PT - JOURNAL ARTICLE AU - Xi Chen AU - Matthew R. Lowerison AU - Zhijie Dong AU - Nathiya Vaithiyalingam Chandra Sekaran AU - Chengwu Huang AU - Shigao Chen AU - Timothy M. Fan AU - Daniel A. Llano AU - Pengfei Song TI - Localization free super-resolution microbubble velocimetry using a long short-term memory neural network AID - 10.1101/2021.10.01.462404 DP - 2022 Jan 01 TA - bioRxiv PG - 2021.10.01.462404 4099 - http://biorxiv.org/content/early/2022/05/18/2021.10.01.462404.short 4100 - http://biorxiv.org/content/early/2022/05/18/2021.10.01.462404.full AB - 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 kidney, tumor, and brain imaging.Competing Interest StatementThe authors have declared no competing interest.