Radiopaque Object Localization Using Multi-Filter Image Fusion
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Solution Overview
Problem
Existing energy-based treatments, such as radiation therapy, struggle to discriminate between target volumes and adjacent tissues, organs, or structures, leading to potential collateral damage due to inaccurate localization of radiopaque objects like fiducials.
Innovation Solution
A method and apparatus using a control circuit to process patient field of view information with multiple filters, followed by multi-cue probabilistic fusion to accurately locate radiopaque objects, enhancing the robustness of target detection and treatment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If radiation therapy is applied to treat target volumes, then therapeutic effect is improved, but collateral damage to adjacent tissues and organs increases due to inaccurate localization of radiopaque objects
Solution Approach 1:
The patent replaces traditional mechanical/image-based fiducial localization methods with a deep learning-based detection system. The neural network automatically identifies and localizes radiopaque objects (fiducials) in imaging data, substituting manual or simple algorithmic approaches with an intelligent system that provides more accurate localization, thereby reducing collateral damage while maintaining therapeutic effect.
Solution Approach 2:
The patent introduces an intermediate processing layer between image acquisition and treatment planning. The deep learning model acts as an intermediary that processes raw imaging data, extracts fiducial locations, and provides enhanced localization information to the treatment planning system, thereby improving accuracy and reducing harmful effects.
2Productivity
If traditional image processing methods are used to locate fiducials, then detection speed is maintained, but measurement precision of fiducial location deteriorates
Solution Approach 1:
The patent substitutes traditional image processing algorithms with a deep learning-based detection system. The neural network automatically identifies and localizes radiopaque objects (fiducials) in imaging data, substituting manual or simple algorithmic approaches with an intelligent system that provides more accurate localization, thereby reducing collateral damage while maintaining therapeutic effect.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the accuracy of energy-based treatment plans by precisely locating radiopaque objects, such as fiducials, thereby reducing collateral damage and enhancing treatment efficacy.
Implementation Method 1
processing the information regarding the at least one field of view using a plurality of different filters to yield a plurality of filtered fields of view that are each different from one another, each filtered field of view comprising a differently filtered version of the at least one field of view; fusing the plurality of filtered fields of view, using multi-cue probabilistic fusion, to yield fused information regarding the location of the at least one radiopaque object
Data Source
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AI summary
A control circuit accesses information regarding at least one field of view of a particular patient, which field of view contains information regarding at least one radiopaque object. The control circuit then processes that information using a plurality of different filters to yield a plurality of filtered fields of view that are each different from one another. Those filtered fields of view are then fused to yield fused information regarding the aforementioned radiopaque object (or objects).