Xiang Li, PhD

We investigate the feasibility of using daily setup free-breathing cone-beam CT (CBCT) as a functional imaging modality to generate longitudinal ventilation maps throughout the treatment course, with the goal of detecting emerging lung function impairment dur...

To improve the effectiveness and efficiency of radiotherapy clinical workflows, we developed and deployed three vendor-specific Event-Driven Framework (EDF) automation applications that reduce manual intervention and enhance communications among therapists, p...

An improved imager and software (HyperSight on a Varian TrueBeam linac) feature a larger detector panel and increase the maximum gantry rotation speed for CBCT imaging to 1.5 revolutions per minute (rpm). While enabling faster acquisition, its impact on free-...

Inferior CBCT quality from artifacts or incomplete data can compromise anatomy visualization during Image-Guided Radiotherapy (IGRT), increasing uncertainty in target localization and organ-at-risk positioning. Improving CBCT reconstruction can enable more re...

To develop a kV-triggered short-arc intrafraction motion monitoring technique for prostate SBRT VMAT by enabling on-treatment reconstruction of a 3D prostate and nearby organs-at-risk (OARs) volume within seconds. We propose an iterative short-arc CBCT recons...

To assess how low-pitch spiral 4DCT impacts image quality, particularly noise, artifacts, and slice sensitivity profile (SSP), to help overcome the limitations of this technique.

Electronic portal imaging devices (EPID) are widely used clinically for patient-specific quality assurance (QA). Anticipating potential failures can help prioritize measurements to assess the need for plan revision and avoid downstream workflow disruptions. W...

This study proposes a transformer-based deep learning framework for markerless lung tumor tracking that improves localization accuracy, robustness, and computational efficiency of real-time intrafraction motion management for seamless clinical integration.

Markerless lung tumor tracking has the potential to reduce target margins and improve organ-at-risk (OAR) sparing during radiotherapy. We previously proposed a deep learning–based target decomposition approach for real-time markerless lung tumor tracking. Thi...

Iodine maps derived from Dual-Energy CT (DECT) provide critical biological information for radiotherapy treatment planning; however, conventional clinical iodine maps often mistakenly include bones due to insufficient X-ray spectral separation. In this study,...

Cone beam has been a mainstay modality across a wide range of clinical applications. Ongoing innovations in both hardware and software continue to expand its capabilities to address the evolving needs for improved image quality, dose efficiency, and workflow...

Therapy Physics