Duke University

The University of North Carolina at Charlotte

Yang Sheng

Yaorong Ge

Qingrong Jackie Wu

Fang-Fang Yin

Chunhao Wang

Qiuwen Wu

Jiahan Zhang

FangFang Yin

Xinyi Li

Manisha Palta

Wentao Wang

Taoran Li

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

Systems and methods for radiation treatment planning based on a model of planning strategies knowledge including treatment states and treatment actions are disclosed. According to an aspect, a method includes receiving geometric characterization data of a target volume for radiation treatment of a patient. The method also includes receiving geometric characterization data of at least one organ at risk proximate the target volume. Further, the method includes constructing a model for applying a predetermined radiation dosage to the target volume based on the received data. The model includes treatment states and associated treatment actions selectable to implement at each state. The method includes presenting information about at least one treatment state, the treatment actions associated with the at least one treatment state, and the rewards associated with the treatment actions associated with the at least one treatment state. The method also includes reconstructing the model.

Systems and methods for radiation treatment planning based on a model of planning strategies knowledge including treatment planning states and actions

Systems and methods for automatic, customized radiation treatment plan generation for cancer are disclosed. According to an aspect, a method includes receiving data indicating anatomy information of a patient and radiation beam characteristics of a radiation therapy system. Further, the method includes determining energy levels for application of radiation beams to the patient.

Systems and methods for automatic, customized radiation treatment plan generation for cancer

Growing community of inventors

Durham, NC, United States of America

Yang Sheng