Search Results for author:
Found 8 papers, 3 papers with code
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.
Physics-Guided Neural Networks for Intraventricular Vector Flow Mapping
Intraventricular vector flow mapping (iVFM) seeks to enhance and quantify color Doppler in cardiac imaging.
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 Unwrapping of Color Doppler Echocardiography using Deep Learning
Color Doppler echocardiography is a widely used non-invasive imaging modality that provides real-time information about the intracardiac blood flow.
Extraction of volumetric indices from echocardiography: which deep learning solution for clinical use?
Deep learning-based methods have spearheaded the automatic analysis of echocardiographic images, taking advantage of the publication of multiple open access datasets annotated by experts (CAMUS being one of the largest public databases).
SIMUS: an open-source simulator for ultrasound imaging. Part II: comparison with three popular simulators
SIMUS produced pressure fields similar to those of Field II and k-Wave.
Medical Physics
SIMUS: an open-source simulator for medical ultrasound imaging. Part I: theory & examples
Conclusions: The simulations described in the accompanying paper (Part II) show that SIMUS can be used for realistic simulations in medical ultrasound imaging.
Medical Physics Signal Processing
Complex Convolutional Neural Networks for Ultrafast Ultrasound Image Reconstruction from In-Phase/Quadrature Signal
In the present study, we derive the complex equivalent of this network; i. e., the Complex-valued Inception for Diverging-wave Network (CID-Net) that operates on I/Q data.
