Computer-Assisted Tumor Ablation Planning with Weighted Cost Functions
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Solution Overview
Problem
Current ablation treatment methods for large tumors face challenges in achieving complete coverage due to imprecise planning and placement of ablations, leading to incomplete tumor destruction and excessive healthy tissue damage, particularly in computer-assisted interventions.
Innovation Solution
A computer-assisted ablation planning system that distinguishes between core-tumor and margin zones in diagnostic imaging, applying weightings in a cost function to prioritize ablation coverage and optimize ablation placement, with a graphical user interface for user preference and visualization of treatment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual mental planning methods are used for ablation placement, then the procedure is simple to perform, but the precision of ablation placement is poor leading to incomplete tumor coverage
Solution Approach 1:
The patent replaces manual mental planning methods with an automated computer-based treatment planning system that uses cost function optimization. The system automatically calculates optimal ablation placements by processing imaging data and applying weighted cost functions, substituting the mechanical/cognitive process of manual planning with an automated computational system that delivers superior precision without requiring complex manual calculations.
2Reliability
If the number of ablations is increased to ensure complete tumor coverage, then the tumor coverage is improved, but the damage to healthy tissue increases
Solution Approach 1:
The patent applies different weightings to different tissue types in the cost function, assigning higher weights to tumor regions and lower weights to healthy tissue regions. This allows the optimization algorithm to prioritize tumor coverage while automatically minimizing damage to healthy tissue, creating a locally optimized solution that addresses different tissue types with different priorities rather than treating all tissue equally.
Solution Approach 2:
The system changes the parameters of the cost function by applying different weightings to various tissue types and ablation outcomes. By adjusting these weight parameters, the system can optimize the balance between complete tumor coverage and minimization of healthy tissue damage, allowing flexible parameter tuning based on specific patient needs and tumor characteristics.
3Manufacturing precision
If computer-assisted intervention is used to optimize ablation placement, then the precision of placement is improved, but the system complexity increases
Solution Approach 1:
The patent replaces complex manual planning processes with an automated computer-based system that uses cost function optimization algorithms. The system automatically processes imaging data, delineates tumor and healthy tissue regions, and calculates optimal ablation placements, substituting manual mechanical/cognitive processes with automated computational processes that deliver superior precision while managing system complexity through algorithmic automation.
4Reliability
If margin zones are explicitly considered in planning, then the completeness of treatment is improved, but the amount of healthy tissue affected increases
Solution Approach 1:
The patent applies different weightings to different tissue types in the cost function, assigning higher weights to tumor regions and lower weights to healthy tissue regions. This allows the optimization algorithm to prioritize tumor coverage while automatically minimizing damage to healthy tissue, creating a locally optimized solution that addresses different tissue types with different priorities rather than treating all tissue equally.
Data Source
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AI summary
A system for ablation planning and treatment includes a delineation module (124) configured to distinguish tissue types in an image, the tissue types including at least a core tissue and a margin zone encapsulating the core tissue. A treatment planning module (140) is configured to apply weightings in a cost function to prioritize ablation coverage of the tissue types including the core tissue and the margin zone to determine ablation characteristics that achieve an ablation composite in accordance with user preferences. A graphical user interface (122) is rendered on a display to indicate the core tissue, the margin zone, the ablation composite and permit user selection of one or more treatment methods.