Laura I. Cervino, PhD

To address the labor-intensive nature of Deformable Image Registration (DIR) QA quantitatively through the use of independent software and in-house scripting for lung and Head & Neck (H&N) sites for clinical use.

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 evaluate whether two intrafraction CBCT-based motion corrections are sufficient and necessary to ensure accurate target coverage in online adaptive stereotactic body radiotherapy (SBRT) of prostate cancer.

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...

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-...

This work commissioned a manual beam gating procedure for breast DIBH treatment on Ethos and Halcyon (O-ring) platforms, which included dosimetric consistency evaluation with an End-to-End (E2E) test and quantifying the response time of manual beam-hold using...

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...

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...

Despite extensive research on automated treatment planning, manual trial-and-error optimization remains common in clinical practice. Knowledge-based and AI-driven approaches show promise but often lack robustness to evolving clinical protocols due to the need...

Radiotherapy (RT) planning for Head and Neck Cancer (HNC) is resource-intensive and prone to variability. This study proposes and validates a fully automated pipeline synergizing deep learning-based 3D dose prediction with a knowledge-based planning (KBP) tem...