Focal Therapy Planning with Constraint-Guided Device Positioning
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Solution Overview
Problem
Existing focal therapy treatments face challenges in efficiently and accurately determining optimized positions and parameters for treatment devices within a patient's body to achieve desired treatment goals while minimizing impact on healthy tissue.
Innovation Solution
A system and method for planning focal therapy treatments that utilize a constraint function generation unit, selection unit, and optimization unit to determine optimized device positions and parameters through a heuristic approach, allowing for efficient and accurate selection of device positions and insertion points, particularly using a template grid for devices like thermal ablation probes and brachytherapy catheters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If optimization procedures are used to determine device positions and parameters for focal therapy treatments, then treatment plan quality is improved, but computational complexity and time consumption increase
Solution Approach 1:
The patent segments the treatment planning process into distinct functional units: a constraint function generation unit that creates objective functions from treatment goals, a selection unit that identifies candidate device positions, and an optimization unit that determines optimal parameters. This segmentation allows each unit to be optimized independently and processed efficiently in sequence, reducing overall computational complexity while maintaining treatment plan quality.
Solution Approach 2:
The patent performs preliminary actions by generating constraint functions from treatment goals before the optimization process begins, and by pre-identifying candidate device positions using the selection unit. This preliminary processing organizes the problem space in advance, allowing the optimization unit to focus computational resources on determining optimal parameters rather than exploring the entire parameter space, thus reducing time consumption while maintaining reliability.
2Measurement precision
If comprehensive optimization is performed for all device positions and parameters, then treatment accuracy is improved, but treatment planning time increases
Solution Approach 1:
The patent applies local quality by focusing optimization efforts on specific candidate device positions identified by the selection unit rather than uniformly optimizing all possible positions. The constraint function is evaluated locally at each candidate position, and optimization is performed only for the most promising candidates, thereby achieving high treatment accuracy for the selected positions while significantly reducing overall planning time.
Solution Approach 2:
The patent implements partial action by performing comprehensive optimization only for a selected subset of candidate device positions rather than all possible positions. The selection unit filters candidate positions based on preliminary criteria, and the optimization unit then performs detailed optimization only on these filtered candidates, achieving sufficient treatment accuracy while reducing planning time through selective rather than exhaustive optimization.
3Reliability
If multiple candidate device positions are evaluated, then treatment effectiveness is improved, but computational resources required increase
Solution Approach 1:
The patent changes parameters by transforming treatment goals into constraint functions with specific mathematical formulations that enable efficient evaluation. The constraint function parameters are adjusted and optimized based on the candidate device positions, allowing the system to evaluate multiple positions effectively while managing computational resources through parameter-based optimization rather than brute-force evaluation.
Solution Approach 2:
The patent uses copying by creating constraint function representations of treatment goals that can be efficiently evaluated across multiple candidate device positions. Instead of performing full simulations for each position, the constraint function serves as a computational model or copy that approximates treatment outcomes, enabling rapid evaluation of multiple positions with reduced computational resource requirements while maintaining treatment effectiveness.
Data Source
AI summary
The invention relates to a system for assisting in planning a focal therapy treatment of a structure (1) within a patient body (2) by applying a treatment quantity using one or more devices (4a,b,c) operated in one or more device positions. The system comprises a unit (10) configured to generate a constraint function representing clinical objectives relating to the treatment quantity, a selection unit (11) configured to determine, for each of at least some non-selected candidate device positions, a sum of negative derivatives of the constraint function with respect to the treatment parameter associated with the respective device position and to select a device position for use in the treatment based on a comparison of the determined sums, and an optimization unit (12; 408) configured to determine at least one optimized treatment parameter for the selected device position based on the constraint function.