Compressed Sensing Doppler Ultrasound Reconstruction Using Block Sparse Bayesian Learning

Photoacoustic imaging with limited sampling: a review of machine learning approaches

Photoacoustic imaging combines high optical absorption contrast and deep acoustic penetration, and can reveal structural, molecular, and functional information about biological tissue non-invasively. Due to practical restrictions, photoacoustic imaging systems often face various challenges, such as complex system configuration, long imaging time, and/or less-than-ideal image quality, which collectively hinder their clinical application. Machine learning has been applied to improve photoacoustic imaging and mitigate the otherwise strict requirements in system setup and data acquisition. In contrast to the previous reviews of learned methods in photoacoustic computed tomography (PACT), this review focuses on the application of machine learning approaches to address the limited spatial sampling problems in photoacoustic imaging, specifically the limited view and undersampling issues. We summarize the relevant PACT works based on their training data, workflow, and model architecture. Notably, we also introduce the recent limited sampling works on the other major implementation of photoacoustic imaging, i.e., photoacoustic microscopy (PAM). With machine learning-based processing, photoacoustic imaging can achieve improved image quality with modest spatial sampling, presenting great potential for low-cost and user-friendly clinical applications.

Monitoring of Neuroendocrine Changes in Acute Stage of Severe Craniocerebral Injury by Transcranial Doppler Ultrasound Image Features Based on Artificial Intelligence Algorithm

This study was aimed at exploring the application value of transcranial Doppler (TCD) based on artificial intelligence algorithm in monitoring the neuroendocrine changes in patients with severe head injury in the acute phase; 80 patients with severe brain injury were included in this study as the study subjects, and they were randomly divided into the control group (conventional TCD) and the experimental group (algorithm-optimized TCD), 40 patients in each group. An artificial intelligence neighborhood segmentation algorithm for TCD images was designed to comprehensively evaluate the application value of this algorithm by measuring the TCD image area segmentation error and running time of this algorithm. In addition, the Glasgow coma scale (GCS) and each neuroendocrine hormone level were used to assess the neuroendocrine status of the patients. The results showed that the running time of the artificial intelligence neighborhood segmentation algorithm for TCD was 3.14 ± 1.02 s, which was significantly shorter than 32.23 ± 9.56 s of traditional convolutional neural network (CNN) algorithms (P < 0.05). The false rejection rate (FRR) of TCD image area segmentation of this algorithm was significantly reduced, and the false acceptance rate (FAR) and true acceptance rate (TAR) were significantly increased (P < 0.05). The consistent rate of the GCS score and Doppler ultrasound imaging diagnosis results in the experimental group was 93.8%, which was significantly higher than the 80.3% in the control group (P < 0.05). The consistency rate of Doppler ultrasound imaging diagnosis results of patients in the experimental group with abnormal levels of follicle stimulating hormone (FSH), prolactin (PRL), growth hormone (GH), adrenocorticotropic hormone (ACTH), and thyroid stimulating hormone (TSH) was significantly higher than that of the control group (P < 0.05). In summary, the artificial intelligence neighborhood segmentation algorithm can significantly shorten the processing time of the TCD image and reduce the segmentation error of the image area, which significantly improves the monitoring level of TCD for patients with severe craniocerebral injury and has good clinical application value.

Efficient synthetic transmit aperture ultrasound based on tensor completion

One of the most important methods in medical ultrasound imaging is the synthetic transmit aperture (STA). Despite the image quality improvement in the STA, this method suffers from several limitations, including a limited data acquisition rate and an increase in the overall time to form a single frame. Tensor completion (TC) is a powerful technique that uses rank minimization to recover missing information from a low-rank tensor. This paper provides a novel random synthetic transmit aperture (RSTA) method based on using only a randomly selected part (a fraction) of the linear array elements in the transmit mode to increase the data acquisition rate and then applying the tensor completion (TC) to improve the image quality. By the proposed method, as it is not necessary to transmit all elements sequentially, the data acquisition rate is improved and the overall time for creating an image is also significantly reduced. We investigated the proposed idea by using several simulated and experimental phantoms. Results showed that the proposed method could increase the data acquisition rate up to three times with the image quality difference of less than 6% compared to the original STA method.

Axial super-resolution in ultrasound imaging with application to non-destructive evaluation

A fundamental challenge in non-destructive evaluation using ultrasound is to accurately estimate the thicknesses of different layers or cracks present in the object under examination, which implicitly corresponds to accurately localizing the point-sources of the reflections from the measured signal. Conventional signal processing techniques cannot overcome the axial-resolution limit of the ultrasound imaging system determined by the wavelength of the transmitted pulse. In this paper, starting from the solution to the 1-D wave equation, we show that the ultrasound reflections could be effectively modeled as finite-rate-of-innovation (FRI) signals. The FRI modeling approach is a new paradigm in signal processing. Apart from allowing for the signals to be sampled below the Nyquist rate, the FRI framework also transforms the reconstruction problem into one of parametric estimation. We employ high-resolution parametric estimation techniques to solve the problem. We demonstrate axial super-resolution capability (resolution below the theoretical limit) of the proposed technique both on simulated as well as experimental data. A comparison of the FRI technique with time-domain and Fourier-domain sparse recovery techniques shows that the FRI technique is more robust. We also assess the resolvability of the proposed technique under different noise conditions on data simulated using the Field-II software and show that the reconstruction technique is robust to noise. For experimental validation, we consider Teflon sheets and Agarose phantoms of varying thicknesses. The experimental results show that the FRI technique is capable of super-resolving by a factor of three below the theoretical limit.

Temporal Super-resolution of Ultrasound Imaging Using Matrix Completion

The temporal super-resolution of the dynamic ultrasound imaging, a means to observe rapid heart movements, is considered an important subject in medical diagnosis of cardiac conditions. Here, a new technique based on the acquisition scheme using the matrix completion (MC) theory is offered for the temporal super-resolution of the two-dimensional (2D) and three-dimensional (3D) ultrasound imaging. MC mentions the problem of completing a low-rank matrix when only a subset of its elements can be observed. Here, the lower scan lines are acquired. Whereby, the proposed method uses temporal and spatial information of the radio frequency (RF) image sequences for the reconstruction of skipped RF lines. This is performed using the construction of the MC images and then reconstruction of them by the MC theory. The results of the proposed method are compared with the compressive sensing (CS) reconstruction methods. The qualitative and quantitative evaluations of 2D and 3D data demonstrate that in the proposed method, which uses the spatial and temporal relation of RF images and the MC theory, the reconstruction is more accurate, and the reconstruction error is lower. The computational complexity of this method is very low. It also does not require hardware adjustments. Therefore, it can be easily implemented in current ultrasound-imaging devices with the frame-rate enhancement. For instance, the frame rate up to two times the original sequence is feasible using the proposed methods, while root mean square error is decreased by about 35% and 30% for 2D and 3D data, respectively, compared with the CS reconstruction method.

Sparse Doppler Sensing Based on Nested Arrays

Spectral Doppler ultrasound imaging allows visualizing blood flow by estimating its velocity distribution over time. Duplex ultrasound is a modality in which an ultrasound system is used for displaying simultaneously both B-mode images and spectral Doppler data. In B-mode imaging, short wideband pulses are used to achieve sufficient spatial image resolution. In contrast, for Doppler imaging, narrowband pulses are preferred in order to attain increased spectral resolution. Thus, the acquisition time must be shared between the two sequences. In this work, we propose a nonuniform slow-time transmission scheme for spectral Doppler, based on nested arrays, which reduces the number of pulses needed for accurate spectrum recovery. We derive the minimal number of Doppler emissions needed, using this approach, for perfect reconstruction of the blood spectrum in a noise-free environment. Next, we provide two spectrum recovery techniques which achieve this minimal number. The first method performs efficient recovery based on the fast Fourier transform. The second allows for continuous recovery of the Doppler frequencies, thus avoiding off-grid error leakage, at the expense of increased complexity. The performance of the techniques is evaluated using realistic Field II simulations as well as in vivo measurements, producing accurate spectrograms of the blood velocities using a significantly reduced number of transmissions. The time gained, where no Doppler pulses are sent, can be used to enable the display of both blood velocities and high quality B-mode images at a high frame rate.

Compressed Sensing Based Synthetic Transmit Aperture for Phased Array Using Hadamard Encoded Diverging Wave Transmissions

Previously, we proposed compressed sensing based synthetic transmit aperture (CS-STA) to improve the contrast and frame rate of STA while maintaining its spatial resolution in linear array by choosing uniform random matrix as the measurement matrix and transmitting the plane waves (PWs). In this paper, to extend CS-STA for phased array imaging and further improve its performance, we design four types of CS-STA implementations with different combinations of measurement matrices (i.e., uniform random and Hadamard matrices) and transmitted waves [i.e., PW and diverging wave (DW)]. Through simulations and phantom experiments with a 3 MHz, 64-element phased array, we find that type-IV CS-STA with the combination of a Hadamard matrix and DW outperforms the other three implementations including the previously proposed type-I CS-STA in terms of image quality and reconstruction time. Specifically, PW transmission produces visible discontinuity and the reconstruction time with uniform random matrix is about 100-fold longer than that with the Hadamard matrix. Compared with STA, with eightfold higher frame rate, type-IV CS-STA achieves 8.2 and 12.3 dB higher contrast-to-noise ratio and signal-to-noise ratio in the simulations, respectively. These improvements are slightly lower in the phantom experiments, which are 6.2 and 6.6 dB, respectively. In addition, CS-STA does not deteriorate the spatial resolution of STA, with the maximum deterioration being smaller than 1/8 wavelength. These results demonstrate that type-IV CS-STA can achieve phased array imaging with high image quality at high frame rate and may be beneficial to cardiac imaging.

Low-Cost 3-D Flow Estimation of Blood With Clutter

Volumetric flow rate estimation is an important ultrasound medical imaging modality that is used for diagnosing cardiovascular diseases. Flow rates are obtained by integrating velocity estimates over a cross-sectional plane. Speckle tracking is a promising approach that overcomes the angle dependency of traditional Doppler methods, but suffers from poor lateral resolution. Recent work improves lateral velocity estimation accuracy by reconstructing a synthetic lateral phase (SLP) signal. However, the estimation accuracy of such approaches is compromised by the presence of clutter. Eigen-based clutter filtering has been shown to be effective in removing the clutter signal; but it is computationally expensive, precluding its use at high volume rates. In this paper, we propose low-complexity schemes for both velocity estimation and clutter filtering. We use a two-tiered motion estimation scheme to combine the low complexity sum-of-absolute-difference and SLP methods to achieve subpixel lateral accuracy. We reduce the complexity of eigen-based clutter filtering by processing in subgroups and replacing singular value decomposition with less compute-intensive power iteration and subspace iteration methods. Finally, to improve flow rate estimation accuracy, we use kernel power weighting when integrating the velocity estimates. We evaluate our method for fast- and slow-moving clutter for beam-to-flow angles of 90° and 60° using Field II simulations, demonstrating high estimation accuracy across scenarios. For instance, for a beam-to-flow angle of 90° and fast-moving clutter, our estimation method provides a bias of -8.8% and standard deviation of 3.1% relative to the actual flow rate.

ULA-OP 256: A 256-Channel Open Scanner for Development and Real-Time Implementation of New Ultrasound Methods

Open scanners offer an increasing support to the ultrasound researchers who are involved in the experimental test of novel methods. Each system presents specific performance in terms of number of channels, flexibility, processing power, data storage capability, and overall dimensions. This paper reports the design criteria and hardware/software implementation details of a new 256-channel ultrasound advanced open platform. This system is organized in a modular architecture, including multiple front-end boards, interconnected by a high-speed (80 Gb/s) ring, capable of finely controlling all transmit (TX) and receive (RX) signals. High flexibility and processing power (equivalent to 2500 GFLOP) are guaranteed by the possibility of individually programming multiple digital signal processors and field programmable gate arrays. Eighty GB of on-board memory are available for the storage of prebeamforming, postbeamforming, and baseband data. The use of latest generation devices allowed to integrate all needed electronics in a small size ( 34 cm ×30 cm ×26 cm). The system implements a multiline beamformer that allows obtaining images of 96 lines by 2048 depths at a frame rate of 720 Hz (expandable to 3000 Hz). The multiline beamforming capability is also exploited to implement a real-time vector Doppler scheme in which a single TX and two independent RX apertures are simultaneously used to maintain the analysis over a full pulse repetition frequency range.