Histogram Matching for Visual Ultrasound Image Comparison

Plane wave compounding with adaptive joint coherence factor weighting

Coherent Plane Wave Compounding (CPWC) is widely used for ultrasound imaging. This technique involves transmitting plane waves into a sample at different transmit angles and recording the resultant backscattered echo at different receive positions. The time-delayed signals from the different combinations of transmit angles and receive positions are then coherently summed to produce a beamformed image. Various techniques have been developed to characterize the quality of CPWC beamforming based on the measured coherence across the transmit or receive apertures. Here, we propose a more granular approach where the signals from every transmit/receive combination are separately evaluated using a quality metric based on their joint spatio-angular coherence. The signals are then individually weighted according to their measured Joint Coherence Factor (JCF) prior to being coherently summed. To facilitate the comparison of JCF beamforming compared to alternative techniques, we further propose a method of image display standardization based on contrast matching. We show results from tissue-mimicking phantoms and human soft-tissue imaging. Fine-grained JCF weighting is found to improve CPWC image quality compared to alternative approaches.

Clutter-Generating Phantom Material. Part I: Development of a Tunable, Acoustic Clutter-Generating Layer for Use With Ultrasound Tissue-Mimicking Phantoms

OBJECTIVE

Acoustic clutter is a major source of image degradation for in vivo ultrasound imaging. However, clutter is often not represented in laboratory testing of ultrasound equipment. A phantom material is proposed that can be used to add calibrated amounts of clutter in the laboratory environment.

METHODS

Previously, the speed of sound in agar has been adjusted by varying the concentration of propanol to which the agar is exposed. That property was leveraged in this work to create a phantom with an adjustable amount of clutter. Agar spheres were soaked in propanol solution, then strained and placed in mineral oil.

RESULTS

Image quality measurements showed an approximate range of achievable contrast degradation levels of 15 dB. Stability studies with the phantom material showed that it can be stored for at least 21 d after the speed of sound tuning in propanol, but once introduced to mineral oil the clutter will change over time.

CONCLUSION

This work demonstrates a clutter-generating phantom material that can be used in conjunction with standard ultrasound imaging phantoms.

Regional Image Quality Scoring for 2-D Echocardiography Using Deep Learning

OBJECTIVE

To develop and compare methods to automatically estimate regional ultrasound image quality for echocardiography separate from view correctness.

METHODS

Three methods for estimating image quality were developed: (i) classic pixel-based metric: the generalized contrast-to-noise ratio (gCNR), computed on myocardial segments (region of interest) and left ventricle lumen (background), extracted by a U-Net segmentation model; (ii) local image coherence: the average local coherence as predicted by a U-Net model that predicts image coherence from B-mode ultrasound images at the pixel level; (iii) deep convolutional network: an end-to-end deep-learning model that predicts the quality of each region in the image directly. These methods were evaluated against manual regional quality annotations provided by three experienced cardiologists.

RESULTS

The results indicated poor performance of the gCNR metric, with Spearman correlation to annotations of ρ = 0.24. The end-to-end learning model obtained the best result, ρ = 0.69, comparable to the inter-observer correlation, ρ = 0.63. Finally, the coherence-based method, with ρ = 0.58, out-performed the classical metrics and was more generic than the end-to-end approach.

CONCLUSION

The deep convolutional network provided the most accurate regional quality prediction, while the coherence-based method offered a more generalizable solution. gCNR showed limited effectiveness in this study. The image quality prediction tool is available as an open-source Python library at https://github.com/GillesVanDeVyver/arqee.

Deep-AutoMO: Deep automated multiobjective neural network for trustworthy lesion malignancy diagnosis in the early stage via digital breast tomosynthesis

Breast cancer is the most prevalent cancer in women, and early diagnosis of malignant lesions is crucial for developing treatment plans. Digital breast tomosynthesis (DBT) has emerged as a valuable tool for early breast cancer detection, as it can identify more lesions and improve the early detection rate. Deep learning has shown great potential in medical image-based cancer diagnosis, including DBT. However, deploying these models in clinical practice may be challenging due to concerns about reliability and robustness. In this study, we developed a novel deep automated multiobjective neural network (Deep-AutoMO) to build a trustworthy model and achieve balance, safety and robustness in a unified way. During the training stage, we introduced a multiobjective immune neural architecture search (MINAS) that simultaneously considers sensitivity and specificity as objective functions, aiming to strike a balance between the two. Each neural network in Deep-AutoMO comprises a combination of a ResNet block, a DenseNet block and a pooling layer. We employ Bayesian optimization to optimize the hyperparameters in the MINAS, enhancing the efficiency of the model training process. In the testing stage, evidential reasoning based on entropy (ERE) approach is proposed to build a safe and robust model. The experimental study on DBT images demonstrated that Deep-AutoMO achieves promising performance with a well-balanced trade-off between sensitivity and specificity, outperforming currently available methods. Moreover, the model's safety is ensured through uncertainty estimation, and its robustness is improved, making it a trustworthy tool for breast cancer diagnosis in clinical settings. We have shared the code on GitHub for other researchers to use. The code can be found at https://github.com/ChaoyangZhang-XJTU/Deep-AutoMO.

Expanding generalized contrast-to-noise ratio into a clinically relevant measure of lesion detectability by considering size and spatial resolution

Purpose

Early image quality metrics were often designed with clinicians in mind, and ideal metrics would correlate with the subjective opinion of practitioners. Over time, adaptive beamformers and other post-processing methods have become more common, and these newer methods often violate assumptions of earlier image quality metrics, invalidating the meaning of those metrics. The result is that beamformers may "manipulate" metrics without producing more clinical information.

Approach

In this work, Smith et al.'s signal-to-noise ratio (SNR) metric for lesion detectability is considered, and a more robust version, here called generalized SNR (gSNR), is proposed that uses generalized contrast-to-noise ratio (gCNR) as a core. It is analytically shown that for Rayleigh distributed data, gCNR is a function of Smith et al.'sC ψ (and therefore can be used as a substitution). More robust methods for estimating the resolution cell size are considered. Simulated lesions are included to verify the equations and demonstrate behavior, and it is shown to apply equally well to in vivo data.

Results

gSNR is shown to be equivalent to SNR for delay-and-sum (DAS) beamformed data, as intended. However, it is shown to be more robust against transformations and report lesion detectability more accurately for non-Rayleigh distributed data. In the simulation included, the SNR of DAS was 4.4 ± 0.8 , and minimum variance (MV) was 6.4 ± 1.9 , but the gSNR of DAS was 4.5 ± 0.9 , and MV was 3.0 ± 0.9 , which agrees with the subjective assessment of the image. Likewise, theDAS 2 transformation (which is clinically identical to DAS) had an incorrect SNR of 9.4 ± 1.0 and a correct gSNR of 4.4 ± 0.9 . Similar results are shown in vivo.

Conclusions

Using gCNR as a component to estimate gSNR creates a robust measure of lesion detectability. Like SNR, gSNR can be compared with the Rose criterion and may better correlate with clinical assessments of image quality for modern beamformers.

High-Resolution Power Doppler Using Null Subtraction Imaging

To improve the spatial resolution of power Doppler (PD) imaging, we explored null subtraction imaging (NSI) as an alternative beamforming technique to delay-and-sum (DAS). NSI is a nonlinear beamforming approach that uses three different apodizations on receive and incoherently sums the beamformed envelopes. NSI uses a null in the beam pattern to improve the lateral resolution, which we apply here for improving PD spatial resolution both with and without contrast microbubbles. In this study, we used NSI with three types of singular value decomposition (SVD)-based clutter filters and noise equalization to generate high-resolution PD images. An element sensitivity correction scheme was also proposed as a crucial component of NSI-based PD imaging. First, a microbubble trace experiment was performed to evaluate the resolution improvement of NSI-based PD over traditional DAS-based PD. Then, both contrast-enhanced and contrast free ultrasound PD images were generated from the scan of a rat brain. The cross-sectional profile of the microbubble traces and microvessels were plotted. FWHM was also estimated to provide a quantitative metric. Furthermore, iso-frequency curves were calculated to provide a resolution evaluation metric over the global field of view. Up to six-fold resolution improvement was demonstrated by the FWHM estimate and four-fold resolution improvement was demonstrated by the iso-frequency curve from the NSI-based PD microvessel images compared to microvessel images generated by traditional DAS-based beamforming. A resolvability of [Formula: see text] was measured from the NSI-based PD microvessel image. The computational cost of NSI-based PD was only increased by 40 percent over the DAS-based PD.

Landmark-based spherical quasi-conformal mapping for hippocampal surface registration

Background

The cognitive decline induced by Alzheimer's disease (AD) is closely related to changes in hippocampal structure captured by magnetic resonance imaging (MRI). To accurately analyze the morphological changes of the hippocampus induced by AD, it is necessary to establish a one-to-one surface correspondence to compare the morphological measurements across different hippocampal surfaces. However, most existing landmark-based registration methods cannot satisfy both landmark matching and diffeomorphism under large deformations. To address these challenges, we propose a landmark-based spherical registration method via quasi-conformal mapping to establish a one-to-one correspondence between different hippocampal surfaces.

Methods

In our approach, we use the eigen-graph of the hippocampal surface to extract the intrinsic and unified landmarks of all the hippocampal surfaces and then realize the parameterization process from the hippocampal surface to a unit sphere according to the barycentric coordinate theory and the triangular mesh optimization algorithm. Finally, through the local stereographic projection, the alignment of the landmarks is achieved based on the quasi-conformal mapping on a two-dimensional (2D) plane under the constraints of Beltrami coefficients which can effectively control the topology distortion.

Results

We verified the proposed registration method on real hippocampus data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and created AD and normal control (NC) groups. Our registration algorithm achieved an area distortion index (ADI) of 0.4362e-4±0.7800e-5 in the AD group and 0.5671e-4±0.602e-5 in the NC group, and it achieved an angle distortion index (Eangle) of 0.6407±0.0258 in the AD group and 0.6271±0.0194 in the NC group. The accuracy of support vector machine (SVM) classification for the AD vs. NC groups based on the morphological features extracted from the registered hippocampal surfaces reached 94.2%.

Conclusions

This landmark-based spherical quasi-conformal mapping for hippocampal surface registration algorithm can maintain precise alignment of the landmarks and bijectivity in the presence of large deformation.

AngioMT: A MATLAB based 2D image-to-physics tool to predict oxygen transport in vascularized microphysiological systems

Microphysiological models (MPS) are increasingly getting recognized as in vitro preclinical systems of pathophysiology and drug discovery. However, there is also a growing need to adapt and advance MPS to include the physiological contributions of the capillary vascular dynamics, because they undergo angiogenesis or vasculogenesis to deliver soluble oxygen and nutrients to its organs. Currently, the process of formation of microvessels in MPS is measured arbitrarily, and vascularized MPS do not include oxygen measurements in their analysis. Sensing and measuring tissue oxygen delivery is extremely difficult because it requires access to opaque and deep tissue, and/or requires extensive integration of biosensors that makes such systems impractical to use in the real world. Here, a finite element method-based oxygen transport program, called AngioMT, is built in MATLAB. AngioMT processes the routinely acquired 2D confocal images of microvascular networks in vitro and solves physical equations of diffusion-reaction dominated oxygen transport phenomena. This user-friendly image-to-physics transition in AngioMT is an enabling tool of MPS analysis because unlike the averaged morphological measures of vessels, it provides information of the spatial transport of oxygen both within the microvessels and the surrounding tissue regions. Further, it solves the more complex higher order reaction mechanisms which also improve the physiological relevance of this tool when compared directly against in vivo measurements. Finally, the program is applied in a multicellular vascularized MPS by including the ability to define additional organ/tissue subtypes in complex co-cultured systems. Therefore, AngioMT serves as an analytical tool to enhance the predictive power and performance of MPS that incorporate microcirculation.

Spatial Coherence Approaches to Distinguish Suspicious Mass Contents in Fundamental and Harmonic Breast Ultrasound Images

When compared to fundamental B-mode imaging, coherence-based beamforming, and harmonic imaging are independently known to reduce acoustic clutter, distinguish solid from fluid content in indeterminate breast masses, and thereby reduce unnecessary biopsies during a breast cancer diagnosis. However, a systematic investigation of independent and combined coherence beamforming and harmonic imaging approaches is necessary for the clinical deployment of the most optimal approach. Therefore, we compare the performance of fundamental and harmonic images created with short-lag spatial coherence (SLSC), M-weighted SLSC (M-SLSC), SLSC combined with robust principal component analysis with no M-weighting (r-SLSC), and r-SLSC with M-weighting (R-SLSC), relative to traditional fundamental and harmonic B-mode images, when distinguishing solid from fluid breast masses. Raw channel data acquired from 40 total breast masses (28 solid, 7 fluid, 5 mixed) were beamformed and analyzed. The contrast of fluid masses was better with fundamental rather than harmonic coherence imaging, due to the lower spatial coherence within the fluid masses in the fundamental coherence images. Relative to SLSC imaging, M-SLSC, r-SLSC, and R-SLSC imaging provided similar contrast across multiple masses (with the exception of clinically challenging complicated cysts) and minimized the range of generalized contrast-to-noise ratios (gCNRs) of fluid masses, yet required additional computational resources. Among the eight coherence imaging modes compared, fundamental SLSC imaging best identified fluid versus solid breast mass contents, outperforming fundamental and harmonic B-mode imaging. With fundamental SLSC images, the specificity and sensitivity to identify fluid masses using the reader-independent metrics of contrast difference, mean lag one coherence (LOC), and gCNR were 0.86 and 1, 1 and 0.89, and 1 and 1, respectively. Results demonstrate that fundamental SLSC imaging and gCNR (or LOC if no coherence image or background region of interest is introduced) have the greatest potential to impact clinical decisions and improve the diagnostic certainty of breast mass contents. These observations are additionally anticipated to extend to masses in other organs.

F-number optimization for synthetic aperture delay-multiply-and-sum reconstruction

The choices of transmit and receive f-numbers impact both ultrasound image contrast and spatial resolution. Although previous studies have evaluated the impact of receive f-number in delay-and-sum (DAS) plane wave imaging, there has not been a systematic study of f-numbers in DAS or delay-multiply-and-sum (DMAS) synthetic aperture (SA) imaging. In this study, we measured the impact on main lobe to side lobe energy ratio (MSER), generalized contrast-to-noise ratio (gCNR), and spatial resolution when varying receive and transmit f-numbers from 1 to 5 in 0.2 increments in DAS and DMAS reconstructed SA images. A wire target in a water tank and a standard imaging phantom were used to measure these metrics. From the water tank wire target images, higher MSER values were achieved with middle-range transmit f-numbers (2-4) and high receive f-numbers (>4) for both DAS and DMAS. From the phantom contrast target images, DAS produced images with high gCNR when using high transmit f-numbers (>4) and high receive f-numbers (>4). This came at the cost of reduced spatial resolution. DMAS produced images with high gCNR when using low transmit f-numbers (<3) and high receive f-numbers (>4). DMAS was not found to have as severe of a tradeoff in spatial resolution when seeking maximum gCNR. However, gCNR was typically lower for DMAS than DAS. For both DAS and DMAS, point target images had high spatial resolution when using low receive f-numbers (<2). Spatial resolution was typically higher for DMAS than DAS. Hanning apodization was found to produce similar trends as those found with rectangular apodization. These findings give insight on the behaviors of DAS and DMAS SA reconstruction algorithms and could be used to guide f-number selection.

Exploiting Mechanics-Based Priors for Lateral Displacement Estimation in Ultrasound Elastography

Tracking the displacement between the pre- and post-deformed radio-frequency (RF) frames is a pivotal step of ultrasound elastography, which depicts tissue mechanical properties to identify pathologies. Due to ultrasound's poor ability to capture information pertaining to the lateral direction, the existing displacement estimation techniques fail to generate an accurate lateral displacement or strain map. The attempts made in the literature to mitigate this well-known issue suffer from one of the following limitations: 1) Sampling size is substantially increased, rendering the method computationally and memory expensive. 2) The lateral displacement estimation entirely depends on the axial one, ignoring data fidelity and creating large errors. This paper proposes exploiting the effective Poisson's ratio (EPR)-based mechanical correspondence between the axial and lateral strains along with the RF data fidelity and displacement continuity to improve the lateral displacement and strain estimation accuracies. We call our techniques MechSOUL (Mechanically-constrained Second-Order Ultrasound eLastography) and L1 -MechSOUL ( L1 -norm-based MechSOUL), which optimize L2 - and L1 -norm-based penalty functions, respectively. Extensive validation experiments with simulated, phantom, and in vivo datasets demonstrate that MechSOUL and L1 -MechSOUL's lateral strain and EPR estimation abilities are substantially superior to those of the recently-published elastography techniques. We have published the MATLAB codes of MechSOUL and L1 -MechSOUL at https://code.sonography.ai.

Fast 3D Super-Resolution Ultrasound With Adaptive Weight-Based Beamforming

OBJECTIVE

Super-resolution ultrasound (SRUS) imaging through localising and tracking sparse microbubbles has been shown to reveal microvascular structure and flow beyond the wave diffraction limit. Most SRUS studies use standard delay and sum (DAS) beamforming, where high side lobes and broad main lobes make isolation and localisation of densely distributed bubbles challenging, particularly in 3D due to the typically small aperture of matrix array probes.

METHOD

This study aimed to improve 3D SRUS by implementing a new fast 3D coherence beamformer based on channel signal variance. Two additional fast coherence beamformers, that have been implemented in 2D were implemented in 3D for the first time as comparison: a nonlinear beamformer with p-th root compression and a coherence factor beamformer. The 3D coherence beamformers, together with DAS, were compared in computer simulation, on a microflow phantom and in vivo.

RESULTS

Simulation results demonstrated that all three adaptive weight-based beamformers can narrow the main lobe, suppress the side lobes, while maintaining the weaker scatter signals. Improved 3D SRUS images of microflow phantom and a rabbit kidney within a 3-second acquisition were obtained using the adaptive weight-based beamformers, when compared with DAS.

CONCLUSION

The adaptive weight-based 3D beamformers can improve the SRUS and the proposed variance-based beamformer performs best in simulations and experiments.

SIGNIFICANCE

Fast 3D SRUS would significantly enhance the potential utility of this emerging imaging modality in a broad range of biomedical applications.

Methods for Enhancing the Robustness of the Generalized Contrast-to-Noise Ratio

The generalized contrast-to-noise ratio (gCNR) is a new but increasingly popular metric for measuring lesion detectability due to its use of probability distribution functions that increase robustness against transformations and dynamic range alterations. The value of these kinds of metrics has become increasingly important as it becomes clear that traditional metrics can be arbitrarily boosted with advanced beamforming or the right kinds of postprocessing. The gCNR works well for most cases; however, we will demonstrate that for some specific cases the implementation of gCNR using histograms requires careful consideration, as histograms can be poor estimates of probability density functions (PDFs) when designed improperly. This is demonstrated with simulated lesions by altering the amount of data and the number of bins used in the calculation, as well as by introducing some extreme transformations that are represented poorly by uniformly spaced histograms. In this work, the viability of a parametric gCNR implementation is tested, more robust methods for implementing histograms are considered, and a new method for estimating gCNR using empirical cumulative distribution functions (eCDFs) is shown. The most consistent methods found were to use histograms on rank-ordered data or histograms with variable bin widths, or to use eCDFs to estimate the gCNR.

Improving Visualization of Osteochondritis Dissecans Using Delay-Multiply-and-Sum Reconstruction

OBJECTIVE

Osteochondritis dissecans (OCD) of the capitellum is a joint defect that is common among adolescent athletes. It is important to diagnose OCD as early as possible, because early-stage OCD lesions have a high rate of spontaneous healing with rest. Medical ultrasound could potentially be used as a screening tool for OCD but is limited by the use of delay-and-sum (DAS) reconstruction. In this study, we tested conventional delay-multiply-and-sum (DMAS) and novel low-pass DMAS reconstruction algorithms for better visualization of OCD lesions.

METHODS

We created phantom and cadaveric OCD models that simulated a range of OCD lesion severities and stabilities. We also imaged an in vivo case of OCD in a patient study. In the reconstructed images, several profiles were taken to measure OCD lesion contrast, cartilage contrast, crack thickness error and bone interface clarity.

RESULTS

In the phantom and cadaveric OCD models, we found that histogram-matched conventional DMAS reconstruction improved lesion contrast by up to 16%, cartilage contrast by 26% and bone interface clarity by 15% on average compared with DAS reconstruction. Histogram-matched low-pass DMAS reconstruction improved lesion contrast by up to 22%, cartilage contrast by 45%, and bone interface clarity by 29% on average compared with DAS reconstruction. In the in vivo case of OCD, we found that histogram-matched conventional and low-pass DMAS reconstruction improved lesion contrast by 22% and 26%, respectively.

CONCLUSION

The application of DMAS reconstruction improved the ability of medical ultrasound to detect OCD lesions of the capitellum when compared with DAS reconstruction.

Texture analysis of ultrasound images obtained with different beamforming techniques and dynamic ranges - A robustness study

Texture analysis of medical images gives quantitative information about the tissue characterization for possible pathology discrimination. Ultrasound B-mode images are generated through a process called beamforming. Then, to obtain the final 8-bit image, the dynamic range value must be set. It is currently unknown how different beamforming techniques or dynamic range values may alter the final image texture. We provide here a robustness analysis of first and higher order texture features using six beamforming methods and seven dynamic range values, on experimental phantom and in vivo musculoskeletal images acquired using two different ultrasound research scanners. To investigate the repeatability of the texture parameters, we applied the multivariate analysis of variance (MANOVA) and estimated the intraclass correlation coefficient (ICC) on the texture features calculated on the B-mode images created with different beamforming methods and dynamic range values. We demonstrated the high repeatability of texture features when varying the dynamic range and showed texture features can differentiate between beamforming methods through a MANOVA analysis, hinting at the potential future clinical application of specific beamformers.

Implementation of constrained swept synthetic aperture using a mechanical fixture

Resolution and target detectability in ultrasound imaging are directly tied to the size of the imaging array. This is especially important for imaging at depth, such as in the detection and diagnosis of hepatocellular carcinoma and other lesions in the liver. Swept synthetic aperture (SSA) imaging has shown promise for building large effective apertures from small physical arrays using motion, but has required bulky fixtures and external motion tracking for precise positioning. In this study we present an approach that constrains the transducer motion with a simple linear sliding fixture and estimates motion from the ultrasound data itself using either speckle tracking or channel correlation. We demonstrate in simulation and phantom experiments the ability of both techniques to accurately estimate lateral transducer motion and form SSA images with improved resolution and target detectability. We observed errors under 83 μm across a 50 mm sweep in simulation and found improvements of up to 61% in resolution and up to 33% in lesion detectability experimentally even imaging through ex vivo tissue layers. This approach will increase the accessibility of SSA imaging and allow us to test its use in clinical settings.

Pixel-based approach to delay multiply and sum beamforming in combination with Wiener filter for improving ultrasound image quality

Unified pixel-based (PB) beamforming has been implemented for ultrasound imaging, offering significant enhancements in lateral resolution compared to the conventional dynamic focusing. However, it still suffers from clutter and off-axis artifacts, limiting the contrast resolution. This paper proposes an efficient method to improve image quality by integrating filtered delay multiply and sum (F-DMAS) into the framework. This hybrid strategy incorporates the spatial coherence of the received data into the beamforming process to improve contrast resolution and clutter rejection in the generated image. We also integrate a Wiener filter to suppress the spatiotemporal spreading using signals echoed from a single scatterer at the transmit focus as a kernel for the deconvolution. The Wiener filter is applied to the received waveforms before performing the hybrid strategy. The Wiener filter is shown to reduce interference due to the interaction between the excitation pulse and the transfer functions of the transducer elements, thus benefiting the axial resolution of the generated images. We validate the proposed method and compare it with other beamforming strategies through a series of experiments, including simulation, phantom, and in vivo studies. The results show that our approach can substantially improve both spatial resolution and contrast over the unified PB algorithm, while still maintaining the good features of this beamformer. The simplicity and good performance of our method show its potential for use in clinical applications.

High Frame Rate Ultrasound Imaging by Means of Tensor Completion: Application to Echocardiography

High frame rate ultrasound (US) imaging enables the monitoring of fast-moving organs. In echocardiography, this is especially needed due to the existence of rapidly moving structures, such as the heart valves. In the last two decades, various methods have been proposed to improve the frame rate. Here, we propose a novel method, based on binary coding patterns (BCPs) and tensor completion (TC), to increase the temporal resolution (i.e., frame rate) in the preprocessing stage of conventional focused ultrasound imaging (CFUI). The rationale behind our proposal is to perform, at first, the beamforming of a fraction of the scan lines, randomly selected in each frame based on BCP. Then, we reconstruct the missing scan lines through TC. The latter is an effective technique for recovering missing information from a low-rank tensor, based on a small number of observations using rank minimization. Following our approach, reducing the transmissions events needed to generate an image, the frame rate is increased by the same proportion. We have applied the proposed technique to a pre-beamformed radio frequency (RF) echocardiographic dataset. Our results show that we can improve the frame rate by a factor from 3 to 4, while keeping the structural similarity (SSIM) of the reconstructed tensor and the original one at values higher than 0.98.

Novel spatio-temporal non-linear beamformers for sparse synthetic aperture ultrasound imaging

The development of two modified non-linear beamformers, Spatio-Temporal Delay Multiply and Sum (ST-DMAS) and Spatio-Temporal Delay Euclidian-Weighted Multiply and Sum (ST-DewMAS) is reported in this paper. A sparse-transmit scheme (with only 8 transmits) on Synthetic Transmit Aperture technique (sparse STA) was chosen to evaluate the beamformers ability to generate the high-resolution Ultrasound image. These methods allow for obtaining superior-quality imaging at enhanced frame rates. The different beamformers of ST-DewMAS, ST-DMAS, Filtered Delay Multiply and Sum (F-DMAS), and Delay and Sum (DAS), were compared in terms of the Axial and Lateral Resolutions, AR and LR, respectively, Contrast-to-Noise Ratio (CNR), Contrast Ratio (CR), and Generalized CNR (GCNR). Experimental results demonstrate that the developed ST-DMAS and ST-DewMAS reconstruction on sparse STA technique resulted in better quality images compared to those obtained using DAS and F-DMAS. Specifically, the metrics of AR, LR CR, CNR, and GCNR showed improvements of more than 25% (for ST-DMAS) and 40 % (for ST-DewMAS) over those from DAS and F-DMAS beamformed images, respectively. Thus, the results demonstrate that the frame rate and image quality of an US system can both be enhanced by ST-DewMAS compared to the beamformers of F-DMAS and DAS.

Combining ADMIRE and MV to Improve Image Quality

Aperture domain model image reconstruction (ADMIRE) is a frequency-domain, model-based beamformer, in part designed for removing reverberation and off-axis clutter. Minimum variance (MV) is alternatively designed to reduce off-axis interference and improve lateral resolution. MV is known to be less effective in high incoherent noise scenarios, and its performance in the presence of reverberation has not been evaluated. By implementing ADMIRE before MV, the benefits of both these beamformers can be achieved. In this article, the assumptions of MV are discussed, specifically their relationship to reverberation clutter. The use of ADMIRE as a preprocessing step to suppress noise from simulations with linear scanning and in vivo curvilinear kidney data is demonstrated, and both narrowband and broadband implementations of MV are applied. With optimal parameters, ADMIRE + MV demonstrated sizing improvements over MV alone by an average of 52.1% in 0-dB signal-to-clutter ratio reverberation cyst simulations and 14.5% in vivo while improving the contrast ratio compared to ADMIRE alone by an average of 15.1% in simulations and 14.0% in vivo. ADMIRE + MV demonstrated a consistent improvement compared to DAS, MV, and ADMIRE both in terms of sizing and contrast ratio.

Spatial Coherence in Medical Ultrasound: A Review

Traditional pulse-echo ultrasound imaging heavily relies on the discernment of signals based on their relative magnitudes but is limited in its ability to mitigate sources of image degradation, the most prevalent of which is acoustic clutter. Advances in computing power and data storage have made it possible for echo data to be alternatively analyzed through the lens of spatial coherence, a measure of the similarity of these signals received across an array. Spatial coherence is not currently explicitly calculated on diagnostic ultrasound scanners but a large number of studies indicate that it can be employed to describe image quality, to adaptively select system parameters and to improve imaging and target detection. With the additional insights provided by spatial coherence, it is poised to play a significant role in the future of medical ultrasound. This review details the theory of spatial coherence in pulse-echo ultrasound and key advances made over the last few decades since its introduction in the 1980s.

Improved ultrasound image quality with pixel-based beamforming using a Wiener-filter and a SNR-dependent coherence factor

Pixel-based beamforming generates focused data by assuming that the waveforms received on a linear transducer array are composed of spherical pulses. It does not take into account the spatiotemporal spread in the data from the length of the excitation pulse or from the transfer functions of the transducer elements. As a result, these beamformers primarily have impacts on lateral, rather than axial, resolution. This paper proposes an efficient method to improve the axial resolution for pixel-based beamforming. We extend our field pattern analysis and show that the received waveforms should be passed through a Wiener filter before being used in the coherent pixel-based beamformer. This filter is designed based on signals echoed from a single scatterer at the transmit focus. The beamformer output is then combined with a coherence factor, that is adaptive to the signal-to-noise ratio, to improve the image contrast and suppress artifacts that have arisen during the filtering process. We validate the proposed method and compare it with other beamforming strategies using a series of experiments, including simulation, phantom and in vivo studies. It is shown to offer significant improvements in axial resolution and contrast over coherent pixel-based beamforming, as well as other spatial filters derived from synthetic aperture imaging. The method also demonstrates robustness to modeling errors in the experimental data. Overall, the imaging results show that the proposed approach has the potential to be of value in clinical applications.

Ultrasound Lesion Detectability as a Distance Between Probability Measures

Lesion detectability (LD) quantifies how easily a lesion or target can be distinguished from the background. LD is commonly used to assess the performance of new ultrasound imaging methods. The contrast-to-noise ratio (CNR) is the most popular measure of LD; however, recent work has exposed its vulnerability to manipulations of dynamic range. The generalized CNR (gCNR) has been proposed as a robust histogram-based alternative that is invariant to such manipulations. Here, we identify key shortcomings of CNR and strengths of gCNR as LD metrics for modern beamformers. Using the measure theory, we pose LD as a distance between empirical probability measures (i.e., histograms) and prove that: 1) gCNR is equal to the total variation distance between probability measures and 2) gCNR is one minus the error rate of the ideal observer. We then explore several consequences of measure-theoretic LD in simulation studies. We find that histogram distances depend on bin selection that LD must be considered in the context of spatial resolution and that many histogram distances are invariant under measure-preserving isomorphisms of the sample space (e.g., dynamic range transformations). Finally, we provide a mathematical interpretation for why quantitative values such as contrast ratio (CR), CNR, and signal-to-noise ratio should not be compared between images with different dynamic ranges or underlying units and demonstrate how histogram matching can be used to reenable such quantitative comparisons.

Training Deep Network Ultrasound Beamformers With Unlabeled In Vivo Data

Conventional delay-and-sum (DAS) beamforming is highly efficient but also suffers from various sources of image degradation. Several adaptive beamformers have been proposed to address this problem, including more recently proposed deep learning methods. With deep learning, adaptive beamforming is typically framed as a regression problem, where clean ground-truth physical information is used for training. Because it is difficult to know ground truth information in vivo, training data are usually simulated. However, deep networks trained on simulations can produce suboptimal in vivo image quality because of a domain shift between simulated and in vivo data. In this work, we propose a novel domain adaptation (DA) scheme to correct for domain shift by incorporating unlabeled in vivo data during training. Unlike classification tasks for which both input domains map to the same target domain, a challenge in our regression-based beamforming scenario is that domain shift exists in both the input and target data. To solve this problem, we leverage cycle-consistent generative adversarial networks to map between simulated and in vivo data in both the input and ground truth target domains. Additionally, to account for separate as well as shared features between simulations and in vivo data, we use augmented feature mapping to train domain-specific beamformers. Using various types of training data, we explore the limitations and underlying functionality of the proposed DA approach. Additionally, we compare our proposed approach to several other adaptive beamformers. Using the DA DNN beamformer, consistent in vivo image quality improvements are achieved compared to established techniques.

Spatial Coherence Beamforming With Multi-Line Transmission to Enhance the Contrast of Coherent Structures in Ultrasound Images Degraded by Acoustic Clutter

This work demonstrates that the combination of multi-line transmission (MLT) and short-lag spatial coherence (SLSC) imaging improves the contrast of highly coherent structures within soft tissues when compared to both traditional SLSC imaging and conventional delay and sum (DAS) beamforming. Experimental tests with small (i.e., [Formula: see text]-3 mm) targets embedded in homogeneous and heterogeneous backgrounds were conducted. DAS or SLSC images were reconstructed when implementing MLT with varying numbers of simultaneously transmitted beams. In images degraded by acoustic clutter, MLT SLSC achieved up to 34.1 dB better target contrast and up to 16 times higher frame rates when compared to the more conventional single-line transmission SLSC images, with lateral resolution improvements as large as 38.2%. MLT SLSC thus represents a promising technique for clinical applications in which ultrasound visualization of highly coherent targets is required (e.g., breast microcalcifications, kidney stones, and percutaneous biopsy needle tracking) and would otherwise be challenging due to the strong presence of acoustic clutter.