Wake Forest University

Duke University

The University of North Carolina at Charlotte

Yaorong Ge

David K Ahn

David J Vining

David R Stelts

Qingrong Jackie Wu

Fang-Fang Yin

Tiffany W Salido

Paul F Hemler

Gordon W Hunt

Lulin Yuan

Yang Sheng

Chunhao Wang

Qiuwen Wu

Wentao Wang

Xinyi Li

Xiaofeng Zhu

Taoran Li

Manisha Palta

Jiahan Zhang

FangFang Yin

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

According to an aspect, a method includes receiving data about a patient, computing geometric characterization of one or more organs at risk proximate to a target volume of a patient or vice versa, and selecting relevant treatment knowledge and experience. The method also includes generating, based on the received data, computed geometric characterization, and available knowledge and experience, a first set of radiation treatment planning parameters that will lead to a high quality plan for the patient. Further, the method includes model-based prediction, based on the data, a second set or more of radiation treatment planning parameters that will lead to alternative achievable plans with different organ sparing objectives for treating the patient. The multiple sets for parameters can be used separately or in conjunction to generate treatment plans.

Systems and methods for specifying treatment criteria and treatment parameters for patient specific radiation therapy planning

Systems and methods for efficient and automatic determination of radiation beam configurations for patient-specific radiation therapy planning are disclosed. According to an aspect, a method includes receiving data based on patient information and geometric characterization of one or more organs at risk proximate to a target volume of a patient. The method includes determining automatically one or more radiation treatment beam configuration sets. Further, the method includes presenting the determined one or more radiation beam configuration sets via a user interface.

Automatic determination of radiation beam configurations for patient-specific radiation therapy 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

Disclosed herein are systems and methods for specifying treatment criteria and treatment planning parameters for patient specific radiation therapy planning. According to an aspect, a method includes receiving data about a patient, computing geometric characterization of one or more organs at risk proximate to a target volume of a patient or vice versa, and selecting relevant treatment knowledge and experience. The method also includes generating, based on the received data, computed geometric characterization, and available knowledge and experience, a first set of radiation treatment planning parameters that will lead to a high quality plan for the patient. Further, the method includes model-based prediction, based on the data, a second set or more of radiation treatment planning parameters that will lead to alternative achievable plans with different organ sparing objectives for treating the patient. The multiple sets for parameters can be used separately or in conjunction to generate treatment plans.

An apparatus and method for automatically generating radiation treatment planning parameters are disclosed. In accordance with the illustrative embodiment, a database is constructed that stores: (i) patient data and past treatment plans by expert human planners for these patients, and (ii) optimal treatment plans that are generated using multi-objective optimization and Pareto front search and that represent the best tradeoff opportunities of the patient case, and a predictive model (e.g., a neural network, a decision tree, a support vector machine [SVM], etc.) is then trained via a learning algorithm on a plurality of input/output mappings derived from the contents of the database. During training, the predictive model is trained to identify and infer patterns in the treatment plan data through a process of generalization. Once trained, the predictive model can then be used to automatically generate radiation treatment planning parameters for new patients.

Automatic generation of patient-specific radiation therapy planning parameters

A system, and computer implemented method are provided for interactively displaying three-dimensional structures. Three-dimensional volume data () is formed from a series of two-dimensional images () representing at least one physical property associated with the three-dimensional structure, such as a body organ having a lumen. A wire frame model of a selected region of interest is generated (). The wireframe model is then deformed or reshaped to more accurately represent the region of interest (). Vertices of the wire frame model may be grouped into regions having a characteristic indicating abnormal structure, such as a lesion. Finally, the deformed wire frame model may be rendered in an interactive three-dimensional display.

Virtual endoscopy with improved image segmentation and lesion detection

A method and system are provided to report the findings of an expert's analysis of image data. The method and system are based on a reporting system that forms the basis of an image management system that can efficiently and systematically generate image reports, facilitate data entry into searchable databases for data mining, and expedite billing and collections for the expert's services. The expert identifies a significant finding on an image and attaches a location:description code to the location of that finding in order to create a significant finding and an entry into a database. Further descriptions of that finding, such as dimensional measurements, may be automatically appended to the finding as secondary attributes. After the evaluation, the system sorts the findings in the database and presents the findings by prioritized categories. The expert edits and approves a multimedia report which may be delivered by electronic means to an end-user.

Image reporting method and system

A computer system and a computer-implemented method are provided for interactively displaying a three-dimensional rendering of a structure having a lumen and for indicating regions of abnormal wall structure. A three-dimensional volume of data is formed from a series of two-dimensional images representing at least one physical property associated with the three-dimensional structure. An isosurface of a selected region of interest is created by a computer from the volume of data based on a selected value or values of a physical property representing the selected region of interest. A wireframe model of the isosurface is generated by the computer wherein the wireframe model includes a plurality of vertices. The vertices are then grouped into populations of contiguous vertices having a characteristic indicating abnormal wall structure by the computer. The wireframe model is then rendered by the computer in an interactive three-dimensional display to indicate the populations of abnormal wall structure.

Growing community of inventors

Winston-Salem, NC, United States of America

Yaorong Ge