Ying Zhang

T1-weighted (T1w) and T2-weighted (T2w) FLAIR MRI provide complementary image contrast for delineating gross tumor volume (GTV) and clinical target volume (CTV) in brain tumor radiotherapy (RT) planning during both MRI simulation (MR-Sim) and MRI-guided RT (M...

The increasing adoption of MRI-guided online adaptive radiotherapy (oART) has improved treatment personalization but introduced substantial technical complexity and time pressure. Existing QA approaches remain largely manual and retrospective, with limited ab...

One major bottleneck in MRI-guided online adaptive radiotherapy is the prolonged treatment process, particularly extended delivery times, partly due to the relatively low dose rate and high prescription doses. Efficient radiation delivery is essential for mai...

Online adaptive prostate MR-guided radiotherapy (MRgRT) is time-sensitive, and contouring with structure preparation can require upwards of 15 minutes per fraction. While vendor-TPS provided contours can be useful, performance and consistency vary by site, pr...

Magnetic resonance imaging-guided online adaptive radiotherapy (MRgRT) requires a complex planning process, during which an upstream peer review prior to physician approval plays an essential role in early error detection and supporting safe and efficient onl...

Timely dose verification is essential for quality assurance (QA) in modern radiotherapy (RT), particularly in online adaptive RT, where measurement-based QA is often impractical. Current approaches are limited by machine/energy-specific designs, hindering sca...

While deep learning autosegmentation models are widely integrated into clinical workflows in radiation oncology, a critical gap has emerged: the "static deployment" trap. Once deployed, model performance can deteriorate due to real-world data evolution, makin...

Accurate lesion segmentation is fundamental to medical image analysis, yet most methods are tailored to specific anatomical sites or modalities, limiting their generalizability in diverse clinical settings. While recent vision-language foundation models enabl...

Treatment planning for MR-guided adaptive radiotherapy (MRgART) requires extensive time and effort in both preplanning and online adaptation processes. It is a major bottleneck hindering the efficiency and quality of MRgART. Specifically, extended preplanning...

AI-based autosegmentation is widely used in radiation oncology to improve efficiency and consistency; however, these models may silently fail when applied to cases that deviate from their training distribution, placing responsibility on clinicians to detect u...

Accurate tumor segmentation is essential for adaptive radiation therapy (ART) but remains time-consuming, labor-intensive, and subject to considerable inter-user variations. Recent advances in foundation models, such as Segment Anything Model 2 (SAM2), show s...