Normalized Spatial Autocorrelation in Ultrasound B-Mode Imaging for Point-Scatterer Detection

OBJECTIVE

Point-scatterer detection plays a key role in medical ultrasound B-mode imaging. Speckle noise and insufficient spatial resolution are important factors affecting point-scatterer detection. To address this issue, normalized spatial autocorrelation in ultrasound B-mode imaging (NSACB) is proposed.

METHODS

First, the acquired data are pre-processed by adding Gaussian white noise (GWN) with a certain signal-to-Gaussian white noise ratio (SGWNR). Next, normalized spatial autocorrelation is applied to the pre-processed data, and the data are divided into several new signals with different spatial lags. Then, the new signals are performed unsigned delay multiply and sum. Finally, the NSACB beamformed data are bandpass filtered by extracting the frequency component around twice the center frequency. Simulated and in vitro experiments were designed for validation.

RESULTS

Simulations revealed that the lateral resolution of NSACB measured by the -6-dB mainlobe width can reach as high as 11.11% of delay and sum (DAS), 25.01% of filtered delay multiply and sum (F-DMAS) and 50% of LAG-FDMAS-SCF. The sidelobe level of the NSACB can be reduced at most by 28 dB. Experimental results of simple and complex scatterer phantoms indicate the image resolution of the proposed NSACB can even reach up to 18.76% of DAS, 27.28% of F-DMAS and 14.29% of LAG-FDMAS-SCF. Compared with these methods, the proposed NSACB can reduce the sidelobe level at least by 18 dB.

CONCLUSION

Although the proposed method causes loss of the ability to observe hypo-echoic structures, these results suggest future work to determine the ability to detect breast microcalcifications, kidney stones, biopsy needle tracking and other scenarios requiring scatterer detection.

Super-resolution of 2D ultrasound images and videos

This paper proposes a novel deep-learning framework for super-resolution ultrasound images and videos in terms of spatial resolution and line reconstruction. To this end, we up-sample the acquired low-resolution image through a vision-based interpolation method; then, we train a learning-based model to improve the quality of the up-sampling. We qualitatively and quantitatively test our model on different anatomical districts (e.g., cardiac, obstetric) images and with different up-sampling resolutions (i.e., 2X, 4X). Our method improves the PSNR median value with respect to SOTA methods of [Formula: see text] on obstetric 2X raw images, [Formula: see text] on cardiac 2X raw images, and [Formula: see text] on abdominal raw 4X images; it also improves the number of pixels with a low prediction error of [Formula: see text] on obstetric 4X raw images, [Formula: see text] on cardiac 4X raw images, and [Formula: see text] on abdominal 4X raw images. The proposed method is then applied to the spatial super-resolution of 2D videos, by optimising the sampling of lines acquired by the probe in terms of the acquisition frequency. Our method specialises trained networks to predict the high-resolution target through the design of the network architecture and the loss function, taking into account the anatomical district and the up-sampling factor and exploiting a large ultrasound data set. The use of deep learning on large data sets overcomes the limitations of vision-based algorithms that are general and do not encode the characteristics of the data. Furthermore, the data set can be enriched with images selected by medical experts to further specialise the individual networks. Through learning and high-performance computing, the proposed super-resolution is specialised to different anatomical districts by training multiple networks. Furthermore, the computational demand is shifted to centralised hardware resources with a real-time execution of the network's prediction on local devices.

Point detection in textured ultrasound images

Detection of point scatterers in textured ultrasound images can be challenging. This paper investigates how four multilook methods can improve the detection. We analyze many images with known point scatterer locations and randomly textured backgrounds. The normalized matched filter (NMF) and multilook coherence factor (MLCF) methods are normalized methods that do not require any texture correction prior to detection analysis. They are especially propitious when optimal texture correction of the ultrasound images is difficult to obtain. The results show significant improvement in detection performance when the MLCF method is weighted with the prewhitened and texture corrected image. The method can be applied even when we do not have prior knowledge about the optimal prewhitening limits. The multilook methods NMF and NMF weighted (NMFW) are very favorable methods to apply on images where acoustic noise dominates the speckle background.

Improving axial resolution based on the deconvolution recovery method combined with adaptive weighting techniques for ultrasound imaging

BACKGROUND

A fundamental challenge in medical ultrasound imaging is to improve the resolution accurately. Adaptive beamforming is often used to improve lateral resolution, such as minimum variance (MV) and phase coherence factor (PCF). However, it is difficult to improve the axial resolution due to the limitation of the spatial pulse length (SPL) of the transmitted signal.

OBJECTIVE

A deconvolution recovery method combines two adaptive weighting techniques to improve axial resolution.

METHODS

A deconvolution recovery (DR) technique is used to improve axial resolution with a shorter SPL. Then, the DR is combined with MV and PCF (DR-MVPCF) to suppress the sidelobe. The influence of different transmission modes, regularization parameters, and the estimation of point spread function are discussed on the proposed algorithm.

RESULTS

In simulation, DR-MVPCF improved axial resolution from 0.41 mm (0.98 λ) to 0.09 mm (0.21 λ) compared with MV-PCF. In the water bath experiment, DR-MVPCF provided improvement of axial resolution from 0.39 mm (0.93 λ) to 0.07 mm (0.17 λ) compared with MV-PCF. In-vivo data experiment, the DR-MVPCF method increased the speckle signal-to-noise ratio and visibility of the structure while the contrast ratio and contrast-noise ratio decreased.

CONCLUSIONS

The proposed method can improve the axial resolution significantly.

Improving Image Quality by Deconvolution Recovery Filter in Ultrasound Imaging

Due to the advantages of non-radiation and real-time performance, ultrasound imaging is essential in medical imaging. Image quality is affected by the performance of the transducer in an ultrasound imaging system. For example, the bandwidth controls the pulse length, resulting in different axial resolutions. Therefore, a transducer with a large bandwidth helps to improve imaging quality. However, large bandwidths lead to increased system cost and sometimes a loss of sensitivity and lateral resolution in attenuating media. In this paper, a deconvolution recovery method combined with a frequency-domain filtering technique (DRF) is proposed to improve the imaging quality, especially for the axial resolution. In this method, the received low-bandwidth echo signals are converted into high-bandwidth signals, which is similar to the echo signals produced by a high-bandwidth transducer, and the imaging quality is improved. Simulation and experiment results show that, compared with Delay-and-sum (DAS) method, the DRF method improved axial resolution from 0.60 to 0.41 mm in simulation and from 0.62 to 0.47 mm in the tissue-mimicking phantom experiment. The contrast ratio performance is improved to some extent compared with the DAS in experimental and in-vivo images. Besides, the proposed method has the potential to further improve image quality by combining it with adaptive weightings, such as the minimum variance method.

Point Detection in Ultrasound Using Prewhitening and Multilook Optimization

We investigate methods to improve the detection of point scatterers in ultrasound imaging using the standard delay-and-sum (DAS) image as our starting point. An optimized whitening transform can increase the spatial resolution of the image. By splitting an image's frequency spectrum into many subsets using the multilook technique, we can exploit the coherent properties of a point scatterer. We present three new multilook methods and evaluate their effect on point detection. The performances are compared to DAS using synthetic aperture Field II simulations of a point scatterer in uniform speckle background. The results show that optimized prewhitening of the images can significantly improve the point detection. The multilook methods have the potential to improve the detection performance further when a sufficient number of looks are used. If prior knowledge about the optimal spectrum limits is unavailable and a nonoptimal prewhitening is applied, applying that the new multilook methods can considerably improve the point detection.

Ultrasound Localization Microscopy in Liquid Metal Flows

Liquid metal convection plays an important role in natural and technical processes. In experimental studies, an instrumentation with a sub-millimeter spatial resolution is required in an opaque fluid to resolve the flow field near the boundary layer. Using ultrasound methods, the trade-off between the frequency and imaging depth of typical laboratory experiments limits the spatial resolution. Therefore, the method of ultrasound localization microscopy (ULM) was introduced in liquid metal experiments for the first time in this study. To isolate the intrinsic scattering particles, an adaptive nonlinear beamformer was applied. As a result, an average spatial resolution of 188 μm could be achieved, which corresponded to a fraction of the ultrasound wavelength of 0.28. A convection experiment was measured using ULM. Due to the increased spatial resolution, the high-velocity gradients and the recirculation areas of a liquid metal convection experiment could be observed for the first time. The presented technique paves the way for in-depth flow studies of convective turbulent liquid metal flows that are close to the boundary layer.

Super-Resolution Ultrasound Imaging Scheme Based on a Symmetric Series Convolutional Neural Network

In this paper, we propose a symmetric series convolutional neural network (SS-CNN), which is a novel deep convolutional neural network (DCNN)-based super-resolution (SR) technique for ultrasound medical imaging. The proposed model comprises two parts: a feature extraction network (FEN) and an up-sampling layer. In the FEN, the low-resolution (LR) counterpart of the ultrasound image passes through a symmetric series of two different DCNNs. The low-level feature maps obtained from the subsequent layers of both DCNNs are concatenated in a feed forward manner, aiding in robust feature extraction to ensure high reconstruction quality. Subsequently, the final concatenated features serve as an input map to the latter 2D convolutional layers, where the textural information of the input image is connected via skip connections. The second part of the proposed model is a sub-pixel convolutional (SPC) layer, which up-samples the output of the FEN by multiplying it with a multi-dimensional kernel followed by a periodic shuffling operation to reconstruct a high-quality SR ultrasound image. We validate the performance of the SS-CNN with publicly available ultrasound image datasets. Experimental results show that the proposed model achieves a high-quality reconstruction of the ultrasound image over the conventional methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), while providing compelling SR reconstruction time.

Detection of Point Scatterers in Medical Ultrasound

We present an overview of the detection of point scatterers in ultrasound images and suggest strategies for evaluating and measuring the detection performance. We use synthetic aperture Field II simulations of a point scatterer in speckle background and evaluate how common imaging techniques affect point target detectability. We discuss how to compare different methods and calculate confidence intervals. In general, applying speckle reduction methods reduces the point detection performance. However, the results show that it is possible to smooth the speckle background and preserve relatively high performance with a suitable and optimized method. The different detection performances of the advanced beamforming methods coherence factor (CF), phase coherence factor (PCF), and Capon's minimum variance (MV) are presented and benchmarked with standard delay-and-sum (DAS). The results show that CF achieves slightly better detection performance than DAS for weak point scatterers, whereas PCF and MV perform worse than DAS. Choice of apodization window and adaptive aperture size affects the probability of detection. Results show that methods that preserve spatial resolution have better detection performance of point scatterers.

From Anatomy to Functional and Molecular Biomarker Imaging and Therapy: Ultrasound Is Safe, Ultrafast, Portable, and Inexpensive

Ultrasound is the most widely used medical imaging modality worldwide. It is abundant, extremely safe, portable, and inexpensive. In this review, we consider some of the current development trends for ultrasound imaging, which build upon its current strength and the popularity it experiences among medical imaging professional users.Ultrasound has rapidly expanded beyond traditional radiology departments and cardiology practices. Computing power and data processing capabilities of commonly available electronics put ultrasound systems in a lab coat pocket or on a user's mobile phone. Taking advantage of new contributions and discoveries in ultrasound physics, signal processing algorithms, and electronics, the performance of ultrasound systems and transducers have progressed in terms of them becoming smaller, with higher imaging performance, and having lower cost. Ultrasound operates in real time, now at ultrafast speeds; kilohertz frame rates are already achieved by many systems.Ultrasound has progressed beyond anatomical imaging and monitoring blood flow in large vessels. With clinical approval of ultrasound contrast agents (gas-filled microbubbles) that are administered in the bloodstream, tissue perfusion studies are now routine. Through the use of modern ultrasound pulse sequences, individual microbubbles, with subpicogram mass, can be detected and observed in real time, many centimeters deep in the body. Ultrasound imaging has broken the wavelength barrier; by tracking positions of microbubbles within the vasculature, superresolution imaging has been made possible. Ultrasound can now trace the smallest vessels and capillaries, and obtain blood velocity data in those vessels.Molecular ultrasound imaging has now moved closer to clinic; the use of microbubbles with a specific affinity to endothelial biomarkers allows selective accumulation and retention of ultrasound contrast in the areas of ischemic injury, inflammation, or neoangiogenesis. This will aid in noninvasive molecular imaging and may provide additional help with real-time guidance of biopsy, surgery, and ablation procedures.The ultrasound field can be tightly focused inside the body, many centimeters deep, with millimeter precision, and ablate lesions by energy deposition, with thermal or mechanical bioeffects. Some of such treatments are already in clinical use, with more indications progressing through the clinical trial stage. In conjunction with intravascular microbubbles, focused ultrasound can be used for tissue-specific drug delivery; localized triggered release of sequestered drugs from particles in the bloodstream may take time to get to clinic. A combination of intravascular microbubbles with circulating drug and low-power ultrasound allows transient opening of vascular endothelial barriers, including blood-brain barrier; this approach has reached clinical trial stage. Therefore, the drugs that normally would not be getting to the target tissue in the brain will now have an opportunity to produce therapeutic efficacy.Overall, medical ultrasound is developing at a brisk rate, even in an environment where other imaging modalities are also advancing rapidly and may be considered more lucrative. With all the current advances that we discuss, and many more to come, ultrasound may help solve many problems that modern medicine is facing.

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.