The University of Texas System

Rensselaer Polytechnic Institute

Xun Jia

Steve B Jiang

Xuejun Gu

Ge Wang

Daniel David Harrison

Vasant Kearney

Weiguo Lu

Klaus Mueller

Mingli Chen

Ko-ay Timmy Siauw

In some embodiments, a method of machine learning includes identifying, by an auto encoder network, a simulator feature based, at least in part, on a received first simulator data set and an emulator feature based, at least in part, on a received first emulator data set. The method further includes determining, by a synthesis control circuitry, a synthesized feature based, at least in part, on the simulator feature and based, at least in part, on the emulator feature; and generating, by the auto encoder network, an intermediate data set based, at least in part, on a second simulator data set and including the synthesized feature. Some embodiments of the method further include determining, by a generative artificial neural network, a synthesized data set based, at least in part, on the intermediate data set and based, at least in part, on an objective function.

CT big data from simulation, emulation and transfer learning

The present disclosure is directed towards a treatment planning system for use in a stereotactic radiotherapy system. In particular, the disclosed systems and methods may be used for generating a treatment plan and/or verifying an existing treatment plan. Moreover, the disclosed systems and methods may be suitable for use in a clinical setting. A method for verifying a treatment plan of a stereotactic radiotherapy device may include the steps of receiving a treatment plan, generating a second treatment plan by applying a modified monte-carlo method to regions of interest in the treatment plan, and identifying discrepancies between the received treatment plan and the generated second treatment plan.

Independent stereotactic radiotherapy dose calculation and treatment plan verification

A cloud-based radiation therapy treatment planning system (TPS). The TPS changes the clinical workflow of the radiotherapy treatment planning process, by increasing the mobility and computational power of the treatment planning software and hardware architecture. The system is divided into computational components. A user device includes a light and flexible user interface, while the server side entails a hospital relay server, and/or a graphics processing unit cluster server. The TPS computational architecture enables the computational power of a GPU-cluster server on a tablet, laptop, or smartphone.

Systems and methods for cloud-based radiation therapy treatment planning

Growing community of inventors

Dallas, TX, United States of America

Xun Jia