RT Journal Article
SR Electronic
T1 Localization free super-resolution microbubble velocimetry using a long short-term memory neural network
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.10.01.462404
DO 10.1101/2021.10.01.462404
A1 Xi Chen
A1 Matthew R. Lowerison
A1 Zhijie Dong
A1 Nathiya Vaithiyalingam Chandra Sekaran
A1 Chengwu Huang
A1 Shigao Chen
A1 Timothy M. Fan
A1 Daniel A. Llano
A1 Pengfei Song
YR 2022
UL http://biorxiv.org/content/early/2022/05/18/2021.10.01.462404.abstract
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.