Contrast-Free Super-Resolution Power Doppler (CS-PD) Based on Deep Neural Networks.
Contrast-Free Super-Resolution Power Doppler (CS-PD) Based on Deep Neural Networks.
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DOI：
10.1109/tuffc.2023.3304527
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发表时间：
2023-10
影响因子：
3.6
通讯作者：
Song, Pengfei
中科院分区：
文献类型：
--
作者：
You, Qi;Lowerison, Matthew R.;Shin, Yirang;Chen, Xi;Sekaran, Nathiya Vaithiyalingam Chandra;Dong, Zhijie;Llano, Daniel Adolfo;Anastasio, Mark A.;Song, Pengfei
关键词：
基于超声定位显微镜(ULM)的超分辨率超声微血管成像是一种新兴的成像方式，能够将微米级的血管分辨到组织深处。在实践中，ULM受到对比剂注射的需要、长时间的数据采集和计算昂贵的后处理时间的限制。在这项研究中，我们提出了一种无对比度超分辨率能量多普勒(CS-PD)技术，该技术利用深度网络以较短的数据采集实现超分辨率。训练数据集由从活体小鼠脑中获取的时空超快超声信号组成，而测试数据集包括活体小鼠脑、鸡胚绒毛尿囊膜(CAM)和健康人类受试者。小鼠体内成像研究表明，与传统的能量多普勒相比，CS-PD在空间分辨率上可以达到大约两倍的提高。此外，CS-PD生成的微血管图像与相应的超微血管图像具有很好的一致性，结构相似指数为0.7837，峰值信噪比为25.52。此外，CS-PD能够保存与传统能量多普勒相似的血流的时间轮廓(例如，脉动性)。最后，在不同成像环境下不同组织的测试数据上验证了CS-PD的泛化能力。所提出的深度神经网络的快速推理时间也使得CS-PD可以用于实时成像。CS-PD的这些特点为多普勒超声的许多临床前和临床应用提供了一种实用、快速和强大的微血管成像解决方案。
Super-resolution ultrasound microvessel imaging based on ultrasound localization microscopy (ULM) is an emerging imaging modality that is capable of resolving micrometer-scaled vessels deep into tissue. In practice, ULM is limited by the need for contrast injection, long data acquisition, and computationally expensive postprocessing times. In this study, we present a contrast-free super-resolution power Doppler (CS-PD) technique that uses deep networks to achieve super-resolution with short data acquisition. The training dataset is comprised of spatiotemporal ultrafast ultrasound signals acquired from in vivo mouse brains, while the testing dataset includes in vivo mouse brain, chicken embryo chorioallantoic membrane (CAM), and healthy human subjects. The in vivo mouse imaging studies demonstrate that CS-PD could achieve an approximate twofold improvement in spatial resolution when compared with conventional power Doppler. In addition, the microvascular images generated by CS-PD showed good agreement with the corresponding ULM images as indicated by a structural similarity index of 0.7837 and a peak signal-to-noise ratio (PSNR) of 25.52. Moreover, CS-PD was able to preserve the temporal profile of the blood flow (e.g., pulsatility) that is similar to conventional power Doppler. Finally, the generalizability of CS-PD was demonstrated on testing data of different tissues using different imaging settings. The fast inference time of the proposed deep neural network also allows CS-PD to be implemented for real-time imaging. These features of CS-PD offer a practical, fast, and robust microvascular imaging solution for many preclinical and clinical applications of Doppler ultrasound.