Elekta has developed a radiation therapy treatment system using machine learning and neural networks to predict radiation therapy doses. The system utilizes three-dimensional medical images and anatomy maps to train the neural network model, providing accurate three-dimensional dose distributions for improved treatment planning. GlobalData’s report on Elekta gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Elekta, Vaping device aerosol generators was a key innovation area identified from patents. Elekta's grant share as of January 2024 was 54%. Grant share is based on the ratio of number of grants to total number of patents.
Radiation therapy system using machine learning for dose prediction
A recently granted patent (Publication Number: US11850445B2) outlines a radiation therapy treatment system designed to predict radiation therapy doses. The system includes an image acquisition device to capture three-dimensional medical images, a machine-readable medium to store these images along with a neural network model, anatomy maps, and dose distributions. An image processing device then trains the neural network model to forecast fluence and dose maps based on the medical images and anatomy maps. These maps illustrate the radiation particles per second and the radiation dose to be administered to a patient. Subsequently, a new three-dimensional dose distribution is generated using the predicted maps.
Furthermore, the patent details a method for predicting radiation therapy doses involving receiving three-dimensional medical images, storing them along with a neural network model, anatomy maps, and dose distributions in a computer-readable medium, and training the neural network model to predict fluence and dose maps based on the input data. The method includes initializing the neural network model, inputting training data, comparing predicted and expected dose distributions, and adjusting the model until reaching a predetermined threshold. Additionally, the method involves testing the trained model with new patient data to determine an error factor. The neural network model utilized in this method is a deep convolutional neural network (DCNN), showcasing advanced technology in the field of radiation therapy treatment prediction.
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