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Dr. Xianjin Dai is a Clinical Assistant Professor and the American Board of Radiology certified Medical Physicist in the Department of Radiation Oncology at Stanford University. Dr. Dai finished the CAMPEP-credited Therapeutic Medical Physics residency at Emory University, and completed his PhD in Biomedical Engineering at the University of Florida. Dr. Dai's research has been focusing on developing and translating novel biomedical imaging techniques for improving the diagnosis, management, and treatment of cancer diseases. His research interests include artificial intelligence in medicine, therapeutic physics, medical image analysis, multimodal imaging, biomedical optics, photoacoustic imaging, ultrasound imaging, and optical coherence tomography. Dr. Dai is a recipient of the DOD Prostate Cancer Research Program (PCRP) Early Investigator Research Award and the American Association of Physicists in Medicine (AAPM) Research Seed Funding Grant.
AI in MedicineBiomedical PhysicsMultimodal ImagingMedical DeviceBiomedical OpticsPhotoacoustic/Thermoacoustic ImagingOptical Imaging (Microscopy, OCT, DOT, FMT)Ultrasound Imaging1. Artificial intelligence (AI) has great potential for improving the efficiency, precision, accuracy, and overall quality of radiation therapy for cancer patients. AI platforms are still not widely adopted into clinical practice due to the challenges associated with the clinical development and implementation of AI-based tools in radiation oncology. The goal of this project is to tackle these challenges with innovative concepts and strategic developments.2. A multimodal imaging platform combining the strengths of several different imaging modalities has the capability of characterizing biological tissue more completely thus offering improved diagnosis, management and treatment of diseases. While multimodality images can be obtained by performing each individual modality separately without integrating them into a single platform, it is, however, time-consuming to acquire multimodality images through such a process, hard to avoid errors from the required complex image registration, and more importantly, impossible to catch dynamic biological processes simultaneously. This project has demonstrated a multimodal imaging system integrating three emerging biomedical imaging techniques, photoacoustic imaging (PAI), optical coherence tomography (OCT), and ultrasound imaging (USI) to obtain optical absorption, scattering, and acoustic properties of tissue simultaneously. Several applications of the multimodal imaging platform have been exploited preclinically. 3. X-ray luminescence computed tomography (XLCT) has been recently proposed as a new imaging modality by detecting the luminescent emission signals arising from the interaction between X-ray and the media. Compared to clinically widely used X-ray CT (anatomical imaging), XLCT represents significant progress in X-ray based imaging techniques as X-ray based molecular or functional imaging becomes achievable in XLCT. Moreover, compared to conventional pure optic-based molecular or functional imaging, XLCT offers two main advantages. First, autofluorescence, problematic for fluorescence imaging can be avoided. Second, deep tissue in vivo imaging with high optical contrast and spatial resolution becomes achievable. However, progress in this area is significantly hindered by technological challenges posed by the fact that currently most XLCT systems take long time to acquire whole-body images (low speed). And XLCT has been entirely relied on conventional nanophosphors emitting light in visible or near-infrared spectrum region (700-1000 nm) with high photon absorption and scattering in biological tissues, which limits XLCT for deeper tissue imaging (insufficient imaging penetration depth) and reduces the spatial resolution (limited spatial resolution). This project has been focused on tackling these challenges with innovative concepts and strategic developments.