University of Texas at Arlington

FLASH radiotherapy delivers curative dose to tumor at ultra-high dose rates (UHDR,>40Gy/s) while mitigating normal tissue toxicity. However, data on late-responding tissues are limited, halting its safe clinical translation. Owing to its steep dose–response a...

Inverse radiotherapy treatment planning involves solving a highly non-convex optimization problem where Large language model (LLM) shows promise for its strong interactive reasoning capabilities. We propose a machine-to-machine in-context learning framework w...

This study is to report our recent comprehensive validation of an in-house preclinical alpha irradiation platform with versatile control over fluence rate, energy, temporal, and spatial irradiation patterns.

Accurate assessment of early radiotherapy response in tumors provides crucial guidance for optimizing radiotherapy protocols. We developed a 3D deep learning model termed Attention Med3D based on transfer learning and attention mechanisms for predicting mid-t...

Accurate prediction of tumor response during chemoradiotherapy is essential for treatment optimization but remains challenging. We developed a deep learning model based on a Dual Path Network (DPN), which is a hybrid architecture combining elements of ResNet...