Training Corpus Generation Using Synthetic CBCT-CT Image Pairs
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Solution Overview
Problem
Existing radiation treatment plans lack the ability to inherently discriminate between target volumes and adjacent tissues, often requiring careful administration to minimize collateral effects, and prior machine learning approaches have not satisfactorily optimized these plans.
Innovation Solution
A machine learning training corpus is generated using paired CT and synthetic CBCT information, simulating realistic motion artifacts without altering patient anatomy, to train models for automatic segmentation and image enhancement, thereby improving treatment plan optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If radiation energy is applied to treat target volumes, then therapeutic effect on tumors is improved, but collateral damage to adjacent tissues and organs increases
Solution Approach 1:
The radiation treatment plan applies different dosing levels to different spatial locations within the treatment volume. The optimization process calculates and assigns specific dose values to each voxel or region, enabling high-dose delivery to the target volume while maintaining lower doses in adjacent healthy tissues. This spatial variation in dosing quality directly addresses the contradiction by making the radiation effect locally adapted to the anatomical structure.
Solution Approach 2:
The treatment plan segments the radiation field into multiple sequential fields or beams, each targeting the tumor from different angles. By dividing the overall treatment into multiple segmented approaches, the system can concentrate dose on the target while distributing and minimizing exposure to any single adjacent tissue structure, thereby reducing cumulative collateral damage.
2Reliability
If optimization process automatically adjusts treatment parameters, then treatment plan quality is improved, but computational time and complexity increase
Solution Approach 1:
The system performs preliminary calculations and pre-computations during the optimization phase, storing intermediate results and dose distribution patterns. When generating or modifying treatment plans, it leverages these pre-computed data to accelerate the optimization process, reducing the time required for subsequent adjustments while maintaining plan quality.
Solution Approach 2:
The patent replaces manual, iterative optimization adjustments with an automated computational system that uses mathematical algorithms to calculate optimal treatment parameters. This substitution of manual mechanical adjustment with automated computational optimization improves both speed and consistency of treatment plan generation.
3Productivity
If machine learning model is used for treatment plan optimization, then automation and efficiency are improved, but model training data requirements and system complexity increase
Solution Approach 1:
The machine learning model learns from copied historical treatment plans and their outcomes, creating a knowledge base from existing successful treatments. By training on replicated patterns from previously optimized plans, the system develops the ability to generate new treatment plans automatically without requiring complex manual programming for each case, thus improving automation while managing complexity.
Data Source
AI summary
A control circuit accesses a plurality of computed tomography (CT) information items and generates a plurality of synthetic cone-beam computed tomography (CBCT) information items as a function thereof. These teachings can then provide for generating a machine learning training corpus as a function of paired data comprising pairs of the synthetic CBCT information items with other information items. Those other information items may comprise, for example, one or more of the aforementioned CT information items and/or structure information.