Our research focuses on three core directions: intelligent neural navigation, image-enabled brain-computer interfaces, and machine learning for smart healthcare. We combine rigorous methodology with close clinical collaboration to build systems that work in the real world.
我们如何打通 diffusion MRI 伪影校正与质量评估之间的关键瓶颈
我们绕开了 tractography 依赖：AutoATQ 解决沿束定量中“重建误差传导”的关键瓶颈
We design explainable, privacy-preserving machine learning systems for clinical decision support—from early detection of neurological disorders to treatment response prediction in oncology.
We build closed-loop brain-computer interface systems that decode visual cortex activity to reconstruct perceived imagery and enable communication for patients with severe motor impairments.
We develop AI-driven navigation frameworks that fuse intraoperative imaging with preoperative MRI/CT to guide neurosurgical instruments in real time, achieving sub-millimeter targeting accuracy.