Radiation therapy planning using deep convolutional network

Abstract

A deep convolutional neural network can be trained to provide a patient radiation treatment plan. Training can include collecting patient data based on at least one image of patient anatomy from patients, determining a treatment plan including a set of control points from the collected patient data, and using the determined treatment plans and the corresponding collected patient data to train a deep convolutional neural network for regression to determine a treatment plan including a set of control points from collected patient data. The trained model can be used to provide a radiation treatment plan, such as in real-time.

Description

[0001] priority claim [0002] This application claims the benefit of priority to US Application Serial No. 15,658,484, filed July 26, 2017, the entire contents of which are incorporated herein by reference. technical field [0003] Embodiments of the invention generally relate to determining machine parameters in radiation therapy treatment systems. In particular, the present invention relates to the use of deep learning techniques to determine machine parameters for treatment planning in radiotherapy systems. Background technique [0004] Radiation therapy or "radiation therapy" can be used to treat cancer or other diseases in mammalian (eg, humans and animals) tissues. One such radiation therapy technique is the Gamma Knife, by which a patient is irradiated with large quantities of low-intensity gamma rays that are focused at a target (eg, a tumor) with high intensity and precision. In another embodiment, radiation therapy is delivered using a linear accelerator whereby...

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Problems solved by technology

The order in which plan constraints can be adjusted can lead to differences in dosage

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