial Time-Activity Curves in PET Imaging.” This innovation eliminates the need for invasive blood sampling by leveraging external detectors and advanced Monte Carlo simulations to accurately estimate the concentration of radioactive tracers in arterial blood. The technology holds transformative potential in PET imaging, particularly for applications in oncology, cardiology, and neurology, making procedures safer and more efficient for both patients and clinicians.

In addition to his research, Dr. Toufique plays a critical role in curriculum development and student mentorship at LIU, where he teaches foundational and applied courses in AI and mathematics. He is a long-standing contributor to the FLUKA Collaboration at CERN, where he supports the development of simulation tools for both medical and high-energy physics. Dr. Toufique is the author and co-author of numerous peer-reviewed publications, conference papers, and invited talks presented internationally, reflecting his continued commitment to scientific excellence, translational research, and academic mentorship.

Research

Dr. Yassine Toufique’s research is centered on the integration of computational modeling, Monte Carlo-based simulation, and artificial intelligence for advanced applications in medical imaging and personalized medicine. His work explores the mechanistic underpinnings of image formation and radiation transport through the development and validation of physics-based simulation pipelines using platforms such as GATE, FLUKA, and GEANT4. These simulations have been applied to optimize image quality, assess dose distribution in radiotherapy, and guide the design of novel PET and CT imaging systems. In the domain of digital twins, Dr. Toufique leads the development of patient-specific, finite-element–based biomechanical models—most notably within the Living Heart and Living Esophagus projects—designed to simulate pathophysiological behavior under real-world clinical constraints. His current work also incorporates explainable deep learning for anatomically-informed segmentation of cardiac and thoracic imaging modalities, bridging data-driven models with physiological priors to enhance interpretability. A key translational component of his research is represented by an international patent (PCT/QA2020/050007) describing a non-invasive technique for estimating arterial input functions in PET imaging using external detection systems and Monte Carlo modeling, addressing a critical barrier in dynamic quantitative PET. Through interdisciplinary collaborations with clinical, academic, and industrial partners—including CERN, Dassault Systèmes, and Texas A&M University—Dr. Toufique’s research continues to advance the scientific foundation of personalized, simulation-driven healthcare technologies.

Distinctions & Awards

2007 – 2nd Prize, Bachelor’s Degree in Physics
Université Mohammed V – Rabat, Morocco

2009 – 1st Prize, Specialized Master in Medical Physics
Université Mohammed V – Rabat, Morocco

2012 – Excellence Scholarship, PCI Project A1/035250/11
IFIC – Instituto de Física Corpuscular, Valencia, Spain

2013 – Selection for IFPU Summer School
Charles University, Prague, Czech Republic

2020 – Best Poster Award, 11th Annual Scientific Meeting
Irish Association of Physicists in Medicine (IAPM)

Patents

• “Method and System for Non-Invasive Measurement of Arterial Blood Time-Activity Curves in PET Imaging,” (with J. O’Doherty and O. Bouhali); International Patent Application No. PCT/QA2020/050007 (June 2, 2020).

Selected Publications

Book Chapter

• • Adjustments to Nuclear Medicine Physics Services in Response to the Pandemic In: Medical Physics during the COVID-19 Pandemic: Global Perspectives in Clinical Practice, Education and Research, CRC Press.

Peer-Reviewed Journal Articles

• Explainable Hybrid Attention Networks for Anatomically Guided Segmentation of Cardiac MRI
  Frontiers in Cardiovascular Medicine (Submitted, 2025)
• GATE Monte Carlo Approach to Heterogeneity Dose Distribution in Small Fields Used in Radiation Therapy
  Biomed. Phys. Eng. Express, 10, 035021 (2024). DOI: 10.1088/2057-1976/ad36cd
• Monte Carlo Simulation of the System Performance of a Long Axial Field-of-View PET with Monolithic LYSO Detectors
  EJNMMI Physics (Accepted, 2023)
• Simulation Study of a Coincidence Detection System for Non-Invasive Arterial Blood Time-Activity Curve Measurement
  EJNMMI Physics, 7, 25 (2020)
• GATE Simulation Study of the Siemens Biograph mCT20 Excel PET/CT System
  Polish Journal of Medical Physics and Engineering, 25 (2019): 7–14
• CT Dose Index (CTDI) Assessment Using GEANT4/GATE
  Perspectives in Science, 12 (2019): 100405
DOI: 10.1016/j.pisc.2019.100405
• Monte Carlo Studies of Sensitivity and Scatter Fraction of a Total Body PET with Monolithic Scintillators
  Eur. J. Nucl. Med. Mol. Imaging, 45 (2018): S717
• Overview of Recent Developments in FLUKA PET Tools
  Physica Medica, 54 (2018): 189–199
• Adaptive Anisotropic Diffusion Algorithm for Medical Image Enhancement
  Journal of Engineering and Applied Science, 60(4), 441–458 (2013)
• Simulation Study of a Coincidence Detection System for Arterial Time-Activity Curve Estimation
  Physica Medica, 84 (2021): 301

Conference Proceedings (Selected)

• Monte Carlo Modeling of VARIAN 2300C/D Accelerator, CompBioMed 2019, London
• Simulation of Dual-Energy CT Systems, ECR 2019, Vienna
• Validation of GATE for Radiation Therapy, ICOA 2018, Morocco
• Monte Carlo Study of Scattered Radiation in CBCT, ECR 2020, Vienna
• Adaptive Diffusion Filter for Ultrasound, MECBME 2014, Qatar
• Grid-Based PET Simulations, AICCSA 2013 & IberGrid 2012
• PET Scanner Validation with FLUKA, ICTR-PHE 2016, Geneva