Xiao Liang
Assistant Professor
The University of Texas MD Anderson Cancer Center at Houston
Department of Radiation Physics
My research focuses on developing and translating artificial intelligence (AI) and medical imaging technologies to improve precision and efficiency in radiation therapy. Specifically, my research focuses on image conversion, auto-segmentation, CT reconstruction, and deformable image registration to enable adaptive radiotherapy. By combining advanced deep learning methods with clinical data, we aim to create robust, generalizable tools that can be seamlessly integrated into clinical workflows.
Students joining me will gain experience in medical image analysis, AI model development, and data-driven workflow optimization for radiotherapy applications. Projects may involve developing AI models for CBCT-to-CT or MR-to-CT conversion, improving online adaptive treatment planning, evaluating the clinical performance of auto-segmentation algorithms, MR only planning for radiotherapy, or auto-planning. Students will have the opportunity to work closely with clinicians and physicists to translate their computational research into real-world clinical settings, learning how imaging and AI can impact patient care directly.
Our research environment encourages innovation, reproducibility, and cross-disciplinary collaboration between physics, engineering, and oncology. Students will develop practical coding and problem-solving skills, gain familiarity with radiation therapy systems and imaging modalities, and learn how to design experiments that address clinically relevant challenges.
Keywords: Artificial intelligence, medical imaging, deep learning, image synthesis, auto-segmentation, deformable image registration, adaptive radiotherapy, MR-only planning, image-guided radiation therapy.
Education & Training
PhD, The University of Texas Southwestern Medical Center, 2023