Ultra-high-field & quantitative MRI
Using 7-Tesla and quantitative MRI to characterize early microstructural change and map magnetic susceptibility, iron, myelin, and water content in subcortical brain structures.
Research
Quantitative imaging for neurodegenerative disease.
My expertise bridges MRI physics with clinical applications, employing multimodal data integration—combining high-resolution imaging with omics and clinical phenotypes—to map iron, myelin, and water content in the brain, especially in subcortical nuclei. I have experience utilizing advanced neuroimaging techniques, including structural MRI, QSM, DTI, and fMRI, to investigate neurodegeneration and neuroinflammation.
My ongoing research focuses on integrating artificial intelligence with mechanistic modeling to develop open-science, AI-driven diagnostic tools that translate complex multi-omics and imaging data into precise clinical biomarkers.
Research directions
From tissue properties to integrated biomarkers
The following themes organize the research described across the current website, CV, projects, and publication record.
Neurodegenerative disease
Studying Parkinson’s disease, amyotrophic lateral sclerosis, Alzheimer’s disease, mild cognitive impairment, and related neuroinflammatory processes through imaging biomarkers.
Multimodal neuroimaging
Combining structural, diffusion, functional, perfusion, spectroscopy, and quantitative susceptibility measures with clinical and neuropsychological phenotypes.
Neuroinformatics & multimodal integration
Integrating high-resolution imaging with omics and clinical phenotypes to investigate disease mechanisms and identify interpretable biomarker patterns.
AI & mechanistic modeling
Applying artificial intelligence, generative computational models, and mechanistic modeling to translate complex imaging and multi-omics data into clinically meaningful biomarkers.
Diffusion, glymphatic & microstructure imaging
Using diffusion-derived measures—including DTI, DKI, DTI-ALPS, and related microstructure approaches—to investigate tissue integrity and glymphatic pathway dysfunction.
Approach
An interdisciplinary imaging workflow
A concise view of how the research themes connect, without adding claims beyond the source material.
Acquire
High-resolution, multimodal, and quantitative MRI data.
Quantify
Structural, microstructural, susceptibility, perfusion, metabolic, and network measures.
Integrate
Imaging features with omics, clinical, and neuropsychological phenotypes.
Model
AI, biological-age, generative, and mechanistic computational approaches.
Academic formation
Radiology to neuroimaging
October 2019 – July 2024
PhD in Neuroimaging
Tehran University of Medical Sciences
A doctoral program focused on imaging methodologies for exploring brain structure and function, encompassing imaging physics and techniques, neuroscience and neuroanatomy, and image processing and interpretation.
Thesis Evaluation of the Structural and Functional Changes of the Brain in Amyotrophic Lateral Sclerosis (ALS) to Identify the Biomarkers of the Disease and Help for a Targeted Treatment
September 2016 – September 2019
MSc in Medical Imaging Technology
Tabriz University of Medical Sciences
A postgraduate degree focused on advanced imaging technologies, image processing and interpretation, and research in diagnostic radiology.
Thesis Magnetic Resonance Imaging Properties of Gadolinium (III)-Loaded Aluminosilicate Nanocomposite Containing Doxorubicin with Folic Acid Coating
September 2012 – June 2016
BSc in Radiology
Hamadan University of Medical Sciences
Focused on diagnostic imaging, radiologic technology, anatomical and physiological sciences, and patient care.
September 2008 – July 2012
Diploma in Experimental Sciences
Zanyaran High School — High School for Elite Students