Machine-Learned Fluence Maps for Radiation Therapy Planning
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Solution Overview
Problem
Existing radiation therapy planning methods are costly and time-consuming, particularly in inverse optimization processes, and struggle to translate two-dimensional image analysis to three-dimensional domains, complicating the delivery of radiation to tumors while minimizing exposure to healthy tissues.
Innovation Solution
A system utilizing machine learning models to generate fluence maps and leaf sequences directly, based on beam eye view projections and digitally reconstructed radiographs, reducing the need for iterative inverse planning by incorporating mechanical constraints of the linear accelerator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional inverse optimization techniques are used for treatment planning, then manufacturing precision (dose delivery accuracy) is improved, but productivity (planning speed) deteriorates
Solution Approach 1:
The patent replaces the conventional iterative inverse optimization computational system with a machine learning-based prediction system. The ML model is trained on historical treatment plans and directly predicts optimal fluence maps, eliminating the need for time-consuming iterative optimization calculations while maintaining dose delivery accuracy.
Solution Approach 2:
The system performs preliminary action by pre-training the machine learning model on extensive historical treatment data before actual treatment planning. This pre-computed knowledge is then rapidly applied to new cases, avoiding the need for slow iterative optimization during the actual planning process.
2Ease of operation
If knowledge-based planning techniques are used, then ease of operation is improved, but adaptability to three-dimensional domains deteriorates
Solution Approach 1:
The patent transitions from two-dimensional image analysis to three-dimensional domain processing by using volumetric data from CT scans and generating three-dimensional fluence maps. The machine learning model processes 3D anatomical structures and delivers 3D dose distributions, maintaining simplicity while achieving full three-dimensional capability.
3Manufacturing precision
If iterative inverse planning is performed, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The patent substitutes the iterative inverse planning computational process with a direct machine learning prediction approach. The trained model instantly generates treatment plans with high accuracy, eliminating the repetitive iterative calculations that consume significant time while maintaining or improving plan quality.
Data Source
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AI summary
Provided herein are systems for planning radiotherapy treatments. Systems can include one or more processors to receive radiotherapy data associated with a set of treatments administered to a set of patients; generate a beam eye view (BEV) projection for each patient of the set of patients; and for each patient of the set of patients, provide treatment data associated with the patient and data associated with the BEV projections corresponding to the patient to a model to train the model to generate an output. The output can represent a fluence map. Systems and method for generating leaf sequences are also provided.