3D Radiotherapy Dose Prediction Using Machine Learning
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Solution Overview
Problem
Current radiation therapy treatment planning systems rely heavily on subjective input from medical professionals, leading to potential unnecessary irradiation of healthy tissues and missed opportunities to deliver higher doses to tumor sites, due to their reliance on expertise and time-consuming processes.
Innovation Solution
A computing system that uses a predictive model, trained with machine learning techniques such as neural networks, to determine the intensity and distribution of radiation fields intersecting with individual volume elements of a patient, generating a three-dimensional radiation dose matrix based on anatomical data, allowing for more accurate and personalized treatment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If treatment planning relies on subjective input from medical professionals, then expertise-based decision making is achieved, but unnecessary irradiation of healthy tissues occurs and time-consuming processes are required
Solution Approach 1:
The patent creates a digital twin or virtual model of the patient's anatomy using CT or MRI scans, which serves as a copy that can be used for treatment planning without repeatedly exposing the patient to imaging radiation. This virtual model allows multiple planning iterations and dose calculations to be performed on the copy rather than requiring repeated subjective assessments
Solution Approach 2:
The patent replaces the manual, subjective mechanical process of treatment planning with an automated computer-based system that uses algorithms to calculate optimal radiation dose distributions. This substitution eliminates the time-consuming nature of manual planning while improving precision through consistent algorithmic application
2Object-affected harmful factors
If shaped radiation beams are used to conform to tumor cross-section, then healthy tissue intersection is minimized, but device complexity increases
Solution Approach 1:
The patent divides the radiation beam into multiple discrete segments or beams, each shaped independently to target specific portions of the tumor from different angles. This segmentation allows the complex treatment to be broken down into simpler, individually optimizable beam components that collectively achieve the desired conformal dose distribution
Solution Approach 2:
The patent applies different dose intensities and beam shapes to different local regions of the tumor, with each beam tailored to the specific geometry of the tumor at that location. This local optimization ensures that each portion of the tumor receives the appropriate dose while minimizing exposure to surrounding healthy tissues with different sensitivities
3Quantity of substance
If multiple radiation beams from different angles are used, then tumor dose is increased while healthy tissue exposure is reduced, but device complexity and treatment time increase
Solution Approach 1:
The patent combines multiple radiation beams from different angles into a unified treatment plan that delivers cumulative dose to the tumor while distributing healthy tissue exposure across multiple lower-dose pathways. The merging of these beams achieves the therapeutic goal of high tumor dose with reduced healthy tissue damage through coordinated multi-angle delivery
Data Source
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AI summary
Generating a three-dimensional radiation dose matrix for a patient for controlling the delivery of radiation dose to patients. The three-dimensional radiation dose matrix for the patient based on an intensity of radiation fields delivered by a radiation therapy delivery system that intersect with volume elements of a patient and determined by a predictive model. The intensity of the radiation fields at volume elements of the patient determined from spatial position data of the volume elements in a patient and radiation therapy delivery system data.