US 12,128,250 B2
Fluence map prediction and treatment plan generation for automatic radiation treatment planning
Qingrong Wu, Durham, NC (US); Yaorong Ge, Durham, NC (US); Fang-Fang Yin, Durham, NC (US); Qiuwen Wu, Durham, NC (US); Chunhao Wang, Durham, NC (US); Yang Sheng, Durham, NC (US); Xinyi Li, Durham, NC (US); and Wentao Wang, Durham, NC (US)
Assigned to DUKE UNIVERSITY, Durham, NC (US); and THE UNIVERSITY OF NORTH CAROLINA AT CHARLOTTE, Charlotte, NC (US)
Filed by DUKE UNIVERSITY, Durham, NC (US)
Filed on Feb. 1, 2022, as Appl. No. 17/590,711.
Claims priority of provisional application 63/143,985, filed on Feb. 1, 2021.
Prior Publication US 2022/0241614 A1, Aug. 4, 2022
Int. Cl. A61N 5/10 (2006.01)
CPC A61N 5/1039 (2013.01) 19 Claims
OG exemplary drawing
 
1. A radiation treatment planning system comprising:
a machine learning system that receives patient data, a physician prescription of target dose, and device data and outputs predicted fluence maps, wherein the machine learning system comprises at least two stages, where a stage of the at least two stages includes converting image scans from the patient data to projection images; and
a treatment planning system that receives the predicted fluence maps from the machine learning system and outputs a treatment plan.