Name
Abstract | ||
|---|---|---|
By localizing microbubbles (MBs) in the vasculature, ultrasound localization microscopy (ULM) has recently been proposed, which greatly improves the spatial resolution of ultrasound (US) imaging and will be helpful for clinical diagnosis. Nevertheless, several challenges remain in fast ULM imaging. The main problems are that current localization methods used to implement fast ULM imaging, e.g., a previously reported localization method based on sparse recovery (CS-ULM), suffer from long data-processing time and exhaustive parameter tuning (optimization). To address these problems, in this paper, we propose a ULM method based on deep learning, which is achieved by using a modified sub-pixel convolutional neural network (CNN), termed as mSPCN-ULM. Simulations and
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</italic>
experiments are performed to evaluate the performance of mSPCN-ULM. Simulation results show that even if under high-density condition (6.4 MBs/mm
<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>
), a high localization precision (
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in the lateral direction and
<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 24\mu \text{m}$ </tex-math></inline-formula>
in the axial direction) and a high localization reliability (Jaccard index of 0.66) can be obtained by mSPCN-ULM, compared to CS-ULM. The
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experimental results indicate that with plane wave scan at a transmit center frequency of 15.625 MHz, microvessels with diameters of
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can be detected and adjacent microvessels with a distance of
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can be separated. Furthermore, when using GPU acceleration, the data-processing time of mSPCN-ULM can be shortened to ~6 sec/frame in the simulations and ~23 sec/frame in the
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experiments, which is 3–4 orders of magnitude faster than CS-ULM. Finally, once the network is trained, mSPCN-ULM does not need parameter tuning to implement ULM. As a result, mSPCN-ULM opens the door to implement ULM with fast data-processing speed, high imaging accuracy, short data-acquisition time, and high flexibility (robustness to parameters) characteristics. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TMI.2020.2986781 | IEEE Transactions on Medical Imaging |
Keywords | DocType | Volume |
Spatial resolution,Convolution,Ultrasonic imaging,Microscopy,In vivo | Journal | 39 |
Issue | ISSN | Citations |
10 | 0278-0062 | 3 |
PageRank | References | Authors |
0.43 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Liu | 1 | 8 | 2.42 |
| Tianyang Zhou | 2 | 6 | 2.93 |
| Mengyang Lu | 3 | 3 | 0.43 |
| Yi Yang | 4 | 16 | 1.66 |
| Qiong He | 5 | 8 | 1.14 |
| Jianwen Luo | 6 | 97 | 25.83 |