Imperial College London
Alternate

Professor MENGXING TANG

Faculty of Engineering, Department of Bioengineering

Professor of Biomedical Imaging
 
 
 
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Contact

 

+44 (0)20 7594 3664mengxing.tang Website

 
 
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Location

 

3.13Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Yan:2022:10.1109/TMI.2022.3152396,
author = {Yan, J and Zhang, T and Broughton-Venner, J and Huang, P and Tang, M},
doi = {10.1109/TMI.2022.3152396},
journal = {IEEE Transactions on Medical Imaging},
pages = {1938--1947},
title = {Super-resolution ultrasound through sparsity-based deconvolution and multi-feature tracking},
url = {http://dx.doi.org/10.1109/TMI.2022.3152396},
volume = {41},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Yan,J
AU  - Zhang,T
AU  - Broughton-Venner,J
AU  - Huang,P
AU  - Tang,M
DO  - 10.1109/TMI.2022.3152396
EP  - 1947
PY  - 2022///
SN  - 0278-0062
SP  - 1938
TI  - Super-resolution ultrasound through sparsity-based deconvolution and multi-feature tracking
T2  - IEEE Transactions on Medical Imaging
UR  - http://dx.doi.org/10.1109/TMI.2022.3152396
UR  - https://ieeexplore.ieee.org/document/9715135
UR  - http://hdl.handle.net/10044/1/94538
VL  - 41
ER  -
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