Super-Resolution Ultrasound Through Sparsity-Based Deconvolution and Multi-Feature Tracking

Ultrasound super-resolution imaging through localisation and tracking of microbubbles can achieve sub-wave-diffraction resolution in mapping both micro-vascular structure and flow dynamics in deep tissue in vivo . Currently, it is still challenging to achieve high accuracy in localisation and tracki...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2022-08, Vol.41 (8), p.1938-1947
Main Authors: Yan, Jipeng, Zhang, Tao, Broughton-Venner, Jacob, Huang, Pintong, Tang, Meng-Xing
Format: Article
Language:English
Subjects:
Animals
Bubbles
Computer Simulation
Cost function
Deconvolution
features-based pairing
graph-based tracking
Humans
Image Processing, Computer-Assisted - methods
Image resolution
Imaging
Kalman filter
Kalman filters
Localization
Lymph nodes
Mice
Microbubbles
Microvessels - diagnostic imaging
motion model
Neuroimaging
Sparsity
Superresolution
Tracking
Ultrasonic imaging
Ultrasonography - methods
Ultrasound
ultrasound localization microscopy
Ultrasound super-resolution imaging
Wave diffraction
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Summary:Ultrasound super-resolution imaging through localisation and tracking of microbubbles can achieve sub-wave-diffraction resolution in mapping both micro-vascular structure and flow dynamics in deep tissue in vivo . Currently, it is still challenging to achieve high accuracy in localisation and tracking particularly with limited imaging frame rates and in the presence of high bubble concentrations. This study introduces microbubble image features into a Kalman tracking framework, and makes the framework compatible with sparsity-based deconvolution to address these key challenges. The performance of the method is evaluated on both simulations using individual bubble signals segmented from in vivo data and experiments on a mouse brain and a human lymph node. The simulation results show that the deconvolution not only significantly improves the accuracy of isolating overlapping bubbles, but also preserves some image features of the bubbles. The combination of such features with Kalman motion model can achieve a significant improvement in tracking precision at a low frame rate over that using the distance measure, while the improvement is not significant at the highest frame rate. The in vivo results show that the proposed framework generates SR images that are significantly different from the current methods with visual improvement, and is more robust to high bubble concentrations and low frame rates.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2022.3152396