Ultrasonic Imaging Motion Compensation for Perfusion Assessment
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Solution Overview
Problem
Ultrasonic diagnostic imaging systems face challenges in accurately assessing the progress of tumor treatment due to respiratory motion, which causes lesions to move in and out of the image plane, leading to variations in brightness and contrast agent wash-in/wash-out measurements that are not solely due to perfusion changes.
Innovation Solution
The system processes medical images by identifying and selectively removing motion effects caused by physiological functions like respiration, allowing only images acquired during specific phases of the motion cycle to be used for further processing, thereby reducing disparate motional effects and enhancing the precision of perfusion assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If images are acquired continuously during the wash-in and wash-out period to assess perfusion, then measurement precision of tumor angiogenesis is improved, but respiratory motion causes lesions to move in and out of the image plane resulting in motional artifacts that degrade measurement precision
Solution Approach 1:
The system performs preliminary actions by acquiring reference images at multiple respiratory phases before the actual perfusion assessment. These reference images are used to create a motion model that predicts and compensates for respiratory motion during the wash-in and wash-out imaging, thereby eliminating motional artifacts before they degrade the measurement precision
Solution Approach 2:
The system implements feedback by continuously monitoring the position of anatomical landmarks across the image sequence, detecting respiratory-induced motion, and automatically adjusting the image analysis to compensate for detected motion. This closed-loop feedback mechanism ensures that perfusion measurements remain precise despite ongoing respiratory motion
2Measurement precision
If manual inspection and deletion of images is performed to eliminate motional effects, then measurement precision is improved, but the process requires intensive user analysis and reduces productivity
Solution Approach 1:
The system performs self-service by automatically detecting respiratory motion patterns from the image sequence itself, without requiring manual intervention. The algorithm autonomously identifies motional artifacts, selects appropriate images for analysis, and compensates for motion effects, thereby eliminating the need for intensive user analysis while maintaining measurement precision
Solution Approach 2:
The system replaces the manual mechanical process of image inspection and deletion with an automated computational algorithm. The algorithm processes the entire image sequence rapidly using image analysis and pattern recognition, substituting human operator effort with automated image processing that achieves both high precision and high productivity
3Measurement precision
If the imaging probe attempts to track the moving lesion during respiration, then the lesion remains in the image plane improving measurement precision, but the complexity of the imaging system increases
Solution Approach 1:
The system uses an intermediary approach by introducing a computational motion compensation layer between the fixed imaging probe and the moving lesion. Rather than mechanically tracking the lesion, the system acquires images at multiple respiratory phases and uses image processing algorithms to virtually align and combine images, thereby maintaining lesion consistency without requiring complex mechanical tracking hardware
Data Source
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AI summary
Ultrasonic imaging methods are described for eliminating the effects of physiological motion such as respiratory motion from image data of a sequence of images which is to be used for quantified analysis such as time-intensity curve computation. A bright reflector in the images of the loop which exhibits motional effects such as cyclical variation in brightness or position from image to image is identified and its variation used to identify cycles of motion. A phase or range of phases of the cycles is identified. Images acquired during the identified phase or phase range of the motion cycles during the loop are then used for quantified processing.