Harmonic spatial coherence imaging: an ultrasonic imaging method based on backscatter coherence

Ultrasound Lung Aeration Map via Physics-Aware Neural Operators

Lung ultrasound is a growing modality in clinics for diagnosing and monitoring acute and chronic lung diseases due to its low cost and accessibility. Lung ultrasound works by emitting diagnostic pulses, receiving pressure waves and converting them into radio frequency (RF) data, which are then processed into B-mode images with beamformers for radiologists to interpret. However, unlike conventional ultrasound for soft tissue anatomical imaging, lung ultrasound interpretation is complicated by complex reverberations from the pleural interface caused by the inability of ultrasound to penetrate air. The indirect B-mode images make interpretation highly dependent on reader expertise, requiring years of training, which limits its widespread use despite its potential for high accuracy in skilled hands. To address these challenges and democratize ultrasound lung imaging as a reliable diagnostic tool, we propose Luna (the Lung Ultrasound Neural operator for Aeration), an AI model that directly reconstructs lung aeration maps from RF data, bypassing the need for traditional beamformers and indirect interpretation of B-mode images. Luna uses a Fourier neural operator, which processes RF data efficiently in Fourier space, enabling accurate reconstruction of lung aeration maps. From reconstructed aeration maps, we calculate lung percent aeration, a key clinical metric, offering a quantitative, reader-independent alternative to traditional semi-quantitative lung ultrasound scoring methods. The development of Luna involves synthetic and real data: We simulate synthetic data with an experimentally validated approach and scan ex vivo swine lungs as real data. Trained on abundant simulated data and fine-tuned with a small amount of real-world data, Luna achieves robust performance, demonstrated by an aeration estimation error of 9% in ex-vivo swine lung scans. We demonstrate the potential of directly reconstructing lung aeration maps from RF data, providing a foundation for improving lung ultrasound interpretability, reproducibility and diagnostic utility.

A unified framework combining coherent compounding, harmonic imaging and angular coherence for simultaneous high-quality B-mode and tissue Doppler in ultrafast echocardiography

Various methods have been proposed to enhance image quality in ultrafast ultrasound. Coherent compounding can improve image quality using multiple steered diverging transmits when motion occurring between transmits is corrected for. Harmonic imaging, a standard technique in conventional focused echocardiography, has been adapted for ultrafast imaging, reducing clutter. Coherence-based approaches have also been shown to increase contrast in clinical settings by enhancing signals from coherent echoes and reducing clutter. Herein, we introduce a simple, unified framework that combines motion-correction, harmonic imaging, and angular-coherence, showing for the first time that their benefits can be combined in real-time. Validation was conducted through in vitro testing on a spinning disk model and in vivo on 4 volunteers. In vitro results confirmed the unified framework capability to achieve high contrast in large-motion contexts up to 17 cm/s. In vivo testing highlighted proficiency in generating images of high quality during low and high tissue velocity phases of the cardiac cycle. Specifically, during ventricular filling, the unified framework increased the gCNR from 0.47 to 0.87 when compared against coherent compounding.

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.

Quantifying the Impact of Imaging Through Body Walls on Shear Wave Elasticity Measurements

In the context of ultrasonic hepatic shear wave elasticity imaging (SWEI), measurement success has been determined to increase when using elevated acoustic output pressures. As SWEI sequences consist of two distinct operations (pushing and tracking), acquisition failures could be attributed to (i) insufficient acoustic radiation force generation resulting in inadequate shear wave amplitude and/or (ii) distorted ultrasonic tissue motion tracking. In the study described here, an opposing window experimental setup that isolated body wall effects separately between the push and track SWEI operations was implemented. A commonly employed commercial track configuration was used, harmonic multiple-track-location SWEI. The effects of imaging through body walls on the pushing and tracking operations of SWEI as a function of mechanical index (MI), spanning 5 different push beam MIs and 10 track beam MIs, were independently assessed using porcine body walls. Shear wave speed yield was found to increase with both increasing push and track MI. Although not consistent across all samples, measurements in a subset of body walls were found to be signal limited during tracking and to increase yield by up to 35% when increasing electronic signal-to-noise ratio by increasing harmonic track transmit pressure.

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.

Diagnostic ultrasound imaging of the lung: A simulation approach based on propagation and reverberation in the human body

Although ultrasound cannot penetrate a tissue/air interface, it images the lung with high diagnostic accuracy. Lung ultrasound imaging relies on the interpretation of "artifacts," which arise from the complex reverberation physics occurring at the lung surface but appear deep inside the lung. This physics is more complex and less understood than conventional B-mode imaging in which the signal directly reflected by the target is used to generate an image. Here, to establish a more direct relationship between the underlying acoustics and lung imaging, simulations are used. The simulations model ultrasound propagation and reverberation in the human abdomen and at the tissue/air interfaces of the lung in a way that allows for direct measurements of acoustic pressure inside the human body and various anatomical structures, something that is not feasible clinically or experimentally. It is shown that the B-mode images beamformed from these acoustical simulations reproduce primary clinical features that are used in diagnostic lung imaging, i.e., A-lines and B-lines, with a clear relationship to known underlying anatomical structures. Both the oblique and parasagittal views are successfully modeled with the latter producing the characteristic "bat sign," arising from the ribs and intercostal part of the pleura. These simulations also establish a quantitative link between the percentage of fluid in exudative regions and the appearance of B-lines, suggesting that the B-mode may be used as a quantitative imaging modality.

On the Relationship between Spatial Coherence and In Situ Pressure for Abdominal Imaging

Tissue harmonic signal quality has been shown to improve with elevated acoustic pressure. The peak rarefaction pressure (PRP) for a given transmit, however, is limited by the Food and Drug Administration guidelines for mechanical index. We have previously demonstrated that the mechanical index overestimates in situ PRP for tightly focused beams in vivo, due primarily to phase aberration. In this study, we evaluate two spatial coherence-based image quality metrics-short-lag spatial coherence and harmonic short-lag spatial coherence-as proxy estimates for phase aberration and assess their correlation with in situ PRP in simulations and experiments when imaging through abdominal body walls. We demonstrate strong correlation between both spatial coherence-based metrics and in situ PRP (R² = 0.77 for harmonic short-lag spatial coherence, R² = 0.67 for short-lag spatial coherence), an observation that could be leveraged in the future for patient-specific selection of acoustic output.

Reverberation Noise Suppression in Ultrasound Channel Signals Using a 3D Fully Convolutional Neural Network

Diffuse reverberation is ultrasound image noise caused by multiple reflections of the transmitted pulse before returning to the transducer, which degrades image quality and impedes the estimation of displacement or flow in techniques such as elastography and Doppler imaging. Diffuse reverberation appears as spatially incoherent noise in the channel signals, where it also degrades the performance of adaptive beamforming methods, sound speed estimation, and methods that require measurements from channel signals. In this paper, we propose a custom 3D fully convolutional neural network (3DCNN) to reduce diffuse reverberation noise in the channel signals. The 3DCNN was trained with channel signals from simulations of random targets that include models of reverberation and thermal noise. It was then evaluated both on phantom and in-vivo experimental data. The 3DCNN showed improvements in image quality metrics such as generalized contrast to noise ratio (GCNR), lag one coherence (LOC) contrast-to-noise ratio (CNR) and contrast for anechoic regions in both phantom and in-vivo experiments. Visually, the contrast of anechoic regions was greatly improved. The CNR was improved in some cases, however the 3DCNN appears to strongly remove uncorrelated and low amplitude signal. In images of in-vivo carotid artery and thyroid, the 3DCNN was compared to short-lag spatial coherence (SLSC) imaging and spatial prediction filtering (FXPF) and demonstrated improved contrast, GCNR, and LOC, while FXPF only improved contrast and SLSC only improved CNR.

Modeling Ultrasound Propagation in the Moving Brain: Applications to Shear Shock Waves and Traumatic Brain Injury

Traumatic brain injury (TBI) studies on the living human brain are experimentally infeasible due to ethical reasons and the elastic properties of the brain degrade rapidly postmortem. We present a simulation approach that models ultrasound propagation in the human brain, while it is moving due to the complex shear shock wave deformation from a traumatic impact. Finite difference simulations can model ultrasound propagation in complex media such as human tissue. Recently, we have shown that the fullwave finite difference approach can also be used to represent displacements that are much smaller than the grid size, such as the motion encountered in shear wave propagation from ultrasound elastography. However, this subresolution displacement model, called impedance flow, was only implemented and validated for acoustical media composed of randomly distributed scatterers. Herein, we propose a generalization of the impedance flow method that describes the continuous subresolution motion of structured acoustical maps, and in particular of acoustical maps of the human brain. It is shown that the average error in simulating subresolution displacements using impedance flow is small when compared to the acoustical wavelength ( λ /1702). The method is then applied to acoustical maps of the human brain with a motion that is imposed by the propagation of a shear shock wave. This motion is determined numerically with a custom piecewise parabolic method that is calibrated to ex vivo observations of shear shocks in the porcine brain. Then the fullwave simulation tool is used to model transmit-receive imaging sequences based on an L7-4 imaging transducer. The simulated radio frequency data are beamformed using a conventional delay-and-sum method and a normalized cross-correlation method designed for shock wave tracking is used to determine the tissue motion. This overall process is an in silico reproduction of the experiments that were previously performed to observe shear shock waves in fresh porcine brain. It is shown that the proposed generalized impedance flow method accurately captures the shear wave motion in terms of the wave profile, shock front characteristics, odd harmonic spectrum generation, and acceleration at the shear shock front. We expect that this approach will lead to improvements in image sequence design that takes into account the aberration and multiple reflections from the brain and in the design of tracking algorithms that can more accurately capture the complex brain motion that occurs during a traumatic impact. These methods of modeling ultrasound propagation in moving media can also be applied to other displacements, such as those generated by shear wave elastography or blood flow.

Incoherent Clutter Suppression Using Lag-One Coherence

The lag-one coherence (LOC), derived from the correlation between the nearest-neighbor channel signals, provides a reliable measure of clutter which, under certain assumptions, can be directly related to the signal-to-noise ratio of individual channel signals. This offers a direct means to decompose the beamsum output power into contributions from speckle and spatially incoherent noise originating from acoustic clutter and thermal noise. In this study, we applied a novel method called lag-one spatial coherence adaptive normalization (LoSCAN) to locally estimate and compensate for the contribution of spatially incoherent clutter from conventional delay-and-sum (DAS) images. Suppression of incoherent clutter by LoSCAN resulted in improved image quality without introducing many of the artifacts common to other adaptive imaging methods. In simulations with known targets and added channel noise, LoSCAN was shown to restore native contrast and increase DAS dynamic range by as much as 10-15 dB. These improvements were accompanied by DAS-like speckle texture along with reduced focal dependence and artifact compared with other adaptive methods. Under in vivo liver and fetal imaging conditions, LoSCAN resulted in increased generalized contrast-to-noise ratio (gCNR) in nearly all matched image pairs ( N = 366 ) with average increases of 0.01, 0.03, and 0.05 in good-, fair-, and poor-quality DAS images, respectively, and overall changes in gCNR from -0.01 to 0.20, contrast-to-noise ratio (CNR) from -0.05 to 0.34, contrast from -9.5 to -0.1 dB, and texture μ/σ from -0.37 to -0.001 relative to DAS.

Coherence-based quantification of acoustic clutter sources in medical ultrasound

The magnitudes by which aberration and incoherent noise sources, such as diffuse reverberation and thermal noise, contribute to degradations in image quality in medical ultrasound are not well understood. Theory predicting degradations in spatial coherence and contrast in response to combinations of incoherent noise and aberration levels is presented, and the theoretical values are compared to those from simulation across a range of magnitudes. A method to separate the contributions of incoherent noise and aberration in the spatial coherence domain is also presented and applied to predictions for losses in contrast. Results indicate excellent agreement between theory and simulations for beamformer gain and expected contrast loss due to incoherent noise and aberration. Error between coherence-predicted aberration contrast loss and measured contrast loss differs by less than 1.5 dB on average, for a -20 dB native contrast target and aberrators with a range of root-mean-square time delay errors. Results also indicate in the same native contrast target the contribution of aberration to contrast loss varies with channel signal-to-noise ratio (SNR), peaking around 0 dB SNR. The proposed framework shows promise to improve the standard by which clutter reduction strategies are evaluated.

The Generalized Contrast-to-Noise Ratio: A Formal Definition for Lesion Detectability

In the last 30 years, the contrast-to-noise ratio (CNR) has been used to estimate the contrast and lesion detectability in ultrasound images. Recent studies have shown that the CNR cannot be used with modern beamformers, as dynamic range alterations can produce arbitrarily high CNR values with no real effect on the probability of lesion detection. We generalize the definition of CNR based on the overlap area between two probability density functions. This generalized CNR (gCNR) is robust against dynamic range alterations; it can be applied to all kind of images, units, or scales; it provides a quantitative measure for contrast; and it has a simple statistical interpretation, i.e., the success rate that can be expected from an ideal observer at the task of separating pixels. We test gCNR on several state-of-the-art imaging algorithms and, in addition, on a trivial compression of the dynamic range. We observe that CNR varies greatly between the state-of-the-art methods, with improvements larger than 100%. We observe that trivial compression leads to a CNR improvement of over 200%. The proposed index, however, yields the same value for compressed and uncompressed images. The tested methods showed mismatched performance in terms of lesion detectability, with variations in gCNR ranging from -0.08 to +0.29. This new metric fixes a methodological flaw in the way we study contrast and allows us to assess the relevance of new imaging algorithms.

Improved Visualization in Difficult-to-Image Stress Echocardiography Patients Using Real-Time Harmonic Spatial Coherence Imaging

Stress echocardiography is used to detect myocardial ischemia by evaluating cardiovascular function both at rest and at elevated heart rates. Stress echocardiography requires excellent visualization of the left ventricle (LV) throughout the cardiac cycle. However, LV endocardial border visualization is often negatively impacted by high levels of clutter associated with patient obesity, which has risen dramatically worldwide in recent decades. Short-lag spatial coherence (SLSC) imaging has demonstrated reduced clutter in several applications. In this work, a computationally efficient formulation of SLSC was implemented into an object-oriented graphics processing unit-based software beamformer, enabling real-time (>30 frames per second) SLSC echocardiography on a research ultrasound scanner. The system was then used to image 15 difficult-to-image stress echocardiography patients in a comparison study of tissue harmonic imaging (THI) and harmonic spatial coherence imaging (HSCI). Video clips of four standard stress echocardiography views acquired with either THI or HSCI were provided in random shuffled order to three experienced readers. Each reader rated the visibility of 17 LV segments as "invisible," "suboptimally visualized," or "well visualized," with the first two categories indicating a need for contrast agent. In a symmetry test unadjusted for patientwise clustering, HSCI demonstrated a clear superiority over THI ( ). When measured on a per-patient basis, the median total score significantly favored HSCI with . When collapsing the ratings to a two-level scale ("needs contrast" versus "well visualized"), HSCI once again showed an overall superiority over THI, with by McNemar test adjusted for clustering.

Frequency-sum beamforming for passive cavitation imaging

Beamforming includes a variety of spatial filtering techniques that may be used for determining sound source locations from near-field sensor array recordings. For this scenario, beamforming resolution depends on the acoustic frequency, array geometry, and target location. Random scattering in the medium between the source and the array may degrade beamforming resolution with higher frequencies being more susceptible to degradation. The performance of frequency-sum (FS) beamforming for reducing such sensitivity to mild scattering while increasing resolution is reported here. FS beamforming was used with a data-dependent [minimum variance (MV)] or data-independent (delay-and-sum, DAS) weight vector to produce higher frequency information from lower frequency signal components via a quadratic product of complex signal amplitudes. The current findings and comparisons are based on simulations and passive cavitation imaging experiments using 3 MHz and 6 MHz emissions recorded by a 128-element linear array. FS beamforming results are compared to conventional DAS and MV beamforming using four metrics: point spread function (PSF) size, axial and lateral contrast, and computation time. FS beamforming produces a smaller PSF than conventional DAS beamforming with less computation time than MV beamforming in free space and mild scattering environments. However, it may fail when multiple unknown sound sources are present.

Experimental evaluation of ultrasound higher-order harmonic imaging with Filtered-Delay Multiply And Sum (F-DMAS) non-linear beamforming

Tissue Harmonic Imaging (THI) mode is currently one of the preferred choices by the clinicians for its ability to provide enhanced ultrasound images, thanks to the use of the second harmonic component of backscattered echoes. This paper aims at investigating whether the combination of THI with Filtered-Delay Multiply And Sum (F-DMAS) beamforming can provide further improvements in image quality. F-DMAS is a new non-linear beamformer, which, similarly to THI, is based on the use of the second harmonics of beamformed signals and is known to increase image contrast resolution and noise rejection. Thus, we have first compared the images obtained by using F-DMAS and the standard Delay And Sum (DAS) beamformers when only the second harmonics of the received signals was selected. Moreover, possible improvements brought about by other harmonic components generated by the combined use of the fundamental plus second harmonics and F-DMAS beamforming have been explored. Experimental results demonstrate that, as compared to standard harmonic imaging with DAS, THI and F-DMAS can be joined to improve the -20 dB lateral resolution up to 1 mm, the contrast ratio up to 12 dB on a cyst-phantom and up to 9 dB on in vivo images.

Clinical Utility of Fetal Short-Lag Spatial Coherence Imaging

In this study, we evaluate the clinical utility of fetal short-lag spatial coherence (SLSC) imaging. Previous work has documented significant improvements in image quality with fetal SLSC imaging as quantified by measurements of contrast and contrast-to-noise ratio (CNR). The objective of this study was to examine whether this improved technical efficacy is indicative of the clinical utility of SLSC imaging. Eighteen healthy volunteers in their first and second trimesters of pregnancy were scanned using a modified Siemens SC2000 clinical scanner. Raw channel data were acquired for routinely examined fetal organs and used to generate fully matched raw and post-processed harmonic B-mode and SLSC image sequences, which were subsequently optimized for dynamic range and other imaging parameters by a blinded sonographer. Optimized videos were reviewed in matched B-mode and SLSC pairs by three blinded clinicians who scored each video based on overall quality, target conspicuity and border definition. SLSC imaging was highly favored over conventional imaging with SLSC scoring equal to (28.2 ± 10.5%) or higher than (63.9 ± 12.9%) B-mode for video pairs across all examined structures and processing conditions. Multivariate modeling revealed that SLSC imaging is a significant predictor of improved image quality with p ≤ 0.002. Expert-user scores for image quality support the application of SLSC in fetal ultrasound imaging.

Improved Sensitivity in Ultrasound Molecular Imaging With Coherence-Based Beamforming

Ultrasound molecular imaging (USMI) is accomplished by detecting microbubble (MB) contrast agents that have bound to specific biomarkers, and can be used for a variety of imaging applications, such as the early detection of cancer. USMI has been widely utilized in preclinical imaging in mice; however, USMI in humans can be challenging because of the low concentration of bound MBs and the signal degradation caused by the presence of heterogenous soft tissue between the transducer and the lesion. Short-lag spatial coherence (SLSC) beamforming has been proposed as a robust technique that is less affected by poor signal quality than standard delay-and-sum (DAS) beamforming. In this paper, USMI performance was assessed using contrast-enhanced ultrasound imaging combined with DAS (conventional CEUS) and with SLSC (SLSC-CEUS). Each method was characterized by flow channel phantom experiments. In a USMI-mimicking phantom, SLSC-CEUS was found to be more robust to high levels of additive thermal noise than DAS, with a 6dB SNR improvement when the thermal noise level was +6dB or higher. However, SLSC-CEUS was also found to be insensitive to increases in MB concentration, making it a poor choice for perfusion imaging. USMI performance was also measured in vivo using VEGFR2-targeted MBs in mice with subcutaneous human hepatocellular carcinoma tumors, with clinical imaging conditions mimicked using a porcine tissue layer between the tumor and the transducer. SLSC-CEUS improved the SNR in each of ten tumors by an average of 41%, corresponding to 3.0dB SNR. These results indicate that the SLSC beamformer is well-suited for USMI applications because of its high sensitivity and robust properties under challenging imaging conditions.

The Impact of Model-Based Clutter Suppression on Cluttered, Aberrated Wavefronts

Recent studies reveal that both phase aberration and reverberation play a major role in degrading ultrasound image quality. We previously developed an algorithm for suppressing clutter, but we have not yet tested it in the context of aberrated wavefronts. In this paper, we evaluate our previously reported algorithm, called aperture domain model image reconstruction (ADMIRE), in the presence of phase aberration and in the presence of multipath scattering and phase aberration. We use simulations to investigate phase aberration corruption and correction in the presence of reverberation. As part of this paper, we observed that ADMIRE leads to suppressed levels of aberration. In order to accurately characterize aberrated signals of interest, we introduced an adaptive component to ADMIRE to account for aberration, referred to as adaptive ADMIRE. We then use ADMIRE, adaptive ADMIRE, and conventional filtering methods to characterize aberration profiles on in vivo liver data. These in vivo results suggest that adaptive ADMIRE could be used to better characterize a wider range of aberrated wavefronts. The aberration profiles' full-width at half-maximum of ADMIRE, adaptive ADMIRE, and postfiltered data with 0.4- mm-1 spatial cutoff frequency are 4.0 ± 0.28 mm, 2.8 ± 1.3 mm, and 2.8 ± 0.57 mm, respectively, while the average root-mean square values in the same order are 16 ± 5.4 ns, 20 ± 6.3 ns, and 19 ± 3.9 ns, respectively. Finally, because ADMIRE suppresses aberration, we perform a limited evaluation of image quality using simulations and in vivo data to determine how ADMIRE and adaptive ADMIRE perform with and without aberration correction.

Visualization of Small-Diameter Vessels by Reduction of Incoherent Reverberation With Coherent Flow Power Doppler

Power Doppler (PD) imaging is a widely used technique for flow detection. Despite the wide use of Doppler ultrasound, limitations exist in the ability of Doppler ultrasound to assess slow flow in the small-diameter vasculature, such as the maternal spiral arteries and fetal villous arteries of the placenta and focal liver lesions. The sensitivity of PD in small vessel detection is limited by the low signal produced by slow flow and the noise associated with small vessels. The noise sources include electronic noise, stationary or slowly moving tissue clutter, reverberation clutter, and off-axis scattering from tissue, among others. In order to provide more sensitive detection of slow flow in small diameter vessels, a coherent flow imaging technique, termed coherent flow PD (CFPD), is characterized and evaluated with simulation, flow phantom experiment studies, and an in vivo animal small vessel detection study. CFPD imaging was introduced as a technique to detect slow blood flow. It has been demonstrated to detect slow flow below the detection threshold of conventional PD imaging using identical pulse sequences and filter parameters. In this paper, we compare CFPD with PD in the detection of blood flow in small-diameter vessels. The results from the study suggest that CFPD is able to provide a 7.5-12.5-dB increase in the signal-to-noise ratio (SNR) over PD images for the same physiological conditions and is less susceptible to reverberation clutter and thermal noise. Due to the increase in SNR, CFPD is able to detect small vessels in high channel noise cases, for which PD was unable to generate enough contrast to observe the vessel.

A model and regularization scheme for ultrasonic beamforming clutter reduction

Acoustic clutter produced by off-axis and multipath scattering is known to cause image degradation, and in some cases these sources may be the prime determinants of in vivo image quality. We have previously shown some success addressing these sources of image degradation by modeling the aperture domain signal from different sources of clutter, and then decomposing aperture domain data using the modeled sources. Our previous model had some shortcomings including model mismatch and failure to recover B-Mode speckle statistics. These shortcomings are addressed here by developing a better model and by using a general regularization approach appropriate for the model and data. We present results with L1 (lasso), L2 (ridge), and L1/L2 combined (elastic-net) regularization methods. We call our new method aperture domain model image reconstruction (ADMIRE). Our results demonstrate that ADMIRE with L1 regularization, or weighted toward L1 in the case of elastic-net regularization, have improved image quality. L1 by itself works well, but additional improvements are seen with elastic-net regularization over the pure L1 constraint. On in vivo example cases, L1 regularization showed mean contrast improvements of 4.6 and 6.8 dB on fundamental and harmonic images, respectively. Elastic net regularization (α = 0.9) showed mean contrast improvements of 17.8 dB on fundamental images and 11.8 dB on harmonic images. We also demonstrate that in uncluttered Field II simulations the decluttering algorithm produces the same contrast, contrast-tonoise ratio, and speckle SNR as normal B-mode imaging, demonstrating that ADMIRE preserves typical image features.

Ultrasound in Radiology: From Anatomic, Functional, Molecular Imaging to Drug Delivery and Image-Guided Therapy

During the past decade, ultrasound has expanded medical imaging well beyond the "traditional" radiology setting: a combination of portability, low cost, and ease of use makes ultrasound imaging an indispensable tool for radiologists as well as for other medical professionals who need to obtain imaging diagnosis or guide a therapeutic intervention quickly and efficiently. Ultrasound combines excellent ability for deep penetration into soft tissues with very good spatial resolution, with only a few exceptions (ie, those involving overlying bone or gas). Real-time imaging (up to hundreds and thousands of frames per second) enables guidance of therapeutic procedures and biopsies; characterization of the mechanical properties of the tissues greatly aids with the accuracy of the procedures. The ability of ultrasound to deposit energy locally brings about the potential for localized intervention encompassing the following: tissue ablation, enhancing penetration through the natural barriers to drug delivery in the body and triggering drug release from carrier microparticles and nanoparticles. The use of microbubble contrast agents brings the ability to monitor and quantify tissue perfusion, and microbubble targeting with ligand-decorated microbubbles brings the ability to obtain molecular biomarker information, that is, ultrasound molecular imaging. Overall, ultrasound has become the most widely used imaging modality in modern medicine; it will continue to grow and expand.

Resolution and brightness characteristics of short-lag spatial coherence (SLSC) images

We previously described a novel beamforming method that images the spatial correlation of an echo wave field with demonstrated applications to clutter reduction in high-noise environments. In this paper, several characteristics of the resolution and brightness of short-lag spatial coherence (SLSC) images formed by this method are compared with B-mode images formed by conventional delay-and-sum beamforming methods. Point target widths were measured to estimate resolution, the autocorrelation of image texture was measured to estimate texture size, and the contrast (i.e., brightness ratio) of clinically relevant targets was assessed. SLSC images demonstrate improved resolution and contrast with increasing values of channel noise and clutter, whereas B-mode resolution was degraded in the presence of high noise (i.e., > -12 dB channel noise-to-signal ratios) and high clutter magnitudes (i.e., > -21 dB relative to point target magnitude). Lateral resolution in SLSC images was improved with increasing lag value, whereas axial resolution was degraded with increasing correlation kernel length. The texture size of SLSC images was smaller than that of matched B-mode images. Results demonstrate that the resolution and contrast of coherence-based images depend on a range of parameters, but are generally superior to those of matched B-mode images under challenging imaging conditions.

Coherent flow power Doppler (CFPD): flow detection using spatial coherence beamforming

Power Doppler imaging is a widely used method of flow detection for tissue perfusion monitoring, inflammatory hyperemia detection, deep vein thrombosis diagnosis, and other clinical applications. However, thermal noise and clutter limit its sensitivity and ability to detect slow flow. In addition, large ensembles are required to obtain sufficient sensitivity, which limits frame rate and yields flash artifacts during moderate tissue motion. We propose an alternative method of flow detection using the spatial coherence of backscattered ultrasound echoes. The method enhances slow flow detection and frame rate, while maintaining or improving the signal quality of conventional power Doppler techniques. The feasibility of this method is demonstrated with simulations, flow-phantom experiments, and an in vivo human thyroid study. In comparison with conventional power Doppler imaging, the proposed method can produce Doppler images with 15- to 30-dB SNR improvement. Therefore, the method is able to detect flow with velocities approximately 50% lower than conventional power Doppler, or improve the frame rate by a factor of 3 with comparable image quality. The results show promise for clinical applications of the method.

In vivo application of short-lag spatial coherence and harmonic spatial coherence imaging in fetal ultrasound

Fetal scanning is one of the most common applications of ultrasound imaging and serves as a source of vital information about maternal and fetal health. Visualization of clinically relevant structures, however, can be severely compromised in difficult-to-image patients due to poor resolution and the presence of high levels of acoustical noise or clutter. We have developed novel coherence-based beamforming methods called Short-Lag Spatial Coherence (SLSC) imaging and Harmonic Spatial Coherence imaging (HSCI), and applied them to suppress the effects of clutter in fetal imaging. This method is used to create images of the spatial coherence of the backscattered ultrasound as opposed to images of echo magnitude. We present the results of a patient study to assess the benefits of coherence-based beamforming in the context of first trimester fetal exams. Matched fundamental B-mode, SLSC, harmonic B-mode, and HSCI images were generated using raw radio frequency data collected on 11 volunteers in the first trimester of pregnancy. The images were compared for qualitative differences in image texture and target conspicuity as well as using quantitative imaging metrics such as signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and contrast. SLSC and HSCI showed statistically significant improvements across all imaging metrics compared with B-mode and harmonic B-mode, respectively. These improvements were greatest for poor quality B-mode images where contrast of anechoic targets was improved from 15 dB in fundamental B-mode to 27 dB in SLSC and 17 dB in harmonic B-mode to 30 dB in HSCI. CNR improved from 1.4 to 2.5 in the fundamental images and 1.4 to 3.1 in the harmonic case. These results exhibit the potential of coherence-based beamforming to improve image quality and target detectability, especially in high noise environments.

Spatial coherence in human tissue: implications for imaging and measurement

The spatial coherence properties of the signal backscattered by human tissue and measured by an ultrasound transducer array are investigated. Fourier acoustics are used to describe the propagation of ultrasound through a model of tissue that includes reverberation and random scattering in the imaging plane. The theoretical development describes how the near-field tissue layer, transducer aperture properties, and reflectivity function at the focus reduce the spatial coherence of the imaging wave measured at the transducer surface. Simulations are used to propagate the acoustic field through a histologically characterized sample of the human abdomen and to validate the theoretical predictions. In vivo measurements performed with a diagnostic ultrasound scanner demonstrate that simulations and theory closely match the measured spatial coherence characteristics in the human body across the transducer array's entire spatial extent. The theoretical framework and simulations are then used to describe the physics of spatial coherence imaging, a type of ultrasound imaging that measures coherence properties instead of echo brightness. The same echo data from an F/2 transducer was used to generate B-mode and short lag spatial coherence images. For an anechoic lesion at the focus, the contrast-to-noise ratio is 1.21 for conventional B-mode imaging and 1.95 for spatial coherence imaging. It is shown that the contrast in spatial coherence imaging depends on the properties of the near-field tissue layer and the backscattering function in the focal plane.

Acoustic characterization of contrast-to-tissue ratio and axial resolution for dual-frequency contrast-specific acoustic angiography imaging

Recently, dual-frequency transducers have enabled high-spatial-resolution and high-contrast imaging of vasculature with minimal tissue artifacts by transmitting at a low frequency and receiving broadband superharmonic echoes scattered by microbubble contrast agents. In this work, we examine the imaging parameters for optimizing contrast-to-tissue ratio (CTR) for dual-frequency imaging and the relationship with spatial resolution. Confocal piston transducers are used in a water bath setup to measure the SNR, CTR, and axial resolution for ultrasound imaging of nonlinear scattering of microbubble contrast agents when transmitting at a lower frequency (1.5 to 8 MHz) and receiving at a higher frequency (7.5 to 25 MHz). Parameters varied include the frequency and peak negative pressure of transmitted waves, center frequency of the receiving transducer, microbubble concentration, and microbubble size. CTR is maximized at the lowest transmission frequencies but would be acceptable for imaging in the 1.5 to 3.5 MHz range. At these frequencies, CTR is optimized when a receiving transducer with a center frequency of 10 MHz is used, with the maximum CTR of 25.5 dB occurring when transmitting at 1.5 MHz with a peak negative pressure of 1600 kPa and receiving with a center frequency of 10 MHz. Axial resolution is influenced more heavily by the receiving center frequency, with a weak decrease in measured pulse lengths associated with increasing transmit frequency. A microbubble population containing predominately 4-μm-diameter bubbles yielded the greatest CTR, followed by 1- and then 2-μm bubbles. Varying concentration showed little effect over the tested parameters. CTR dependence on transmit frequency and peak pressure were confirmed through in vivo imaging in two rodents. These findings may lead to improved imaging of vascular remodeling in superficial or luminal cancers such as those of the breast, prostate, and colon.

Short-lag spatial coherence imaging on matrix arrays, part 1: Beamforming methods and simulation studies

Short-lag spatial coherence (SLSC) imaging is a beamforming technique that has demonstrated improved imaging performance compared with conventional B-mode imaging in previous studies. Thus far, the use of 1-D arrays has limited coherence measurements and SLSC imaging to a single dimension. Here, the SLSC algorithm is extended for use on 2-D matrix array transducers and applied in a simulation study examining imaging performance as a function of subaperture configuration and of incoherent channel noise. SLSC images generated with a 2-D array yielded superior contrast-to-noise ratio (CNR) and texture SNR measurements over SLSC images made on a corresponding 1-D array and over B-mode imaging. SLSC images generated with square subapertures were found to be superior to SLSC images generated with subapertures of equal surface area that spanned the whole array in one dimension. Subaperture beamforming was found to have little effect on SLSC imaging performance for subapertures up to 8 x 8 elements in size on a 64 × 64 element transducer. Additionally, the use of 8 x 8, 4 x 4, and 2 x 2 element subapertures provided 8, 4, and 2 times improvement in channel SNR along with 2640-, 328-, and 25-fold reduction in computation time, respectively. These results indicate that volumetric SLSC imaging is readily applicable to existing 2-D arrays that employ subaperture beamforming.

Ultrasound backscatter tensor imaging (BTI): analysis of the spatial coherence of ultrasonic speckle in anisotropic soft tissues

The assessment of fiber architecture is of major interest in the progression of myocardial disease. Recent techniques such as magnetic resonance diffusion tensor imaging (MR-DTI) or ultrasound elastic tensor imaging (ETI) can derive the fiber directions by measuring the anisotropy of water diffusion or tissue elasticity, but these techniques present severe limitations in a clinical setting. In this study, we propose a new technique, backscatter tensor imaging (BTI), which enables determination of the fiber directions in skeletal muscles and myocardial tissues, by measuring the spatial coherence of ultrasonic speckle. We compare the results to ultrasound ETI. Acquisitions were performed using a linear transducer array connected to an ultrasonic scanner mounted on a motorized rotation device with angles from 0° to 355° by 5° increments to image ex vivo bovine skeletal muscle and porcine left ventricular myocardial samples. At each angle, multiple plane waves were transmitted and the backscattered echoes recorded. The coherence factor was measured as the ratio of coherent intensity over incoherent intensity of backscattered echoes. In skeletal muscle, maximal/minimal coherence factor was found for the probe parallel/perpendicular to the fibers. In myocardium, the coherence was assessed across the entire myocardial thickness, and the position of maxima and minima varied transmurally because of the complex fibers distribution. In ETI, the shear wave speed variation with the probe angle was found to follow the coherence variation. Spatial coherence can thus reveal the anisotropy of the ultrasonic speckle in skeletal muscle and myocardium. BTI could be used on any type of ultrasonic scanner with rotating phased-array probes or 2-D matrix probes for noninvasive evaluation of myocardial fibers.

Reverberation clutter from subcutaneous tissue layers: simulation and in vivo demonstrations

The degradation of ultrasonic image quality is typically attributed to aberration and reverberation. Although the sources and impact of aberration are well understood, very little is known about the source and impact of image degradation caused by reverberation. Reverberation is typically associated with multiple reflections at two interfaces along the same propagation path, as with the arterial wall or a metal sphere. However, the reverberation that results in image degradation includes more complex interaction between the propagating wave and the tissue. Simulations of wave propagation in realistic and simplified models of the abdominal wall are used to illustrate the characteristics of coherent and diffuse clutter generated by reverberation. In the realistic models, diffuse reverberation clutter is divided into that originating from the tissue interfaces and that originating from sub-resolution diffuse scatterers. In the simplified models, the magnitude of the reverberation clutter is observed as angle and density of the connective tissue are altered. The results suggest that multi-path scattering from the connective tissue/fat interfaces is a dominant component of reverberation clutter. Diffuse reverberation clutter is maximal when the connective tissue is near normal to the beam direction and increases with the density of connective tissue layers at these large angles. The presence of a thick fascial or fibrous layer at the distal boundary of the abdominal wall magnifies the amount of reverberation clutter. The simulations also illustrate that compression of the abdominal layer, a technique often used to mitigate clutter in overweight and obese patients, increases the decay of reverberation clutter with depth. In addition, rotation of the transducer or steering of the beam with respect to highly reflecting boundaries can reduce coherent clutter and transform it to diffuse clutter, which can be further reduced using coherence-based beamforming techniques. In vivo images of the human bladder illustrate some of the reverberation effects observed in simulation.

Short-lag spatial coherence imaging of cardiac ultrasound data: initial clinical results

Short-lag spatial coherence (SLSC) imaging is a novel beamforming technique that reduces acoustic clutter in ultrasound images. A clinical study was conducted to investigate clutter reduction and endocardial border detection in cardiac SLSC images. Individual channel echo data were acquired from the left ventricle of 14 volunteers, after informed consent and institutional review board approval. Paired B-mode and SLSC images were created from these data. Contrast, contrast-to-noise, and signal-to-noise ratios were measured in paired images, and these metrics were improved with SLSC imaging in most cases. Three cardiology fellows rated the visibility of endocardial segments in randomly ordered B-mode and SLSC cine loops. SLSC imaging offered 22%-33% improvement (p < 0.05) in endocardial border visibility when B-mode image quality was poor (i.e., 80% or more of the endocardial segments could not be visualized by the three reviewers). The percentage of volunteers with poor-quality images was decreased from 21% to 7% with the SLSC beamformer. Results suggest that SLSC imaging has the potential to improve clinical cardiac assessments that are challenged by clutter.

Synthetic aperture focusing for short-lag spatial coherence imaging

It has been demonstrated that short-lag spatial coherence (SLSC) ultrasound imaging can provide improved speckle SNR and lesion CNR compared with conventional Bmode images, especially in the presence of noise and clutter. Application of the van Cittert-Zernike theorem predicts that coherence among the ultrasound echoes received across an array is reduced significantly away from the transmit focal depth, leading to a limited axial depth of field in SLSC images. Transmit focus throughout the field of view can be achieved using synthetic aperture methods to combine multiple transmit events into a single final image. A synthetic aperture can be formed with either focused or diverging transmit beams. We explore the application of these methods to form synthetically focused channel data to create SLSC images with an extended axial depth of field. An analytical expression of SLSC image brightness through depth is derived for the dynamic receive focus case. Experimental results in a phantom and in vivo are presented and compared with dynamic receive focused SLSC images, demonstrating improved SNR and CNR away from the transmit focus and an axial depth of field four to five times longer.

Effects of dual apodization with cross-correlation on tissue harmonic and pulse inversion harmonic imaging in the presence of phase aberration

Dual apodization with cross-correlation (DAX) is a relatively new beamforming technique which can suppress side lobes and clutter to enhance ultrasound image contrast. However, previous studies have shown that with increasing aberrator strength, contrast enhancements with DAX diminish and DAX becomes more prone to image artifacts. In this paper, we propose integrating DAX with tissue harmonic imaging (THI) or pulse inversion harmonic imaging (PIHI) to overcome their shortcomings and achieve higher image contrast. Compared with conventional imaging, our experimental results showed that DAX with THI allows for synergistic enhancements of image contrast with improvements of more than 231% for a 5-mm pork aberrator and 703% for a 12-mm pork aberrator. With PIHI, improvements of 238% and 890% were observed for the two pork tissue samples. Our results suggest that the complementary contrast enhancement mechanism employed by the proposed method may be useful in improving imaging of technically difficult patients in clinics.

In vivo application of short-lag spatial coherence imaging in human liver

We present the results of a patient study conducted to assess the performance of two novel imaging methods, namely short-lag spatial coherence (SLSC) and harmonic spatial coherence imaging (HSCI), in an in vivo liver environment. Similar in appearance to the B-mode images, SLSC and HSCI images are based solely on the spatial coherence of fundamental and harmonic echo data, respectively, and do not depend on the echo magnitude. SLSC and HSCI suppress incoherent echo signals and thus tend to reduce clutter. The SLSC and HSCI images of 17 patients demonstrated sharper delineation of blood vessel walls, suppressed clutter inside the vessel lumen, and showed reduced speckle in surrounding tissue compared to matched B-modes. Target contrast and contrast-to-noise ratio (CNR) show statistically significant improvements between fundamental B-mode and SLSC imaging and between harmonic B-mode and HSCI imaging (in all cases p < 0.001). The magnitude of improvement in contrast and CNR increases as the overall quality of B-mode images decreases. Poor-quality fundamental B-mode images (where image quality classification is based on both contrast and CNR) exhibit the highest improvements in both contrast and CNR (288% improvement in contrast and 533% improvement in CNR).