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Microbubbles

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Post-processing radio-frequency signal based on deep learning method for ultrasonic microbubble imaging.

Biomedical engineering online
BACKGROUND: Improving imaging quality is a fundamental problem in ultrasound contrast agent imaging (UCAI) research. Plane wave imaging (PWI) has been deemed as a potential method for UCAI due to its' high frame rate and low mechanical index. High fr...

Contrast-Enhanced Ultrasound Quantification: From Kinetic Modeling to Machine Learning.

Ultrasound in medicine & biology
Ultrasound contrast agents (UCAs) have opened up immense diagnostic possibilities by combined use of indicator dilution principles and dynamic contrast-enhanced ultrasound (DCE-US) imaging. UCAs are microbubbles encapsulated in a biocompatible shell....

Deep Learning for Ultrasound Localization Microscopy.

IEEE transactions on medical imaging
By localizing microbubbles (MBs) in the vasculature, ultrasound localization microscopy (ULM) has recently been proposed, which greatly improves the spatial resolution of ultrasound (US) imaging and will be helpful for clinical diagnosis. Nevertheles...

Nondestructive Detection of Targeted Microbubbles Using Dual-Mode Data and Deep Learning for Real-Time Ultrasound Molecular Imaging.

IEEE transactions on medical imaging
Ultrasound molecular imaging (UMI) is enabled by targeted microbubbles (MBs), which are highly reflective ultrasound contrast agents that bind to specific biomarkers. Distinguishing between adherent MBs and background signals can be challenging in vi...

Deep Learning of Spatiotemporal Filtering for Fast Super-Resolution Ultrasound Imaging.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Super-resolution ultrasound (SR-US) imaging is a new technique that breaks the diffraction limit and allows visualization of microvascular structures down to tens of micrometers. The image processing methods for the spatiotemporal filtering needed in...

Detection and Localization of Ultrasound Scatterers Using Convolutional Neural Networks.

IEEE transactions on medical imaging
Delay-and-sum (DAS) beamforming is unable to identify individual scatterers when their density is so high that their point spread functions overlap. This paper proposes a convolutional neural network (CNN)-based method to detect and localize high-den...

Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass.

Scientific reports
The need for the estimation of the number of microbubbles (MBs) in cardiopulmonary bypass surgery has been recognized among surgeons to avoid postoperative neurological complications. MBs that exceed the diameter of human capillaries may cause endoth...

Super-Resolution Ultrasound Localization Microscopy Through Deep Learning.

IEEE transactions on medical imaging
Ultrasound localization microscopy has enabled super-resolution vascular imaging through precise localization of individual ultrasound contrast agents (microbubbles) across numerous imaging frames. However, analysis of high-density regions with signi...

A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy.

IEEE transactions on medical imaging
Ultrasound Localization Microscopy (ULM) can resolve the microvascular bed down to a few micrometers. To achieve such performance, microbubble contrast agents must perfuse the entire microvascular network. Microbubbles are then located individually a...

Faster super-resolution ultrasound imaging with a deep learning model for tissue decluttering and contrast agent localization.

Biomedical physics & engineering express
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 b...