Radiotherapy Planning Optimization via Mass-Based Dose Objectives
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Solution Overview
Problem
Current radiotherapy techniques often result in high doses of radiation to healthy tissues, leading to severe side effects and limiting the delivery of therapeutic doses to cancerous tissues, as they do not accurately account for the spatial distribution of radiation dose within structures, relying on volume-based objectives that lack spatial information.
Innovation Solution
The method involves optimizing radiotherapy treatment plans using mass-based or density-based objectives, which minimize the radiation dose to healthy tissues by directing beams through lower density regions, thereby reducing collateral damage while maintaining effective tumor targeting, employing techniques like Intensity Modulated Radiation Therapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT), and utilizing computational algorithms to determine near-optimal solutions for dose distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If volume-based objectives are used in radiotherapy planning, then the treatment planning process is simpler, but the radiation dose to healthy tissues cannot be accurately minimized due to lack of spatial information
Solution Approach 1:
The patent changes the optimization parameter from volume-based to mass-based or density-based objectives. This allows the planning system to account for spatial variations in tissue density and radiation dose deposition, improving measurement precision without significantly increasing computational complexity.
Solution Approach 2:
The patent introduces a new dimension to the optimization problem by incorporating mass and density parameters alongside traditional volume-based objectives. This additional dimensional information enables more precise spatial dose distribution control while maintaining computational feasibility.
2Reliability
If higher radiation doses are delivered to cancerous tissues, then tumor control probability increases, but healthy tissues receive higher doses causing severe side effects
Solution Approach 1:
The patent applies local quality by using mass-based and density-based optimization to create spatially varying dose distributions. This allows different regions (tumor vs. healthy tissue) to receive appropriately differentiated doses, maximizing tumor control while minimizing healthy tissue damage through localized dose modulation.
Solution Approach 2:
By changing from volume-based to mass-based optimization parameters, the system can more accurately predict and control actual radiation dose deposition in different tissue types, enabling higher tumor doses without proportionally increasing healthy tissue exposure.
3Object-affected harmful factors
If mass-based optimization is implemented, then radiation dose to healthy tissues is reduced, but the treatment planning computation becomes more complex
Solution Approach 1:
The patent performs preliminary calculations of mass and density parameters before the optimization process. By pre-computing these parameters from CT or MRI data, the system reduces the computational burden during the actual optimization phase, making mass-based planning more feasible despite its inherent complexity.
Data Source
AI summary
A method of radiotherapy treatment planning is described. A planned target volume is identified for radiotherapy treatment and at least one volume of interest is identified. The mass, density, and total deposited energy contained in the planned target volume and the volumes of interest are identified and dose objectives are determined. At least one of the objectives is a function of the identified mass, density, or deposited energy. A composite objective function is determined using the dose objectives for the planned target volume and the volumes of interest. A near optimal solution to the composite objective function is determined to produce a radiotherapy treatment plan.