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Sparse Convolutional Beamforming for Ultrasound Imaging
Regev Cohen,Yonina C. Eldar +1 more
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TLDR
In this article, a nonlinear beamformer called COnvolutional Beamforming Algorithm (COBA) is proposed to reduce the number of receiving channels while producing high quality images.Abstract:
The standard technique used by commercial medical ultrasound systems to form B-mode images is delay and sum (DAS) beamforming. However, DAS often results in limited image resolution and contrast, which are governed by the center frequency and the aperture size of the ultrasound transducer. A large number of elements leads to improved resolution but at the same time increases the data size and the system cost due to the receive electronics required for each element. Therefore, reducing the number of receiving channels while producing high quality images is of great importance. In this paper, we introduce a nonlinear beamformer called COnvolutional Beamforming Algorithm (COBA), which achieves significant improvement of lateral resolution and contrast. In addition, it can be implemented efficiently using the fast Fourier transform. Based on the COBA concept, we next present two sparse beamformers with closed form expressions for the sensor locations, which result in the same beam pattern as DAS and COBA while using far fewer array elements. Optimization of the number of elements shows that they require a minimal number of elements which is on the order of the square root of the number used by DAS. The performance of the proposed methods is tested and validated using simulated data, phantom scans and in vivo cardiac data. The results demonstrate that COBA outperforms DAS in terms of resolution and contrast and that the suggested beamformers offer a sizable element reduction while generating images with an equivalent or improved quality in comparison to DAS.read more
Citations
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Journal ArticleDOI
Deep Learning in Ultrasound Imaging
TL;DR: In this article, the authors consider deep learning strategies in ultrasound systems, from the front end to advanced applications, and provide the reader with a broad understanding of the possible impact of deep learning methodologies on many aspects of ultrasound imaging.
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Adaptive Ultrasound Beamforming using Deep Learning
Ben Luijten,Regev Cohen,Frederik Jan De Bruijn,Harold Agnes Wilhelmus Schmeitz,Massimo Mischi,Yonina C. Eldar,Ruud J. G. van Sloun +6 more
TL;DR: In this article, a deep neural network was proposed to perform high-quality ultrasound beamforming using very little training data, and applied to two distinct ultrasound acquisition strategies (plane wave and synthetic aperture) and demonstrated that high image quality can be maintained when measuring at low data-rates, using undersampled array designs.
Journal ArticleDOI
Modified structural Luneburg lens for broadband focusing and collimation
TL;DR: In this paper, a structural Luneburg lens with variable thickness was proposed to focus and collimate flexural wave propagation in a piezoelectric energy harvester, which can lead to a significant increase in the harvested voltage compared to that obtained without focusing.
Journal ArticleDOI
CohereNet: A Deep Learning Architecture for Ultrasound Spatial Correlation Estimation and Coherence-Based Beamforming
TL;DR: Results are promising for the application of deep learning to estimate correlation functions derived from ultrasound data in multiple areas of ultrasound imaging and beamforming, possibly replacing GPU-based approaches in low-power, remote, and synchronization-dependent applications.
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Regularization by Denoising via Fixed-Point Projection (RED-PRO)
TL;DR: This paper reformulates RED as a convex optimization problem utilizing a projection (RED- PRO) onto the fixed-point set of demicontractive denoisers, and establishes a novel unification of RED-PRO and PnP, while providing guarantees for their convergence to the globally optimal solution.
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