Eric Aliotta, 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.

Quantify dosimetric differences between original online adaptive plans (adapt-to-position [ATP] or adapt-to-shape [ATS]) and baseline shift (BLS)–corrected plans for prostate MRgRT on 1.5 Tesla MR-Linac. BLS is a fast, during treatment, replan applying virtua...

Tumor hypoxia drives radio-resistance in Head & Neck cancer (HNC) and can guide radiation dose de-escalation. Tumor hypoxia can be imaged using 18F-fluoromisonidazole (FMISO-PET; non-standard-of-care). Because hypoxia drives glycolysis, we assessed correlatio...

Conventional diffusion-weighted MRI (DWI) uses single-shot echo planar imaging (ssEPI), which is highly susceptible to geometric distortion, limiting its use for radiotherapy guidance on MR-LINAC systems. Turbo-spin-echo (TSE) and multi-shot EPI (msEPI) DWI r...

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...

To quantify longitudinal changes in Diffusion Weighted MRI (DWI) acquired during MR-LINAC adaptive radiotherapy for pancreatic cancer and correlate with clinical outcomes.