Localization Free Super-Resolution Microbubble Velocimetry Using a Long Short-Term Memory Neural Network

Xi Chen, Matthew R. Lowerison, Zhijie Dong, Nathiya Vaithiyalingam Chandra Sekaran, Daniel A. Llano, Pengfei Song

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)2374-2385
Number of pages12
JournalIEEE transactions on medical imaging
Issue number8
StatePublished - Aug 1 2023


  • Contrast ultrasound
  • deep-learning
  • micro-bubble
  • super-resolution imaging
  • ultrafast ultrasound

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Electrical and Electronic Engineering
  • Computer Science Applications


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