AI-Driven Quantitative MRI, Advanced Medical Imaging, and Computational Modeling for Precision Medicine

Overview We are advancing an interdisciplinary biomedical imaging research program at the University of Illinois Chicago that integrates quantitative MRI, artificial intelligence (AI), computational biomechanics, and multimodal biomedical data analysis to develop innovative diagnostic technologies and imaging biomarkers for precision medicine.

Research Focus and Innovation Our research encompasses advanced diffusion MRI (DTI, DKI, NODDI, and WMTI), MRI pulse sequence development, motion correction, image reconstruction, AI-driven medical image segmentation and classification, and physics-based computational modeling.

We integrate machine learning, deep learning, explainable AI, finite element analysis (FEA), and computational fluid dynamics (CFD) with MRI and CT to investigate tissue microstructure, biomechanical properties, physiological function, and disease progression.

Scientific Significance and Applications Our methodologies aim to address critical challenges in biomedical imaging, including diagnostic accuracy, quantitative reproducibility, cross-platform consistency, image quality, and clinical interpretation.

Potential applications include: Neuroimaging: Quantitative brain microstructure, neurological disorders, and multimodal neuroimaging biomarkers. Cancer Imaging: AI-assisted tumor segmentation, tumor grading, tissue characterization, and treatment-response assessment. Computational Biomechanics: Patient-specific finite element modeling of anatomical structures, tissue deformation, and mechanical properties. Cardiovascular Imaging: MRI-based blood-flow assessment, physiological signal processing, and computational hemodynamics. MRI Technology Development: Motion correction, advanced acquisition strategies, quantitative imaging, and image reconstruction.

Research Progress and Capabilities Our research has developed computational pipelines for quantitative diffusion MRI, multi-vendor imaging analysis, deep learning-based disease characterization, and patient-specific biomechanical simulation. These approaches provide a technical foundation for prospective validation, prototype development, and translational research.

Collaboration and Funding Opportunities We seek academic, clinical, and industry partners interested in sponsored research, proof-of-concept studies, pilot funding, joint grant applications, technology co-development, and clinical validation.

Potential partnerships include MRI manufacturers, medical imaging companies, healthcare technology developers, AI and software organizations, biotechnology companies, and research institutions.

We aim to extend existing methodologies through prospective clinical studies, independent validation, improved computational robustness, and integration into practical biomedical imaging workflows, with the long-term objective of developing scalable, reproducible, and clinically meaningful diagnostic technologies.