ArticleIEEE transactions on medical imaging2023
Localization Free Super-Resolution Microbubble Velocimetry Using a Long Short-Term Memory Neural Network.
Article in IEEE transactions on medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 53 citations in OpenAlex.
- Contrast-Free Ultrasound Microvascular Imaging via Radiality and Similarity Weighting.IEEE transactions on ultrasonics · 2026Article
- Clinically translatable ultrasound localization microscopy reveals cerebrovascular remodelling and prognosis in patients with traumatic brain injury.Nature biomedical engineering · 2026Article
- Super-resolution ultrasound quantifying microvascular alterations for early detection of metastatic cervical lymph nodes: a prospective diagnostic study.Scientific reports · 2025Article
- High-Volume Acquisition Rate Nonlinear Imaging Enables Robust 3-D Ultrasound Localization Microscopy.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2025Article
- Deep-Learning-Driven High Spatial Resolution Attenuation Imaging for Ultrasound Tomography (AI-UT).IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2025Article
- Enhanced ultrasound particle image velocimetry (E-uPIV) enables fast flow mapping of microvasculature.Communications engineering · 2025Article
- OptimizingPhysics in medicine and biology · 2025Article
- First clinical utility of sensing Ultrasound Localization Microscopy (sULM): identifying renal pseudotumors.Theranostics · 2025Article
- 3D ultrasound localization microscopy of the nonhuman primate brain.EBioMedicine · 2025Article
- Article
- Context-aware deep learning enables high-efficacy localization of high concentration microbubbles for super-resolution ultrasound localization microscopy.Nature communications · 2024Article
- Improving sub-pixel accuracy in ultrasound localization microscopy using supervised and self-supervised deep learning.Measurement science & technology · 2024Article
- A Convolutional Neural Network for Beamforming and Image Reconstruction in Passive Cavitation Imaging.Sensors (Basel, Switzerland) · 2023Article
- Contrast-Free Super-Resolution Power Doppler (CS-PD) Based on Deep Neural Networks.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2023Article
- Super-resolution ultrasound microvascular imaging: Is it ready for clinical use?Zeitschrift fur medizinische Physik · 2023Review
- Ultrasound localization microscopy.Zeitschrift fur medizinische Physik · 2023Review
- High-Volume-Rate 3-D Ultrasound Imaging Using Fast-Tilting and Redirecting Reflectors.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2023Article
- Backscattering amplitude in ultrasound localization microscopy.Scientific reports · 2023Article
- The Emergence of Functional Ultrasound for Noninvasive Brain-Computer Interface.Research (Washington, D.C.) · 2023Review
- Curvelet Transform-Based Sparsity Promoting Algorithm for Fast Ultrasound Localization Microscopy.IEEE transactions on medical imaging · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.