Medical Image Sequence Alignment via Translational Shift Correction
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Solution Overview
Problem
Current medical imaging systems face challenges in aligning and comparing angiographic images due to patient movement and variations in X-ray power, leading to misinterpretations and the need for manual synchronization of contrast agent flow, which limits accurate assessment of treatment completion in procedures like embolization.
Innovation Solution
A system that automatically processes and aligns medical image sequences in space, time, and luminance by identifying mask images, determining translational shifts, and applying geometric transformations to correct misalignment, while synchronizing luminance intensity to facilitate concurrent comparison of angiographic images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual synchronization of contrast agent flow times is used, then users can review two different multiple frame image sequences independently simultaneously, but the timing alignment between images is imprecise and requires manual intervention
Solution Approach 1:
The system automatically identifies contrast agent entrance images in each sequence and performs temporal synchronization without manual intervention. The processor detects the timing of contrast agent introduction and aligns the sequences autonomously, eliminating the need for manual synchronization while achieving precise timing alignment.
Solution Approach 2:
The manual mechanical synchronization process is replaced with an automated image processing system that uses algorithmic detection of contrast agent entrance and automatic temporal alignment, substituting human operation with computational processing for more precise results.
2Adaptability or versatility
If images are acquired at different times during a procedure, then more comprehensive treatment stages can be captured, but patient movement causes mis-alignment between images
Solution Approach 1:
The system introduces an automated image registration process as an intermediary step between image acquisition and review. This registration process detects and corrects spatial mis-alignments caused by patient movement, enabling accurate comparison of images taken at different treatment stages while maintaining comprehensive stage coverage.
Solution Approach 2:
The system automatically adjusts spatial parameters through image registration transformations, correcting mis-alignments by applying geometric transformations that realign anatomical structures across images acquired at different times, thereby maintaining precision despite temporal separation.
3Adaptability or versatility
If X-ray power values vary between acquisitions, then imaging conditions can be adapted to patient anatomy, but luminance intensity values differ affecting image comparison accuracy
Solution Approach 1:
The system automatically normalizes luminance parameters by detecting variations in X-ray power values and applying corrective transformations to luminance intensity data. This allows the system to maintain adaptability to different patient anatomies while ensuring accurate luminance comparison across images acquired under different exposure conditions.
4Measurement precision
If automatic processing is implemented, then image alignment precision is improved, but processing complexity increases
Solution Approach 1:
The processing system performs automatic image registration, temporal synchronization, and luminance normalization without requiring complex manual configuration. The system self-adjusts by automatically detecting key features (contrast agent entrance, anatomical landmarks) and applying appropriate transformations, reducing the operational complexity despite the sophisticated processing performed.
Data Source
AI summary
A system automatically processes different medical image sequences facilitating comparison of the sequences in adjacent respective display areas. An image data processor, identifies first and second mask images of first and second image sequences respectively as images preceding introduction of contrast agent and determines a translational shift between the first and second mask images. The image data processor transforms data representing individual images of at least one of the first image sequence and the second image sequence in response to the determined translational shift to reduce mis-alignment of the individual images of the first image sequence relative to the individual images of the second image sequence. A display presents first and second image sequences corrected for mis-alignment, in substantially adjacent display areas to facilitate user comparison.