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Motion-compensated frame rate up-conversion in carotid ultrasound images using optical flow and manifold learning
Fereshteh Yousefi Rizi1, Sima Navabian1, Zahra Alizadeh Sani2
1Department of Biomedical Engineering, Islamic Azad University of South Tehran Branch, Tehran, Iran.
Insights
This study introduces a novel hybrid method combining manifold learning and optical flow to enhance carotid ultrasound imaging. The new technique improves the accuracy of carotid wall motion assessment by increasing the frame rate.
Area of Science:
- Medical Imaging
- Biomedical Engineering
- Cardiovascular Ultrasound
Background:
- Carotid ultrasonography is crucial for assessing atherosclerosis.
- Low frame rates in B-mode cineloops hinder accurate carotid artery wall motion tracking.
- Existing manifold learning methods for frame rate up-conversion have limitations.
Purpose of the Study:
- To develop a hybrid frame rate up-conversion (FRUC) method for carotid ultrasound.
- To improve the assessment of carotid artery wall motion by accounting for rapid movements.
- To overcome limitations of previous manifold learning techniques.
Main Methods:
- A hybrid approach combining manifold learning and optical flow was proposed.
- Locally linear embedding (LLE) identified relationships between cardiac cycle frames.
- Optical flow estimation reconstructed motion-compensated frames.
Main Results:
- The hybrid method successfully increased the frame rate of carotid ultrasound images.
- Reconstructed frames provided more accurate carotid wall motion analysis.
- The new method demonstrated superior performance compared to manifold learning alone.
Conclusions:
- The proposed hybrid FRUC method enhances carotid ultrasound image quality.
- Accurate carotid wall motion assessment is improved, aiding in atherosclerosis evaluation.
- This technique offers a more effective solution for analyzing dynamic carotid artery movements.
Objective:
Carotid ultrasonography is a reliable and non-invasive method to evaluate atherosclerosis disease and its complications. B-mode cineloops are widely used to assess the severity of atherosclerosis and its progression; ho- wever, tracking rapid wall motions of the carotid artery is still a challenging issue due the low frame rate. The aim of this paper was to present a new hybrid frame rate up-conversion (FRUC) method that accounts for motion based on manifold learning and optical flow.
Methods:
In the last decade, manifold learning technique has been used to pseudo-increase the frame rate of carotid ultrasound images, but due to the dependence of this method to the number of recorded cardiac cycles and frames, a new hybrid method based on manifold learning and optical flow was proposed in this paper.
Results:
Locally linear embedding (LLE) algorithm was first applied to find the relation between the frames of consecutive cardiac cycles in a low dimensional manifold. Then by applying the optical flow motion estimation algorithm, a motion compensated frame was reconstructed.
Conclusion:
Consequently, a cycle with more frames was created to provide a more accurate consideration of carotid wall motion compared to the typical B-mode ultrasound ima-ges. The results revealed that our new hybrid method outperforms the pseudo-increasing frame rate scheme based on manifold learning.
