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Found 7 papers, 2 papers with code
Towards Transcranial 3D Ultrasound Localization Microscopy of the Nonhuman Primate Brain
Hemodynamic changes occur in stroke and neurodegenerative diseases.
Boosting Cardiac Color Doppler Frame Rates with Deep Learning
ConvNeXt emerged as the sole model to achieve high-quality results on in vivo aliased samples.
Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy
We show that Sparse Tensor Neural Networks in 3D ULM allow for the same benefits as dense deep learning based method in 2D ULM i. e. the use of higher concentration in silico and reduced acquisition time.
Inverse Problem Based on a Sparse Representation of Contrast-enhanced Ultrasound Data for in vivo Transcranial Imaging
First used in contrast-enhanced ultrasound (CEUS), highly echoic microbubbles allowed for the development of novel imaging modalities such as ultrasound localization microscopy (ULM).
A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy
Ultrasound data sets from multiple microbubbles flowing through the microvascular network were simulated and used as ground truth to train the 3D CNN to track microbubbles.
Ultrafast Cardiac Imaging Using Deep Learning For Speckle-Tracking Echocardiography
The obtained results showed that, while using only three DWs as input, the CNN-based approach yielded an image quality and a motion accuracy equivalent to those obtained by compounding 31 DWs free of motion artifacts.
Phase Aberration Correction for in vivo Ultrasound Localization Microscopy Using a Spatiotemporal Complex-Valued Neural Network
The CV-CNN was more robust than the coherence-based method and could perform aberration correction in a 6-month-old mouse.
