PhD defence B. (Bo) Li

Below is a brief summary of the dissertation:

This thesis develops deep learning approaches to enhance brain MRI analysis across three key areas: diffusion MRI analysis, interpretable AI for aging and dementia studies that minimize the effects of confounding, and clinical translation of AI-driven neuroimaging methods. We introduce automated, robust techniques for white matter bundle segmentation and spatial registration of diffusion MRI, improving tract-specific analysis in both cross-sectional and longitudinal studies, achieving comparable accuracy and robustness to methods developed for superior-quality diffusion MRI data while ensuring applicability to large-scale population studies. Beyond diffusion MRI, we address challenges in mitigating confounding effects and enhancing interpretability in deep learning-based imaging studies. Lastly, we bridge the gap between AI advancements and clinical applications by developing tailored frameworks for real-world challenges, including longitudinal glioma analysis and cerebral small vessel disease detection. By advancing these methodologies, this thesis contributed to improved accuracy, reproducibility, and clinical utility of AI-driven neuroimaging analysis, supporting population studies and neurological disease research.