ArticleBiomedical physics & engineering express2021
Faster super-resolution ultrasound imaging with a deep learning model for tissue decluttering and contrast agent localization.
Article in Biomedical physics & engineering express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Super-resolution contrast-enhanced ultrasound: recent advances.Ultrasonography (Seoul, Korea) · 2026Article
- Harnessing chemically crosslinked microbubble clusters using deep learning for ultrasound contrast imaging.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Advancements in Three-Dimensional Super-Resolution Ultrasound Imaging: A Narrative Review.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2025Review
- Enhancing fetal ultrasound image quality and anatomical plane recognition in low-resource settings using super-resolution models.Scientific reports · 2025Article
- Current trends in the characterization and monitoring of vascular response to cancer therapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Super-resolution techniques for biomedical applications and challenges.Biomedical engineering letters · 2024Review
- Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals.Life (Basel, Switzerland) · 2023Review
- Assessment of Transarterial Chemoembolization Using Super-resolution Ultrasound Imaging and a Rat Model of Hepatocellular Carcinoma.Ultrasound in medicine & biology · 2023Article
- Generalized mathematical framework for contrast-enhanced ultrasound imaging with pulse inversion spectral deconvolution.Ultrasonics · 2023Article
- Three-Dimensional Ultrasound Localization Microscopy with Bipartite Graph-Based Microbubble Pairing and Kalman-Filtering-Based Tracking on a 256-Channel Verasonics Ultrasound System with a 32 × 32 Matrix Array.Journal of medical and biological engineering · 2022Article
- Research on the Multi-Element Synthetic Aperture Focusing Technique in Breast Ultrasound Imaging, Based on the Ring Array.Micromachines · 2022Article
- Super-Resolution Swin Transformer and Attention Network for Medical CT Imaging.BioMed research international · 2022Article
- Faster super-resolution ultrasound imaging with a deep learning model for tissue decluttering and contrast agent localization.Biomedical physics & engineering express · 2021Article
- 3-D Super-Resolution Ultrasound Imaging for Monitoring Early Changes in Breast Cancer after Treatment with a Vascular-Disrupting Agent.IEEE International Ultrasonics Symposium : [proceedings]. IEEE International Ultrasonics Symposium · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Super-resolution ultrasound (SR-US) imaging allows visualization of microvascular structures as small as tens of micrometers in diameter. However, use in the clinical setting has been impeded in part by ultrasound (US) acquisition times exceeding a breath-hold and by the need for extensive offline computation. Deep learning techniques have been shown to be effective in modeling the two more computationally intensive steps of microbubble (MB) contrast agent detection and localization. Performance gains by deep networks over conventional methods are more than two orders of magnitude and in addition the networks can localize overlapping MBs. The ability to separate overlapping MBs allows use of higher contrast agent concentrations and reduces US image acquisition time. Herein we propose a fully convolutional neural network (CNN) architecture to perform the operations of MB detection as well as localization in a single model. Termed SRUSnet, the network is based on the MobileNetV3 architecture modified for 3-D input data, minimal convergence time, and high-resolution data output using a flexible regression head. Also, we propose to combine linear B-mode US imaging and nonlinear contrast pulse sequencing (CPS) which has been shown to increase MB detection and further reduce the US image acquisition time. The network was trained with
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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 Socratic graph.