Unified Multimodal Radiotherapy Optimization Framework
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Solution Overview
Problem
Current radiation therapy planning systems are limited by the need for separate optimization algorithms for each type of radiation modality, such as photon and ion therapies, which results in sub-optimal solutions when combining different treatment types in a single prescription.
Innovation Solution
A system and method that concurrently optimize dose delivery from both photon and ion therapy devices using a single optimization routine, iteratively adjusting parameters to generate simulation models that meet treatment objectives, allowing for combined photon and ion treatment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate optimization algorithms are used for each radiation modality (photon and ion), then each modality can be optimized individually, but the overall treatment plan becomes sub-optimal when combining multiple modalities
Solution Approach 1:
The patent merges separate optimization algorithms for photon and ion therapies into a single unified optimization framework. This allows simultaneous optimization of multiple radiation modalities, enabling the system to find globally optimal treatment plans that consider interactions between different modalities rather than sub-optimal separate solutions.
Solution Approach 2:
The optimization system is designed to be universal and modality-agnostic, capable of handling any type of radiation therapy (photons, electrons, protons, ions, etc.) through a single algorithm. This multi-functional approach allows the same optimization framework to adapt to different treatment modalities and their unique characteristics without requiring separate specialized algorithms.
2Reliability
If a single combined optimization technique is implemented for multimodal radiation therapy, then overall treatment optimization improves, but system complexity increases
Solution Approach 1:
The unified optimization system is segmented into modular components, each handling specific aspects of the optimization process for different modalities. This segmentation allows the complex multi-modality optimization to be broken down into manageable parts that can be independently developed, tested, and maintained while working together as an integrated system.
Solution Approach 2:
The system manages complexity by dynamically adjusting optimization parameters based on the specific treatment modality and clinical scenario. Rather than using a fixed complex algorithm for all cases, the system adapts parameters such as objective functions, constraints, and optimization weights to match the particular treatment plan requirements, simplifying the optimization process for each specific case.
Data Source
AI summary
When performing multimodal radiotherapy planning, an optimizer (36) concurrently optimizes a combined treatment plan that employs an intensity modulated radiotherapy (IMRT) device (30) and an intensity modulated proton therapy (IMPT) that respectively generate a photon beam and an ion beam for treating a volume of interest (18) in a patient (34). A simulator (40) iteratively generates multiple variations of a simulation model (44) according to optimization parameters that are varied by the optimizer (36) until the simulation model (44) satisfies user-entered treatment objective criteria (48) (e.g., maximum dose, does placement, etc.)