Neural Network X-ray Image Scoring
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Solution Overview
Problem
Current methods for determining the optimal perspective of X-ray projection images in medical imaging are inefficient, often requiring a trial-and-error approach that increases X-ray dose and results in marginally improved images, and manual analysis is time-consuming, especially for archiving purposes.
Innovation Solution
A computer-implemented method using a neural network to generate predicted image perspective score values for X-ray projection images, trained to assess the quality of perspectives and determine which images to archive, combining subjective and objective scoring for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a trial-and-error approach is used to find the optimal perspective, then the image quality may be marginally improved, but the X-ray dose increases and the process becomes time-consuming
Solution Approach 1:
The system performs preliminary analysis of multiple X-ray projection images from different perspectives using a neural network before final archiving. The neural network automatically scores and evaluates image perspectives, allowing the radiographer to identify the optimal perspective in advance without requiring multiple trial exposures, thereby reducing total X-ray dose while maintaining image quality.
Solution Approach 2:
The system creates a virtual copy of the X-ray imaging process by using a neural network model that can predict and score image perspectives without requiring actual additional X-ray exposures. The neural network learns from training data to simulate and evaluate different imaging perspectives, allowing virtual assessment of image quality without the need for physical repositioning or repeated exposures.
2Measurement precision
If manual analysis of X-ray images is performed to determine archiving criteria, then image quality assessment is possible, but the process is time-consuming and inefficient
Solution Approach 1:
The system replaces the manual mechanical process of radiographer analysis with an automated neural network system. The neural network automatically scores, evaluates, and determines whether images meet archiving criteria based on learned patterns from training data, eliminating the need for manual review while maintaining or improving assessment accuracy through consistent application of established imaging criteria.
3Loss of information
If multiple X-ray projection images are acquired from different perspectives, then comprehensive information is obtained, but data storage requirements increase due to redundant images
Solution Approach 1:
The system implements feedback through the neural network's scoring mechanism that evaluates each X-ray projection image's perspective quality in real-time. Based on the scores, the system provides feedback to determine which images meet archiving criteria and should be retained, and which do not meet the criteria and can be discarded, thereby reducing storage requirements while ensuring complete information is preserved through selective archiving of only the most informative images.
Data Source
AI summary
A computer-implemented method of determining image perspective score values (s1) for X-ray projection images (110) representing a region of interest (120) in a subject, is provided. The method includes: receiving (SI 10) a plurality of X-ray projection C images (110), the X-ray projection images representing the region of interest (120) from a plurality of different perspectives of an X-ray imaging system (130) respective the region of interest; inputting (S120) the X-ray projection images into a neural network (NN1); and in response to the inputting, generating (S130) a predicted image perspective score value (s1) for each of the X-ray projection images.