Object Support Removal from CT Imaging Data
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Solution Overview
Problem
Existing methods for removing object supports from imaging data in transmission imaging systems, such as CT, SPECT, and PET, are either manual and time-consuming, or rely on inaccurate assumptions about the object support's shape and structure, limiting their effectiveness across different imaging systems and scenarios.
Innovation Solution
An automatic or semi-automatic method that identifies and removes the object support by analyzing sagittal imaging planes, using techniques like binary thresholding and edge detection to locate the top edge of the object support, allowing for its removal from transverse imaging planes without relying on specific assumptions about its shape or structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to remove object support, then accuracy can be controlled by user expertise, but time consumption increases and reproducibility decreases
Solution Approach 1:
The system performs automatic object support removal without requiring manual user intervention. The algorithm independently identifies and removes the object support by analyzing imaging data, making the process self-service and eliminating the time consumption and variability associated with manual methods.
Solution Approach 2:
The patent replaces manual mechanical interaction with automated computational algorithms. Instead of requiring a user to manually select and remove the object support, the system uses image processing techniques to automatically identify and remove it, substituting human expertise with computational methods.
2Productivity
If model-based automatic methods are used, then processing speed increases, but adaptability to different object support types decreases
Solution Approach 1:
The system employs a universal approach that can handle various types of object supports without requiring separate models for each type. By using general image analysis techniques that detect object supports based on their imaging characteristics rather than predefined models, the system achieves both speed and adaptability across different imaging scenarios.
Solution Approach 2:
The patent utilizes changes in imaging parameters and characteristics to identify and remove object supports. Instead of relying on fixed geometric models, the system analyzes variations in density, shape, and position parameters across different imaging slices to automatically adapt to various object support configurations.
3Ease of operation
If simple manual methods with couch removal plane are used, then ease of operation increases, but accuracy decreases when object support is not flat
Solution Approach 1:
The system replaces the simple manual couch removal plane method with automated image analysis. Instead of relying on a single reference plane that fails for non-flat supports, the system analyzes the actual imaging data to identify the precise boundaries of the object support, providing accurate removal regardless of the support's geometric configuration.
Data Source
AI summary
A method and system for removing an object support from imaging data such as CT imaging data are provided. The automatic or semi-automatic removal process comprises identifying and locating the top edge of the object support in sagittal imaging plane data, and then removing the object support from transverse or volumetric imaging data.