Author profile
Icahn School of Medicine at Mount Sinai
To implement a full abdominal motion model that combines respiration with gastrointestinal (GI) motility and quantify its interplay impact in pencil-beam scanning (PBS) proton therapy.
This work aims to develop a physics-aware deep learning framework for radiotherapy dose prediction that improves accuracy and clinical efficiency. The proposed Physics-Aware Multimodal UNet (PhysMM-UNet) integrates CT images, fluence maps, organ masks, and mu...
Accurate real-time tumor tracking is critical for MRI-guided radiotherapy, where geometric uncertainty can significantly increase dose to surrounding critical organs. Continuous cine-MRI enables motion-adaptive treatment. However, accurate tracking under larg...
To quantify the dosimetric consequences of physiology-composed abdominal motion on pancreatic cancer SBRT.
Existing deep learning-based dose prediction methods primarily learn empirical mappings between anatomy and dose, without modeling beam delivery physics. This gap may limit their robustness and accuracy, especially in heterogeneous regions where dose depositi...
To quantify the impact of gastrointestinal (GI) motility on pencil-beam scanning (PBS) proton therapy for abdominal cancers, and assess how fractionation and motion amplitude mitigate motility-induced interplay effects.