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Found 41 papers, 4 papers with code
Exploiting Mechanics-Based Priors for Lateral Displacement Estimation in Ultrasound Elastography
This paper proposes exploiting the effective Poisson's ratio (EPR)-based mechanical correspondence between the axial and lateral strains along with the RF data fidelity and displacement continuity to improve the lateral displacement and strain estimation accuracies.
Phase Aberration Correction without Reference Data: An Adaptive Mixed Loss Deep Learning Approach
Phase aberration is one of the primary sources of image quality degradation in ultrasound, which is induced by spatial variations in sound speed across the heterogeneous medium.
A deep learning approach for patchless estimation of ultrasound quantitative parametric image with uncertainty measurement
Quantitative ultrasound (QUS) aims to find properties of scatterers which are related to the tissue microstructure.
Lateral Strain Imaging using Self-supervised and Physically Inspired Constraints in Unsupervised Regularized Elastography
Many modifications have been proposed to improve the displacement estimation of CNNs for USE in the axial direction.
Homodyned K-distribution: parameter estimation and uncertainty quantification using Bayesian neural networks
Speckle statistics are the QUS parameters that describe the first order statistics of ultrasound (US) envelope data.
Infusing known operators in convolutional neural networks for lateral strain imaging in ultrasound elastography
This method took into account the range of the feasible lateral strain defined by the rules of physics of motion and employed a regularization strategy to improve the lateral strains.
Transformer-Based Microbubble Localization
Ultrasound Localization Microscopy (ULM) is an emerging technique that employs the localization of echogenic microbubbles (MBs) to finely sample and image the microcirculation beyond the diffraction limit of ultrasound imaging.
Analytic Optimization-Based Microbubble Tracking in Ultrasound Super-Resolution Microscopy
Ultrasound localization microscopy (ULM) refers to a promising medical imaging modality that systematically leverages the advantages of contrast-enhanced ultrasound (CEUS) to surpass the diffraction barrier and delineate the microvascular map.
DiffeoRaptor: Diffeomorphic Inter-modal Image Registration using RaPTOR
Results: The proposed algorithm, named DiffeoRaptor, was validated with three public databases for the tasks of brain and abdominal image registration while comparing the results against three state-of-the-art techniques, including FLASH, NiftyReg, and Symmetric image normalization (SyN).
Physics-Inspired Regularized Pulse-Echo Quantitative Ultrasound: Efficient Optimization with ADMM
Pulse-echo Quantitative ultrasound (PEQUS), which estimates the quantitative properties of tissue microstructure, entails estimating the average attenuation and the backscatter coefficient (BSC).
RESECT-SEG: Open access annotations of intra-operative brain tumor ultrasound images
The proposed dataset contains tumor tissues and resection cavity annotations of the iUS images.
Inverse Problem of Ultrasound Beamforming with Denoising-Based Regularized Solutions
During the past few years, inverse problem formulations of ultrasound beamforming have attracted a growing interest.
Deep Estimation of Speckle Statistics Parametric Images
Quantitative Ultrasound (QUS) provides important information about the tissue properties.
Physically Inspired Constraint for Unsupervised Regularized Ultrasound Elastography
Recently, the architecture of the optical flow networks has been modified to be able to use RF data.
Incorporating Gradient Similarity for Robust Time Delay Estimation in Ultrasound Elastography
The data term associated with the existing techniques takes only the amplitude similarity into account and hence is not sufficiently robust to the outlier samples present in the RF frames under consideration.
Bi-Directional Semi-Supervised Training of Convolutional Neural Networks for Ultrasound Elastography Displacement Estimation
We also show that the network fine-tuned by our proposed method using experimental phantom data performs well on in vivo data similar to the network fine-tuned on in vivo data.
Robust Scatterer Number Density Segmentation of Ultrasound Images
In conventional methods, the envelope data is divided into small overlapping windows (a strategy here we refer to as patching), and statistical parameters such as SNR and skewness are employed to classify each patch of envelope data.
Ultrasound Strain Imaging using ADMM
ADMM empowers the proposed algorithm to use different techniques for optimizing different parts of the cost function and obtain high-contrast strain images with smooth background and sharp boundaries.
Deep Ultrasound Denoising Without Clean Data
On one hand, the transmitted ultrasound beam gets attenuated as propagates through the tissue.
Second-Order Ultrasound Elastography with L1-norm Spatial Regularization
To resolve these issues, herein, we propose a novel TDE algorithm where instead of L2-, L1-norms of both first- and second-order displacement derivatives are taken into account to devise the continuity functional.
A Unifying Approach to Inverse Problems of Ultrasound Beamforming and Deconvolution
In parallel to beamforming approaches, deconvolution methods have also been explored in ultrasound imaging to mitigate the adverse effects of PSF.
Vision Transformer for Classification of Breast Ultrasound Images
In the past decade, convolutional neural networks (CNNs) have emerged as the method of choice in vision applications and have shown excellent potential in automatic classification of US images.
Fast Strain Estimation and Frame Selection in Ultrasound Elastography using Machine Learning
In the inference stage, we use dynamic programming (DP) to compute an initial displacement estimate of around 1% of the samples, and then decompose this sparse displacement into a linear combination of the 12 displacement modes.
Plane-Wave Ultrasound Beamforming: A Deep Learning Approach
Simulation test results confirm that the proposed method reconstructs images with a high quality in terms of resolution and contrast, which are also visually similar to the proposed ground-truth image.
An Ultra-Fast Method for Simulation of Realistic Ultrasound Images
Convolutional neural networks (CNNs) have attracted a rapidly growing interest in a variety of different processing tasks in the medical ultrasound community.
Estimation of the Scatterer Size Distributions in Quantitative Ultrasound Using Constrained Optimization
In the first technique, we cast scatterer size distribution as an optimization problem, and efficiently solve it using a linear system of equations.
Ultrasound Domain Adaptation Using Frequency Domain Analysis
A common issue in exploiting simulated ultrasound data for training neural networks is the domain shift problem, where the trained models on synthetic data are not generalizable to clinical data.
Automatic 3D Ultrasound Segmentation of Uterus Using Deep Learning
In the second scenario, our proposed network was trained using all the planes of each 3D volume.
Explainable AI and susceptibility to adversarial attacks: a case study in classification of breast ultrasound images
Ultrasound is a non-invasive imaging modality that can be conveniently used to classify suspicious breast nodules and potentially detect the onset of breast cancer.
Investigating Shift-Variance of Convolutional Neural Networks in Ultrasound Image Segmentation
To the best of our knowledge, this problem has not been studied in ultrasound image segmentation or even more broadly in ultrasound images.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
Fast and Robust Localization of Surgical Array using Kalman Filter
This paper introduces a fast and computationally efficient implementation of linear KF to improve the measurement accuracy of an optical tracking system with high temporal resolution.
Robotics
Virtual Source Synthetic Aperture for Accurate Lateral Displacement Estimation in Ultrasound Elastography
Despite capabilities of elastography techniques in estimating displacement in both axial and lateral directions, estimation of axial displacement is more accurate than lateral direction due to higher sampling frequency, higher resolution and having a carrier signal propagating in the axial direction.
Ultrasound Scatterer Density Classification Using Convolutional Neural Networks by Exploiting Patch Statistics
The results also show that the proposed network is able to work with different imaging parameters with no need for a reference phantom.
Semi-Supervised Training of Optical Flow Convolutional Neural Networks in Ultrasound Elastography
Convolutional Neural Networks (CNN) have been found to have great potential in optical flow problems thanks to an abundance of data available for training a deep network.
Image and Video Processing
Fine tuning U-Net for ultrasound image segmentation: which layers?
In this study, we investigated the effect of fine-tuning different layers of a U-Net which was trained on segmentation of natural images in breast ultrasound image segmentation.
Automatic Frame Selection using CNN in Ultrasound Elastography
Ultrasound elastography is used to estimate the mechanical properties of the tissue by monitoring its response to an internal or external force.
Breast lesion segmentation in ultrasound images with limited annotated data
Ultrasound (US) is one of the most commonly used imaging modalities in both diagnosis and surgical interventions due to its low-cost, safety, and non-invasive characteristic.
Automatic Frame Selection Using MLP Neural Network in Ultrasound Elastography
This work focuses on strain imaging in quasi-static elastography, where the tissue undergoes slow deformations and strain images are estimated as a surrogate for elasticity modulus.
Fast Approximate Time-Delay Estimation in Ultrasound Elastography Using Principal Component Analysis
Time delay estimation (TDE) is a critical and challenging step in all ultrasound elastography methods.
Ultrasound segmentation using U-Net: learning from simulated data and testing on real data
Therefore, in this study, we propose the use of simulated ultrasound (US) images for training the U-Net deep learning segmentation architecture and test on tissue-mimicking phantom data collected by an ultrasound machine.
