Localization Free Super-Resolution Microbubble Velocimetry Using a Long Short-Term Memory Neural Network.
Localization Free Super-Resolution Microbubble Velocimetry Using a Long Short-Term Memory Neural Network.
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DOI：
10.1109/tmi.2023.3251197
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发表时间：
2023-08
影响因子：
10.6
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超声定位显微镜是一种超分辨率成像技术，其利用造影剂微泡的独特特性来避开成像分辨率和穿透深度之间的基本权衡。然而，传统的重建技术仅限于低浓度的微泡，以避免定位和跟踪误差。一些研究小组已经引入了基于稀疏和深度学习的方法来克服这一限制，从重叠的微泡信号中提取有用的血管结构信息，但这些解决方案尚未被证明可以产生微循环的血流速度图。在这里，我们介绍了Deep-SMV，一种基于长短期记忆神经网络的无定位超分辨率微泡测速技术，它提供了高成像速度和对高微泡浓度的鲁棒性，并以超分辨率直接输出血液速度测量值。使用微泡流模拟对真实的体内血管数据有效地训练Deep-SMV，并展示了适用于功能血管成像和超分辨率脉动标测的实时速度图重建。该技术已成功应用于多种成像场景，包括流动通道模型、鸡胚绒毛尿囊膜和小鼠脑成像。Deep-SMV的实现可在https：//github.com/chenxiptz/SR_microvessel_velocimetry上公开获得，两个预训练模型可在https://doi.org/10.7910/DVN/SECUFD上获得。
Ultrasound localization microscopy is a super-resolution imaging technique that exploits the unique characteristics of contrast microbubbles to side-step the fundamental trade-off between imaging resolution and penetration depth. However, the conventional reconstruction technique is confined to low microbubble concentrations to avoid localization and tracking errors. Several research groups have introduced sparsity- and deep learning-based approaches to overcome this constraint to extract useful vascular structural information from overlapping microbubble signals, but these solutions have not been demonstrated to produce blood flow velocity maps of the microcirculation. Here, we introduce Deep-SMV, a localization free super-resolution microbubble velocimetry technique, based on a long short-term memory neural network, that provides high imaging speed and robustness to high microbubble concentrations, and directly outputs blood velocity measurements at a super-resolution. Deep-SMV is trained efficiently using microbubble flow simulation on real in vivo vascular data and demonstrates real-time velocity map reconstruction suitable for functional vascular imaging and pulsatility mapping at super-resolution. The technique is successfully applied to a wide variety of imaging scenarios, include flow channel phantoms, chicken embryo chorioallantoic membranes, and mouse brain imaging. An implementation of Deep-SMV is openly available at https://github.com/chenxiptz/SR_microvessel_velocimetry, with two pre-trained models available at https://doi.org/10.7910/DVN/SECUFD.