Aberration correction in diagnostic ultrasound: A review of the prior field and current directions.

Medical ultrasound images are reconstructed with simplifying assumptions on wave propagation, with one of the most prominent assumptions being that the imaging medium is composed of a constant sound speed. When the assumption of a constant sound speed are violated, which is true in most in vivoor clinical imaging scenarios, distortion of the transmitted and received ultrasound wavefronts appear and degrade the image quality. This distortion is known as aberration, and the techniques used to correct for the distortion are known as aberration correction techniques. Several models have been proposed to understand and correct for aberration. In this review paper, aberration and aberration correction are explored from the early models and correction techniques, including the near-field phase screen model and its associated correction techniques such as nearest-neighbor cross-correlation, to more recent models and correction techniques that incorporate spatially varying aberration and diffractive effects, such as models and techniques that rely on the estimation of the sound speed distribution in the imaging medium. In addition to historical models, future directions of ultrasound aberration correction are proposed.

Use of a convolutional neural network and quantitative ultrasound for diagnosis of fatty liver.

Quantitative ultrasound (QUS) was used to classify rabbits that were induced to have liver disease by placing them on a fatty diet for a defined duration and/or periodically injecting them with CCl4. The ground truth of the liver state was based on lipid liver percents estimated via the Folch assay and hydroxyproline concentration to quantify fibrosis. Rabbits were scanned ultrasonically in vivo using a SonixOne scanner and an L9-4/38 linear array. Liver fat percentage was classified based on the ultrasonic backscattered radiofrequency (RF) signals from the livers using either QUS or a 1-D convolutional neural network (CNN). Use of QUS parameters with linear regression and canonical correlation analysis demonstrated that the QUS parameters could differentiate between livers with lipid levels above or below 5%. However, the QUS parameters were not sensitive to fibrosis. The CNN was implemented by analyzing raw RF ultrasound signals without using separate reference data. The CNN outputs the classification of liver as either above or below a threshold of 5% fat level in the liver. The CNN outperformed the classification utilizing the QUS parameters combined with a support vector machine in differentiating between low and high lipid liver levels (i.e., accuracies of 74% versus 59% on the testing data). Therefore, although the CNN did not provide a physical interpretation of the tissue properties (e.g., attenuation of the medium or scatterer properties) the CNN had much higher accuracy in predicting fatty liver state and did not require an external reference scan.

The convolutional interpretation of registration-based plane wave steered pulse-echo local sound speed estimators.

Pulse-echo reconstruction of sound speed has long been considered a difficult problem within the domain of quantitative biomedical ultrasound. However, recent results (Jaeger 2015 Ultrasound Med. Biol. 41 235-50; Jaeger and Frenz 2015 Ultrasonics 62 299-304; Jaeger et al 2015 Phys. Med. Biol. 60 4497-515) have demonstrated that pulse-echo reconstructions of sound speed are achievable by exploiting correlations in post-beamformed data from steered, plane-wave excitations in the presence of diffuse scatterers. Despite these recent advances, a coherent theoretical imaging framework for describing the approach and results is lacking in the literature. In this work, the problem of sound speed reconstruction using steered, plane-wave excitations is reformulated as a truncated convolutional problem, and the theoretical implications of this reformulation are explored. Additionally, a matrix-free algorithm is proposed that leverages the computational and storage advantages of the fast Fourier transform (FFT) while simultaneously avoiding FFT wraparound artifacts. In particular, the storage constraints of the approach are reduced down from [Formula: see text] to [Formula: see text] over full matrix reconstruction, making this approach a better candidate for large reconstructions on clinical machines. This algorithm was then tested in the open source simulation package k-Wave to assess its robustness to modeling error and resolution reduction was demonstrated under full-wave propagation conditions relative to ideal straight-ray simulations. The method was also validated in a phantom experiment.

Improving Spatial Resolution Using Incoherent Subtraction of Receive Beams Having Different Apodizations.

In ultrasonic imaging, reduction of lateral sidelobes can result in an improved image with less distortion and fewer artifacts. In general, apodization is used to lower sidelobes in exchange for increasing the width of the main lobe, and thus decreasing lateral resolution. Null subtraction imaging (NSI) is a nonlinear image processing technique that uses different receive apodizations on copies of the same RF data to maintain low sidelobe levels while simultaneously improving lateral resolution. The images created with three different apodization functions are combined to form an image with low sidelobe levels and apparent improvements in lateral resolution compared to conventional rectangular apodization. To evaluate the performance of this technique for different imaging tasks, experiments were performed on an ATS539 phantom containing wire targets to assess lateral resolution and cylindrical anechoic and hyperechoic targets to assess contrast. NSI images were compared against rectangular apodized images and minimum variance beamformed images. In experiments, the apparent lateral resolution was observed to improve by a factor of more than 35× when compared to rectangular apodization. Image quality was assessed by the estimation of lateral resolution (-6-dB receive beamwidth), main-lobe-to-sidelobe ratio, and contrast-to-noise ratio (CNR). Imaging with NSI using a focal number of 2 (f/2), the -6-dB beamwidth on receive as measured from a small wire target in the ATS phantom was 0.03λ compared to 2.79λ for rectangular apodization. Sidelobes were observed to decrease by 32.9 dB with NSI compared to rectangular apodization. However, the ability to observe the contrast of anechoic and hyperechoic targets reduced when utilizing the NSI scheme, i.e., the CNR decreased from -3.05 to -1.01 for anechoic targets and 1.65 to 0.45 for the hyperechoic targets.

Characterizing Fatty Liver in vivo in Rabbits, Using Quantitative Ultrasound.

Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease and can often lead to fibrosis, cirrhosis, cancer and complete liver failure. Liver biopsy is the current standard of care to quantify hepatic steatosis, but it comes with increased patient risk and only samples a small portion of the liver. Imaging approaches to assess NAFLD include proton density fat fraction estimated via magnetic resonance imaging (MRI) and shear wave elastography. However, MRI is expensive and shear wave elastography is not proven to be sensitive to fat content of the liver (Kramer et al. 2016). On the other hand, ultrasonic attenuation and the backscatter coefficient (BSC) have been observed to be sensitive to levels of fat in the liver (Lin et al. 2015; Paige et al. 2017). In this study, we assessed the use of attenuation and the BSC to quantify hepatic steatosis in vivo in a rabbit model of fatty liver. Rabbits were maintained on a high-fat diet for 0, 1, 2, 3 or 6 wk, with 3 rabbits per diet group (total N = 15). An array transducer (L9-4) with a center frequency of 4.5 MHz connected to a SonixOne scanner was used to gather radio frequency (RF) backscattered data in vivo from rabbits. The RF signals were used to estimate an average attenuation and BSC for each rabbit. Two approaches were used to parameterize the BSC (i.e., the effective scatterer diameter and effective acoustic concentration using a spherical Gaussian model and a model-free approach using a principal component analysis [PCA]). The 2 major components of the PCA from the BSCs, which captured 96% of the variance of the transformed data, were used to generate input features to a support vector machine for classification. Rabbits were separated into two liver fat-level classes, such that approximately half of the rabbits were in the low-lipid class (≤9% lipid liver level) and half of the rabbits in the high-lipid class (>9% lipid liver level). The slope and the midband fit of the attenuation coefficient provided statistically significant differences (p value = 0.00014 and p value = 0.007, using a two-sample t test) between low and high-lipid fat classes. The proposed model-free and model-based parameterization of the BSC and attenuation coefficient parameters yielded classification accuracies of 84.11 %, 82.93 % and 78.91 % for differentiating low-lipid versus high-lipid classes, respectively. The results suggest that attenuation and BSC analysis can differentiate low-fat versus high-fat livers in a rabbit model of fatty liver disease.

High-Frame-Rate Doppler Ultrasound Using a Repeated Transmit Sequence.

The maximum detectable velocity of high-frame-rate color flow Doppler ultrasound is limited by the imaging frame rate when using coherent compounding techniques. Traditionally, high quality ultrasonic images are produced at a high frame rate via coherent compounding of steered plane wave reconstructions. However, this compounding operation results in an effective downsampling of the slow-time signal, thereby artificially reducing the frame rate. To alleviate this effect, a new transmit sequence is introduced where each transmit angle is repeated in succession. This transmit sequence allows for direct comparison between low resolution, pre-compounded frames at a short time interval in ways that are resistent to sidelobe motion. Use of this transmit sequence increases the maximum detectable velocity by a scale factor of the transmit sequence length. The performance of this new transmit sequence was evaluated using a rotating cylindrical phantom and compared with traditional methods using a 15-MHz linear array transducer. Axial velocity estimates were recorded for a range of ±300 mm/s and compared to the known ground truth. Using these new techniques, the root mean square error was reduced from over 400 mm/s to below 50 mm/s in the high-velocity regime compared to traditional techniques. The standard deviation of the velocity estimate in the same velocity range was reduced from 250 mm/s to 30 mm/s. This result demonstrates the viability of the repeated transmit sequence methods in detecting and quantifying high-velocity flow.