Yabo Fu

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...

Current AAPM guidelines (TG-104 and MPPG 2.b) primarily address kV or standard MV portal beams for imaging and are not well suited for low-dose-rate 2.5 MV imaging beams, particularly in establishing dose and image quality baselines. This study developed and...

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...

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...