Micro-beamforming with Error Compensation

Fourier-Based Fast 3-D Ultrasound Imaging Using Row-Column-Addressed 2-D Arrays

A Fourier-based fast 3-D ultrasound imaging method using row-column-addressed (RCA) 2-D arrays is presented. The row elements in an RCA array are activated sequentially, and all the column elements are used to receive. The obtained dataset is adapted to approximate to that obtained using a fully sampled array after a plane wave at a given incident angle is transmitted. In this way, the fast algorithm in plane-wave Fourier imaging (PWFI) can be applied to the adapted dataset. In addition, synthesizing multiple datasets based on multiple incident angles enables angular compounding, which improves the image quality. The proposed method was validated using computer simulations and physical-phantom experiments. The results show that the spatial resolution and contrast of the proposed method are comparable with those of its PWFI counterpart without requiring a fully sampled (FS) array. Compared with the delay-and-sum (DAS) method using the RCA array, the proposed method provides comparable spatial resolution but lower contrast; however, the computational complexity is significantly reduced from O(N4Nz) to O(WN2Nz log2(N2Nz)) , where N is the number of elements on each side of the RCA array, Nz is the number of voxels in the axial direction in the output image, and W is the number of compounding angles. For example, in the simulated results when the maximum compounding angle M is 5°, at a given point the lateral - 6-dB width provided by the proposed method is 0.241 mm (0.267 mm for DAS), the contrast ratio of a hyperechoic cyst is 8.87 dB (9.10 dB for DAS), the number of real number operations is reduced by a factor of 20.62, and the number of memory accesses is reduced by a factor of 47.21, both compared with DAS. This novel fast algorithm could facilitate the development of compact real-time 3-D imaging systems, especially when the channel count is high and a large field of view (FOV) is required.

Improving Lateral Resolution in 3-D Imaging With Micro-beamforming Through Adaptive Beamforming by Deep Learning

There is an increased desire for miniature ultrasound probes with small apertures to provide volumetric images at high frame rates for in-body applications. Satisfying these increased requirements makes simultaneous achievement of a good lateral resolution a challenge. As micro-beamforming is often employed to reduce data rate and cable count to acceptable levels, receive processing methods that try to improve spatial resolution will have to compensate the introduced reduction in focusing. Existing beamformers do not realize sufficient improvement and/or have a computational cost that prohibits their use. Here we propose the use of adaptive beamforming by deep learning (ABLE) in combination with training targets generated by a large aperture array, which inherently has better lateral resolution. In addition, we modify ABLE to extend its receptive field across multiple voxels. We illustrate that this method improves lateral resolution both quantitatively and qualitatively, such that image quality is improved compared with that achieved by existing delay-and-sum, coherence factor, filtered-delay-multiplication-and-sum and Eigen-based minimum variance beamformers. We found that only in silica data are required to train the network, making the method easily implementable in practice.

Three-Dimensional Ultrasound Localization Microscopy with Bipartite Graph-Based Microbubble Pairing and Kalman-Filtering-Based Tracking on a 256-Channel Verasonics Ultrasound System with a 32 × 32 Matrix Array

Three-dimensional (3D) ultrasound localization microscopy (ULM) using a 2-D matrix probe and microbubbles (MBs) has been recently proposed to visualize microvasculature beyond the ultrasound diffraction limit in three spatial dimensions. However, 3D ULM suffers from several limitations: (1) high system complexity due to numerous channel counts, (2) complex MB flow dynamics in 3D, and (3) extremely long acquisition time. To reduce the system complexity while maintaining high image quality, we used a sub-aperture process to reduce received channel counts. To address the second issue, a 3D bipartite graph-based method with Kalman filtering-based tracking was used in this study for MB tracking. An MB separation approach was incorporated to separate high concentration MB data into multiple, sparser MB datasets, allowing better MB localization and tracking for a limited acquisition time. The proposed method was first validated in a flow channel phantom, showing improved spatial resolutions compared with the contrasted enhanced power Doppler image. Then the proposed method was evaluated with an in vivo chicken embryo brain dataset. Results showed that the reconstructed 3D super-resolution image achieved a spatial resolution of around 52 μm (smaller than the wavelength of around 200 μm). Microvessels that cannot be resolved clearly using localization only, can be well identified with the tailored 3D pairing and tracking algorithms. To sum up, the feasibility of the 3D ULM is shown, indicating the great possibility in clinical applications.

Design of 2D Planar Sparse Binned Arrays Based on the Coarray Analysis

The analysis of the beampattern is the base of sparse arrays design process. However, in the case of bidimensional arrays, this analysis has a high computational cost, turning the design process into a long and complex task. If the imaging system development is considered a holistic process, the aperture is a sampling grid that must be considered in the spatial domain through the coarray structure. Here, we propose to guide the aperture design process using statistical parameters of the distribution of the weights in the coarray. We have studied three designs of sparse matrix binned arrays with different sparseness degrees. Our results prove that there is a relationship between these parameters and the beampattern, which is valuable and improves the array design process. The proposed methodology reduces the computational cost up to 58 times with respect to the conventional fitness function based on the beampattern analysis.

Sparse Convolutional Beamforming for 3-D Ultrafast Ultrasound Imaging

Real-time 3-D ultrasound (US) provides a complete visualization of inner body organs and blood vasculature, crucial for diagnosis and treatment of diverse diseases. However, 3-D systems require massive hardware due to the huge number of transducer elements and consequent data size. This increases cost significantly and limit both frame rate and image quality, thus preventing the 3-D US from being common practice in clinics worldwide. A recent study presented a technique called sparse convolutional beamforming algorithm (SCOBA), which obtains improved image quality while allowing notable element reduction in the context of 2-D focused imaging. In this article, we build upon previous work and introduce a nonlinear beamformer for 3-D imaging, called COBA-3D, consisting of 2-D spatial convolution of the in-phase and quadrature received signals. The proposed technique considers diverging-wave transmission and achieves improved image resolution and contrast compared with standard delay-and-sum beamforming while enabling a high frame rate. Incorporating 2-D sparse arrays into our method creates SCOBA-3D: a sparse beamformer that offers significant element reduction and, thus, allows performing 3-D imaging with the resources typically available for 2-D setups. To create 2-D thinned arrays, we present a scalable and systematic way to design 2-D fractal sparse arrays. The proposed framework paves the way for affordable ultrafast US devices that perform high-quality 3-D imaging, as demonstrated using phantom and ex-vivo data.

3-D Large-Pitch Synthetic Transmit Aperture Imaging With a Reduced Number of Measurement Channels: A Feasibility Study

A 3-D synthetic transmit aperture ultrasound imaging system with a fully addressed array usually leads to high hardware complexity and cost since each element in the array is individually controlled. To reduce the hardware complexity, we had presented the large-pitch synthetic transmit aperture (LPSTA) ultrasound imaging for 2-D imaging using a 1-D phased array to reduce the number of measurement channels M (the product of number of transmissions, [Formula: see text], and the number of receiving channels in each transmission, [Formula: see text]). In this article, we extend this method to a 2-D matrix array for 3-D imaging. We present both numerical simulations and experimental measurements. We combined L × L adjacent elements into transmission subapertures (SAP) and K × K adjacent elements into receive SAPs in synthetic transmit aperture (STA) imaging. In the image reconstruction, we conducted the first attempt to apply and integrate Gaussian-approximated spatial response function (G-SRF) with delay and sum (DAS) to improve the image contrast, especially for the near-field targets. The imaging performance obtained from G-SRF was also evaluated numerically and compared with the previously presented frequency-domain SRF (Freq-domain SRF). The 3-D large-pitch synthetic transmit aperture (3-D-LPSTA) with G-SRF can provide a computationally efficient solution compared with the standard 3-D-STA method. With approximately 1900-fold reduction in the number of measurement channels, 3-D-LPSTA can provide image contrast comparable with the standard 3-D-STA with a full array and significantly better than using a periodically sparse array with similar complexity. In addition to reducing the system complexity, the 3-D-LPSTA achieves 700-fold reduction in computational complexity and 523-fold reduction in data storage. Finally, we evaluated and implemented the G-SRF using phantom data, which were consistent with the simulation results showing that the G-SRF can improve the image contrast. The results demonstrate that the proposed 3-D-LPSTA shows the great potential for designing an inexpensive ultrasound system to ensure the real-time 3-D clinical ultrasound imaging using large arrays. The limits of the proposed method were also discussed.