Michael Zeineh

Michael Zeineh is an Associate Professor in the Department of Radiology at Stanford University School of Medicine, specializing in advanced neuroimaging techniques to study brain microstructure in neurological disorders and trauma. His research bridges engineering, neuroscience, and clinical radiology with significant contributions to traumatic brain injury biomechanics, Alzheimer's disease biomarker validation, and AI-enhanced imaging.

His educational background includes:

• Bachelor of Science in Biology from Caltech (1995)
• M.D.-Ph.D. from UCLA (2003)
• Radiology Residency at Stanford University (2004-2008)
• Neuroradiology Fellowship (2008-2010)

Dr. Zeineh's research program focuses on three interconnected domains: (1) Traumatic brain injury biomechanics, where he pioneers instrumented mouthguard technology to quantify head impacts in sports and develops computational models for brain strain prediction; (2) Neurodegenerative disease imaging, particularly Alzheimer's disease, using multi-modal MRI/PET to validate histology-correlated biomarkers; and (3) AI-driven image enhancement, applying deep learning for microstructure prediction, image denoising, and genomic analysis in chronic fatigue syndrome. His lab integrates physics-based modeling with machine learning to solve clinical neuroimaging challenges.

The 2025 publications reveal accelerating convergence of clinical guidelines development (e.g., ASNR consensus statements for PET/fMRI), precision biomechanics (head impact subtyping and strain modeling), and translational AI applications (diffusion models for 7T MRI, transfer learning for microstructure prediction). These works consistently target clinical implementation barriers in neuroimaging.

Dr. Zeineh actively mentors students through directed research courses across Biomedical Engineering (BIOE 391/392), Neurosciences (NEPR 299), and Radiology (RAD 101/199/280/299/370/399), supervising undergraduate, graduate, and medical scholars. His advising emphasizes cross-disciplinary technical skills in imaging physics, computational modeling, and clinical translation.

His laboratory develops integrated neuroimaging platforms combining high-field MRI (7T), PET/MRI, instrumented impact sensors, and computational models. Current projects focus on real-time concussion detection systems, micron-resolution MRI-histology validation pipelines for Alzheimer's biomarkers, and AI-optimized deep brain stimulation targeting for neurodegenerative disorders.