Elekta has filed a patent for a method in adaptive radiotherapy treatment planning. The method involves receiving measurement data captured at different time points, mapping it to a treatment geometry representation, using a dynamical model to estimate positions over time, and determining treatment actions based on the model’s estimation. 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.
Adaptive radiotherapy treatment planning method for patients
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A patent application (Publication Number: US20240001148A1) describes a method for adaptive radiotherapy treatment planning for patients. The method involves receiving a sequence of measurement data captured at different time points, including historical data and current treatment data in the form of two-dimensional images. This data is mapped to a representation of the treatment geometry, which is then used in a dynamical model to estimate the positions of the treatment geometry over time. Based on this estimation, the method determines if a change in position meets certain criteria and decides on a treatment action for the patient at a future time point.
Furthermore, the method includes executing the treatment action at the defined future time point, using a control policy to minimize average cost over future time points. The treatment action involves calculating the dose to be delivered to the patient based on the estimation from the dynamical model and adapting the treatment plan accordingly. The process also involves using machine learning models, such as neural networks, in various stages of the method, including mapping measurement data, estimating positions over time, and determining treatment actions. Overall, the method aims to improve adaptive radiotherapy treatment planning by utilizing dynamic models, machine learning, and cost optimization strategies to deliver personalized and effective treatment plans for patients.
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