Current Research and Scholarly Interests
Intelligent Image-guided intervention
AI in Medicine
Medical Image Analysis
Biomedical Physics
Multimodal Imaging
Ultrasound Imaging
Medical Device
Biomedical Optics (Optical, Photoacoustic, OCT)
1. Artificial intelligence (AI) holds significant promise for improving the efficiency, precision, accuracy, and overall quality of radiation therapy for cancer patients. However, despite rapid technical advances, AI platforms have not yet been widely adopted in clinical practice due to persistent challenges in the development, validation, and implementation of AI-based tools in radiation oncology. The goal of this work is to address these barriers through innovative methodological concepts and strategic translational developments.
2. Image-guided intervention leverages real-time imaging to support precise targeting, navigation, and decision-making during diagnostic and therapeutic procedures. While conventional image guidance often relies on preoperative images or single-modality intraoperative imaging, these approaches are limited by anatomical deformation, temporal delays, and incomplete visualization of critical structures. Our goal is to address these limitations through advanced image-guided intervention strategies that integrate real-time imaging, intelligent modeling, and adaptive guidance to improve procedural accuracy, safety, and clinical outcomes.
3. Integrative multimodal imaging platforms leverage the complementary strengths of multiple imaging modalities to achieve comprehensive biological tissue characterization, enabling improved disease diagnosis, management, and treatment. Sequential acquisition of individual modalities is inefficient, vulnerable to registration error, and fundamentally limited in its ability to capture dynamic biological processes. Our goal is to address these constraints through advanced methodological innovations and targeted translational development.
