ArticleIEEE transactions on medical imaging2021
Reverberation Noise Suppression in Ultrasound Channel Signals Using a 3D Fully Convolutional Neural Network.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Deconstruction and reconstruction of degrading effects in ultrasound imaging: aberration, multiple reverberation, and trailing reverberation.Biomedical physics & engineering express · 2026Article
- Labeled Numerical Phantom of Abdominal Wall for Wave-Physics-Based Ultrasound Imaging: Applications to Image Reconstruction.IEEE transactions on ultrasonics · 2026Article
- Unsupervised Beamforming with Optimized Coherence Loss for Clutter Suppression in Single Plane-Wave Ultrasound Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- UltraFlex: Iterative Model-Based Ultrasonic Flexible-Array Shape Calibration.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2025Article
- Quantification of the relative contribution of phase aberration and reverberation in transcranial ultrasound imaging: an experimentally calibrated fullwave study in 2D and 3D.Physics in medicine and biology · 2025Article
- A narrative review of deep learning in thyroid imaging: current progress and future prospects.Quantitative imaging in medicine and surgery · 2024Review
- Review of Deep Learning Approaches for Interleaved Photoacoustic and Ultrasound (PAUS) Imaging.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2023Article
- Ultrasound imaging of lung disease and its relationship to histopathology: An experimentally validated simulation approach.The Journal of the Acoustical Society of America · 2023Article
- Optimal transmit apodization for the maximization of lag-one coherence with applications to aberration delay estimation.Ultrasonics · 2023Article
- Distributed Aberration Correction Techniques Based on Tomographic Sound Speed Estimates.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2022Article
- Disease-Specific Imaging Utilizing Support Vector Machine Classification of H-Scan Parameters: Assessment of Steatosis in a Rat Model.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2022Article
- Noninvasive estimation of local speed of sound by pulse-echo ultrasound in a rat model of nonalcoholic fatty liver.Physics in medicine and biology · 2022Article
- Reverberation Clutter Suppression Using 2-D Spatial Coherence Analysis.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2022Article
- Upstream Machine Learning in Radiology.Radiologic clinics of North America · 2021Review
- Deep Learning to Obtain Simultaneous Image and Segmentation Outputs From a Single Input of Raw Ultrasound Channel Data.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2020Article
- CohereNet: A Deep Learning Architecture for Ultrasound Spatial Correlation Estimation and Coherence-Based Beamforming.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2020Article
- Assessing the Robustness of Frequency-Domain Ultrasound Beamforming Using Deep Neural Networks.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2020Article
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4 authors.
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Abstract
Diffuse reverberation is ultrasound image noise caused by multiple reflections of the transmitted pulse before returning to the transducer, which degrades image quality and impedes the estimation of displacement or flow in techniques such as elastography and Doppler imaging. Diffuse reverberation appears as spatially incoherent noise in the channel signals, where it also degrades the performance of adaptive beamforming methods, sound speed estimation, and methods that require measurements from channel signals. In this paper, we propose a custom 3D fully convolutional neural network (3DCNN) to reduce diffuse reverberation noise in the channel signals. The 3DCNN was trained with channel signals from simulations of random targets that include models of reverberation and thermal noise. It was then evaluated both on phantom and in-vivo experimental data. The 3DCNN showed improvements in image quality metrics such as generalized contrast to noise ratio (GCNR), lag one coherence (LOC) contrast-to-noise ratio (CNR) and contrast for anechoic regions in both phantom and in-vivo experiments. Visually, the contrast of anechoic regions was greatly improved. The CNR was improved in some cases, however the 3DCNN appears to strongly remove uncorrelated and low amplitude signal. In images of in-vivo carotid artery and thyroid, the 3DCNN was compared to short-lag spatial coherence (SLSC) imaging and spatial prediction filtering (FXPF) and demonstrated improved contrast, GCNR, and LOC, while FXPF only improved contrast and SLSC only improved CNR.
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