Long Length Imaging Stitching Deviation Detection
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Solution Overview
Problem
In X-ray radiography, especially for elongate bodies like the spine or legs, patient movement and system miscalibration lead to inaccuracies in long length image stitching, causing measurement errors in Computed Radiography (CR) and Digital Radiography (DR) systems, as existing methods fail to effectively prevent or correct for these issues during the composition of partial images.
Innovation Solution
A computer-implemented method that applies different stitching techniques to detect deviations and generates warnings for patient movement and system inaccuracies by comparing stitching parameters derived from the x-ray source and detector setup, object geometry, and user interaction, ensuring accurate alignment and measurement correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple partial images are stitched to create long length images, then the ability to image elongate bodies is improved, but measurement precision deteriorates due to patient movement and system miscalibration
Solution Approach 1:
The system applies multiple different stitching methods to the same set of partial images and compares their results. When deviations between stitching methods exceed a threshold, a warning is generated. This feedback mechanism allows the system to self-validate stitching accuracy and detect patient movement or system errors without requiring additional hardware or complex calibration procedures.
Solution Approach 2:
The invention creates multiple copies of the stitching process using different algorithms (theoretical stitching parameters, object geometry reconstruction, user interaction). By comparing these independent copies of the stitching operation, the system can identify deviations caused by patient movement or system errors, thereby maintaining measurement precision across the extended imaging area.
2Area of stationary object
If FPD positioning is adjusted to improve imaging coverage, then the ability to capture elongate bodies is improved, but manufacturing precision deteriorates due to positioning inaccuracies
Solution Approach 1:
The system uses feedback from multiple stitching methods to detect positioning errors. By comparing results from theoretical parameters, object geometry reconstruction, and user interaction, the system can identify when FPD positioning inaccuracies have affected the stitching quality and generate appropriate warnings.
3Stability of the object's composition
If patient movement compensation is applied to maintain image quality, then image completeness is improved, but measurement precision deteriorates due to inaccurate compensation
Solution Approach 1:
The system applies feedback by comparing multiple stitching results to detect when patient movement has occurred. Rather than blindly applying compensation, the system first detects deviations between stitching methods and generates warnings, allowing operators to assess whether compensation is appropriate and maintain measurement precision.
4Measurement precision
If multiple stitching methods are compared to detect deviations, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system creates multiple independent copies of the stitching process using different algorithms (theoretical parameters, object geometry, user interaction). These parallel copying operations allow comparison for accuracy validation without requiring complex integrated systems, as each stitching method operates independently and can be implemented using existing software capabilities.
Data Source
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AI summary
Method of generating radiation images of an elongate body by applying different computer-implemented stitching methods to the same set of partial radiation images. A warning is generated in case of substantial deviation between the applied stitching methods.