Fast super-resolution ultrasound microvessel imaging using spatiotemporal data with deep fully convolutional neural network. (23rd March 2021)

• Record Type: Journal Article
• Authors: Lok, U-Wai; Huang, Chengwu; Gong, Ping; Tang, Shanshan; Yang, Lulu; Zhang, Wei; Kim, Yohan; Korfiatis, Panagiotis; Blezek, Daniel J; Lucien, Fabrice; Zheng, Rongqin; Trzasko, Joshua D; Chen, Shigao
• Is Part Of: Physics in medicine & biology. Volume 66:Number 7(2021)
• Publication Date: 2021-03-23
• Subjects: deep learning-based ULM -- spatiotemporal MB signals -- fully convolutional neural network -- ultrasound microvessel imaging; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/abeb31
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: 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 μ m to 35 μ m, which is smaller than the ultrasound wavelength (73 μ m). 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 μ m (FWHM). Adjacent microvessels with a distance of 670 μ m, 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.