X-ray Diagnosis Apparatus Automatic Roadmap Image Selection
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Solution Overview
Problem
Conventional X-ray diagnosis systems require manual selection of images when blood vessels are most filled with contrast material, which is inefficient and prone to errors due to variations in X-ray output stability, detector after-images, and body motion, leading to suboptimal image selection.
Innovation Solution
An X-ray diagnosis apparatus that calculates the average, median, and mode of pixel values for multiple X-ray images chronologically, automatically selecting the image with the largest difference or ratio between these values to ensure optimal contrast and accurate representation of blood vessels filled with contrast material.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of images is performed by an operator, then flexibility in choosing the optimal image is improved, but time consumption and error probability increase
Solution Approach 1:
The system automatically selects the optimal angiogram image by computing pixel value statistics (average, median, mode) and their differences for each image frame, eliminating the need for manual operator selection while maintaining high accuracy in identifying when blood vessels are most filled with contrast material
Solution Approach 2:
The manual visual inspection and selection process is replaced by an automated computational system that calculates statistical parameters of pixel values and uses algorithms to objectively determine the optimal image frame, substituting human judgment with mathematical computation
2Reliability
If manual selection is used, then operator judgment can account for variations in X-ray output and body motion, but the process becomes inefficient and prone to errors
Solution Approach 1:
The system autonomously handles the complete image selection process by continuously analyzing pixel value statistics across multiple frames, automatically compensating for X-ray output variations and body motion without requiring operator intervention, thereby improving both reliability and efficiency
Solution Approach 2:
The system computes statistical parameters (average, median, mode) and their differences for each image frame in real-time, using this feedback to objectively identify the frame with maximum contrast material filling, ensuring consistent and reliable selection across varying conditions
3Measurement precision
If the operator manually checks images on a monitor, then visual assessment of blood vessel filling can be performed, but the process is time-consuming and subject to human error
Solution Approach 1:
Visual inspection by the operator is replaced by automated computational analysis of pixel value statistics, where the system calculates average, median, and mode values for each frame and identifies the optimal image through mathematical comparison, eliminating human error and accelerating the process
Solution Approach 2:
The system automatically performs the detection and selection function that previously required manual visual assessment, continuously monitoring pixel value changes and autonomously determining when blood vessels are optimally filled with contrast material
Data Source
AI summary
An X-ray diagnosis apparatus includes an X-ray tube, an X-ray detector, and a processing circuitry. The X-ray tube generates X-rays. The X-ray detector detects the X-rays that have passed through a subject. The processing circuitry calculates, for each of a plurality of X-ray images that are acquired chronologically, an average of pixel values and a reference value based on the pixel values. The processing circuitry extracts, from the plurality of X-ray images, an X-ray image with a relatively large difference between the average and the reference value, an X-ray image with a relatively large ratio between the average and the reference value, or an X-ray image with a relatively large number of pixels each representing a value equal to or larger than the average or the reference value.