Beam Angle Optimization via Reinforcement Learning
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Solution Overview
Problem
Current methods for optimizing beam angles in radiation therapy are inefficient, error-prone, and heavily reliant on subjective medical professional skills, leading to time-consuming and tedious processes.
Innovation Solution
A computer-implemented method using an end-to-end machine learning model that ingests medical images, clinical goals, and treatment plans to holistically optimize beam angles, reducing reliance on medical professionals and improving response time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional manual methods are used for beam angle optimization, then medical professionals can determine treatment parameters based on their expertise, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces the manual mechanical process of beam angle determination with an automated machine learning system. The neural network model processes medical images and treatment parameters to automatically optimize beam angles, eliminating the time-consuming manual iteration while maintaining or improving accuracy through consistent algorithmic application of optimization criteria
Solution Approach 2:
The system enables self-service automation where the machine learning model independently performs beam angle optimization without requiring continuous human intervention. The model uses reinforcement learning to autonomously explore and determine optimal beam angles based on the treatment plan and anatomical constraints, reducing both time and potential human error
2Ease of operation
If conventional trial-and-error methods are used for determining radiation parameters, then medical professionals can achieve treatment goals, but the process is tedious and relies heavily on subjective skills
Solution Approach 1:
The patent substitutes the subjective manual trial-and-error process with an objective machine learning system. The neural network automatically iterates through possible beam angle configurations and evaluates them against clinical goals, eliminating the tedious manual process while improving efficiency through parallel computation and learned optimization strategies
Solution Approach 2:
The system automatically adjusts multiple radiation parameters including beam angles, intensities, and field geometries simultaneously. The machine learning model learns optimal parameter combinations from training data and applies these transformations to new cases, making the complex multi-parameter optimization process easier and more efficient
3Adaptability or versatility
If manual beam angle optimization is performed, then treatment plans can be customized for individual patients, but the process is time-consuming and cannot be easily scaled
Solution Approach 1:
The patent creates a universal machine learning system that handles diverse treatment scenarios through a single platform. The model processes various tumor types, anatomical configurations, and radiation therapy protocols using the same underlying architecture, enabling both customization for individual patients and scalable throughput across multiple cases simultaneously
Data Source
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AI summary
Embodiments described herein provide for revising radiation therapy treatment plans, and in particular, revising beam angles used during radiation therapy treatment. A computer may receive a radiation therapy treatment plan based on a particular patient's diagnosis. The computer may use a machine learning model (520) to revise radiation therapy treatment parameters (510a) such as a beam angle indicating a direction of radiation into the patient. The machine learning model (520) may use reinforcement learning to optimize an initial beam angle from the radiation therapy treatment plan, revising the beam angle. The performance of the machine learning model (520) is measured against metrics including fulfilling dosimetric clinical goals. The machine learning model (520) may present (210) the revised beam angle for display to a medical professional, or transmit (206) the revised beam angle to downstream applications to further revise the radiation therapy treatment plan.