Fast super-resolution ultrasound microvessel imaging using spatiotemporal data with deep fully convolutional neural network.
Fast super-resolution ultrasound microvessel imaging using spatiotemporal data with deep fully convolutional neural network.
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基于深度全卷积神经网络的超分辨率超声微血管成像。
DOI：
10.1088/1361-6560/abeb31
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发表时间：
2021-03-23
影响因子：
3.5
通讯作者：
Chen S
中科院分区：
文献类型：
--
作者：
Lok UW;Huang C;Gong P;Tang S;Yang L;Zhang W;Kim Y;Korfiatis P;Blezek DJ;Lucien F;Zheng R;Trzasko JD;Chen S
已经提出了超声波（ULM）以超过超声衍射的限制来形象，但ulm可以通过分辨率分辨率达到微血管图像，但长期的数据获取时间和处理时间是临界的限制。高度的连贯的MB信号提供了强大的功能，可将MB信号与背景伪像区分开，在这项研究中，通过空间暂时的超声数据集进行了深度的神经元网络，以更好地识别MB信号的MB信号，以更好地识别MB信号。在鸡肉胚胎脉络膜叠象膜数据集中首先证明了拟议网络的强度和移动信号。 35μm，比超声波长小（73μm）在体内的肝脏数据中进一步测试。传统的深ulm另外，重建具有1024×512像素的高分辨率超声框架的处理时间约为16毫秒，与最先进的深度紫外线相当。
Ultrasound localization microscopy (ULM) has been proposed to image microvasculature beyond the ultrasound diffraction limit. Although ULM can attain microvascular images with a sub-diffraction resolution, long data acquisition time and processing time are the critical limitations. Deep learning-based ULM (deep-ULM) has been proposed to mitigate these limitations. However, microbubble (MB) localization used in deep-ULMs is currently based on spatial information without the use of temporal information. The highly spatiotemporally coherent MB signals provide a strong feature that can be used to differentiate MB signals from background artifacts. In this study, a deep neural network was employed and trained with spatiotemporal ultrasound datasets to better identify the MB signals by leveraging both the spatial and temporal information of the MB signals. Training, validation and testing datasets were acquired from MB suspension to mimic the realistic intensity-varying and moving MB signals. The performance of the proposed network was first demonstrated in the chicken embryo chorioallantoic membrane dataset with an optical microscopic image as the reference standard. Substantial improvement in spatial resolution was shown for the reconstructed super-resolved images compared with power Doppler images. The full-width-half-maximum (FWHM) of a microvessel was improved from 133 μm to 35 μm, which is smaller than the ultrasound wavelength (73 μm). The proposed method was further tested in an in vivo human liver data. Results showed the reconstructed super-resolved images could resolve a microvessel of nearly 170 μm (FWHM). Adjacent microvessels with a distance of 670 μm, which cannot be resolved with power Doppler imaging, can be well-separated with the proposed method. Improved contrast ratios using the proposed method were shown compared with that of the conventional deep-ULM method. Additionally, the processing time to reconstruct a high-resolution ultrasound frame with an image size of 1024 × 512 pixels was around 16 ms, comparable to state-of-the-art deep-ULMs.
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DOI：
10.1109/tuffc.2017.2778941
发表时间：
2018-03
期刊：
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
影响因子：
--
作者：
Song P;Trzasko JD;Manduca A;Huang R;Kadirvel R;Kallmes DF;Chen S
通讯作者：
Chen S
影响因子：
4.2
作者：
Hingot, Vincent;Errico, Claudia;Couture, Olivier
通讯作者：
Couture, Olivier
影响因子：
2.9
作者：
Christensen-Jeffries K;Couture O;Dayton PA;Eldar YC;Hynynen K;Kiessling F;O'Reilly M;Pinton GF;Schmitz G;Tang MX;Tanter M;van Sloun RJG
通讯作者：
van Sloun RJG
影响因子：
10.6
作者：
Demene, Charlie;Deffieux, Thomas;Tanter, Mickael
通讯作者：
Tanter, Mickael
影响因子：
10.6
作者：
Christensen-Jeffries, Kirsten;Browning, Richard J.;Eckersley, Robert J.
通讯作者：
Eckersley, Robert J.