Duke University

The University of North Carolina at Charlotte

Qiuwen Wu

Yaorong Ge

Fang-Fang Yin

Qingrong Jackie Wu

Yang Sheng

Chunhao Wang

Bo Liu

Xinyi Li

Wentao Wang

Xile Zhang

Fugen Zhou

A radiation treatment planning system can include a machine learning system that receives patient data, including an image scan (e.g., CT scan) and contour(s), a physician prescription, including planning target and dose, and device (radiation beam) data and outputs predicted fluence maps. The machine learning system includes at least two stages, where a stage of the at least two stages includes converting image scans from the patient data to projection images. A treatment planning system can receive the predicted fluence maps and generates treatment plans without performing inverse optimization.

Fluence map prediction and treatment plan generation for automatic radiation treatment planning

Example methods and systems for photon energy synthesis and introducing energy modulation into radiation treatment planning process and delivery process are provided. Example methods comprise obtaining, via a linear accelerator, a first photon energy beam dosimetric data, a second photon energy beam dosimetric data, and an intermediate photon energy beam dosimetric data, and fitting the first photon energy beam dosimetric data and the second photon energy beam dosimetric data according to a preset fitting coefficient. A synthesized photon beam is then generated from dosimetric data from the fitted first photon energy beam dosimetric data and the fitted second photon energy beam dosimetric data. Further, continuously variable photon energies can be derived, where two or more existing photon energy beams are combined linearly and applied to both forward and inverse radiation planning modules for a radiation treatment plan.

Systems and methods for synthetic photon radiation therapy

Growing community of inventors

Durham, NC, United States of America

Qiuwen Wu