Image Processing Apparatus for Periodic Movement Phase Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing radiation imaging systems face challenges in accurately superimposing blood vessel images onto fluoroscopic moving images, especially due to the heart's periodic movement and breathing, which can lead to incorrect phase identification and poor alignment of blood vessel images during catheter insertion procedures.
Innovation Solution
An image processing apparatus that acquires both current and past images of a subject with periodic movement, searches for feature points, calculates vector groups based on centroids, and selects the most similar past image to superimpose on the current fluoroscopic image, accounting for parallel movements caused by breathing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If blood vessel images are superimposed on fluoroscopic moving images, then the visibility of blood vessels is improved, but the alignment accuracy deteriorates due to heart movement and breathing
Solution Approach 1:
The system pre-acquires multiple still images of the subject at different phases of the cardiac cycle before the fluoroscopic examination. These pre-acquired images are stored and later selected based on the real-time phase detection during fluoroscopy, allowing the blood vessel image to be accurately aligned with the current heart position.
Solution Approach 2:
The system dynamically selects different pre-acquired blood vessel images based on the real-time phase of the cardiac cycle detected from the fluoroscopic moving image. Instead of using a single static blood vessel image, the system adapts the selection to match the dynamic movement of the heart, ensuring accurate alignment throughout the cardiac cycle.
2Measurement precision
If multiple past images are acquired to cover periodic movement, then the phase identification accuracy is improved, but the data processing complexity increases
Solution Approach 1:
The system segments the cardiac cycle into multiple discrete phases by acquiring still images at different time points. Each image represents a specific phase of the cardiac cycle, allowing the system to selectively process and compare only relevant phases rather than analyzing continuous data, thereby reducing computational complexity while maintaining accuracy.
Solution Approach 2:
The system creates multiple copies of the subject's anatomical structure at different cardiac phases by acquiring multiple still images. These copies are then compared against the fluoroscopic image to find the best match, avoiding the need for complex real-time phase calculation while achieving accurate phase identification.
3Measurement precision
If feature point association is performed between current and past images, then the alignment precision is improved, but the processing time increases
Solution Approach 1:
The system pre-identifies and marks feature points on multiple past images during the image acquisition phase. This preliminary processing allows the fluoroscopic examination to proceed in real-time without requiring intensive feature point detection during the actual procedure, reducing processing time while maintaining alignment precision.
Solution Approach 2:
The system performs feature point association only on key anatomical landmarks rather than all pixels or all features in the images. By selecting only the most critical feature points for association, the system achieves sufficient alignment precision with significantly reduced computational effort and processing time.
Data Source
AI summary
An image processing apparatus includes a current image acquisition device which acquires a current image of a subject having a structure having a periodic movement, a past image acquisition device which acquires multiple past images of the subject such that the past images captured for over one or more periods of the periodic movement are acquired, and circuitry which searches multiple feature points on each past image and the current image, associates the feature points on the current image and the feature points on each of the past images, calculates, for each of the past images, a degree of similarity between the feature points on each of the past images and the feature points on the current image based on association, and identifies to which one of the past images the current image corresponds such that at which phase of the periodic movement the current image is positioned is estimated.