Respiratory Phase Estimation from Fluoroscopic Images
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Solution Overview
Problem
Existing methods for determining respiratory phase from fluoroscopic images often rely on dedicated sensors or local motion analysis, which may not provide a reliable or rapid indication of respiratory phase, especially when larger anatomical structures like the diaphragm are involved.
Innovation Solution
A method that analyzes a sequence of digitized fluoroscopic images to determine respiratory phase by applying a smoothing filter to the average pixel intensities of selected zones within the images, selecting the best zone based on figures-of-merit, and extrapolating phase estimates to reduce X-ray exposure and noise impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dedicated sensors or local motion analysis are used to determine respiratory phase, then measurement precision may be improved, but device complexity increases
Solution Approach 1:
The fluoroscopic imaging system itself provides the data needed for respiratory phase determination by analyzing pixel intensity variations in the captured images. The system uses its own imaging capability to detect diaphragm motion and extract respiratory phase information, eliminating the need for separate dedicated sensors.
Solution Approach 2:
The patent replaces physical sensors with a computational analysis method that processes image data. Instead of using mechanical or electronic sensors to detect respiratory motion, the system substitutes a signal processing approach that analyzes temporal variations in pixel intensities from fluoroscopic images to determine respiratory phase.
2Reliability
If dedicated sensors are used for respiratory phase determination, then reliability of respiratory phase indication is improved, but ease of operation deteriorates
Solution Approach 1:
The fluoroscopic system performs dual functions: capturing images for diagnostic purposes and simultaneously analyzing those same images for respiratory phase determination. This self-service approach eliminates the need for separate sensor placement and setup procedures, making the system easier to operate while maintaining reliability.
3Reliability
If larger anatomical structures like diaphragm are analyzed for respiratory phase, then reliability of respiratory phase indication is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent extracts respiratory phase information by focusing analysis on specific regions of the fluoroscopic images where the diaphragm is located. By isolating and analyzing only the relevant portions of the images (specific zones containing the diaphragm), the system reduces the complexity of detecting and measuring anatomical structure motion while improving reliability.
Solution Approach 2:
The analysis is applied locally to specific zones within the fluoroscopic images rather than processing the entire image. By concentrating computational resources on regions containing the diaphragm and other respiratory-related anatomical structures, the system improves measurement reliability while managing the difficulty of detection and measurement.
4Measurement precision
If more X-ray images are taken to improve respiratory phase estimation, then measurement precision is improved, but object-affected harmful factors increase
Solution Approach 1:
The patent uses partial action by analyzing temporal variations in pixel intensities from a sequence of images rather than requiring excessive X-ray exposure. By extracting respiratory phase information from subtle intensity changes in fluoroscopic images over time, the system achieves adequate measurement precision while minimizing the number of X-ray exposures needed.
Data Source
Figure 1A~1B
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Figure 3A~3B
AI summary
Method of determining respiratory phase of a living body from a sequence of digitized fluoroscopic images includes the steps of: in each living-body-region image in the sequence, defining one or more zones with each image having identical image-to-image zone locations, sizes, and shapes; for each image, computing an average pixel intensity for each zone to form a sequence; modifying the average pixel intensities by (a) computing the mean value of the sequence of average pixel intensities, (b) subtracting the mean from each average pixel intensity, (c) summing the absolute values of the modified average pixel intensities to form a zone-sequence sum A; computing absolute-value first differences for each sequential pair of average pixel intensities and summing the differences to form a zone- sequence first-difference sum B; selecting the zone having the highest ratio A/B; and using the sequence of modified average pixel intensities of the selected zone to determine respiratory phase.