Short-lag spatial coherence of backscattered echoes: imaging characteristics

Comparative Assessment of Real-Time and Offline Short-Lag Spatial Coherence Imaging of Ultrasound Breast Masses

OBJECTIVE

To perform the first known investigation of differences between real-time and offline B-mode and short-lag spatial coherence (SLSC) images when evaluating fluid or solid content in 60 hypoechoic breast masses.

METHODS

Real-time and retrospective (i.e., offline) reader studies were conducted with three board-certified breast radiologists, followed by objective, reader-independent discrimination using generalized contrast-to-noise ratio (gCNR).

RESULTS

The content of 12 fluid, solid and mixed (i.e., containing fluid and solid components) masses were uncertain when reading real-time B-mode images. With real-time and offline SLSC images, 15 and 5, respectively, aggregated solid and mixed masses (and no fluid masses) were uncertain. Therefore, with real-time SLSC imaging, uncertainty about solid masses increased relative to offline SLSC imaging, while uncertainty about fluid masses decreased relative to real-time B-mode imaging. When assessing real-time SLSC reader results, 100% (11/11) of solid masses with uncertain content were correctly classified with a gCNR<0.73 threshold applied to real-time SLSC images. The areas under receiver operator characteristic curves characterizing gCNR as an objective metric to discriminate complicated cysts from solid masses were 0.963 and 0.998 with real-time and offline SLSC images, respectively, which are both considered excellent for diagnostic testing.

CONCLUSION

Results are promising to support real-time SLSC imaging and gCNR application to real-time SLSC images to enhance sensitivity and specificity, reduce reader variability, and mitigate uncertainty about fluid or solid content, particularly when distinguishing complicated cysts (which are benign) from hypoechoic solid masses (which could be cancerous).

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.

Overfit detection method for deep neural networks trained to beamform ultrasound images

Deep neural networks (DNNs) have remarkable potential to reconstruct ultrasound images. However, this promise can suffer from overfitting to training data, which is typically detected via loss function monitoring during an otherwise time-consuming training process or via access to new sources of test data. We present a method to detect overfitting with associated evaluation approaches that only require knowledge of a network architecture and associated trained weights. Three types of artificial DNN inputs (i.e., zeros, ones, and Gaussian noise), unseen during DNN training, were input to three DNNs designed for ultrasound image formation, trained on multi-site data, and submitted to the Challenge on Ultrasound Beamforming with Deep Learning (CUBDL). Overfitting was detected using these artificial DNN inputs. Qualitative and quantitative comparisons of DNN-created images to ground truth images immediately revealed signs of overfitting (e.g., zeros input produced mean output values ≥0.08, ones input produced mean output values ≤0.07, with corresponding image-to-image normalized correlations ≤0.8). The proposed approach is promising to detect overfitting without requiring lengthy network retraining or the curation of additional test data. Potential applications include sanity checks during federated learning, as well as optimization, security, public policy, regulation creation, and benchmarking.

Ultrafast Coherence-Based Power Doppler Estimation Using Nonlinear Compounding With Complementary Subset Transmit

OBJECTIVE

Conventional coherent plane wave compounding (CPWC) and sum-of-square power Doppler (PD) estimation lead to low contrast and high noise level in ultrafast PD imaging when the number of plane-wave angle and the ensemble length is limited. The coherence-based PD estimation using temporal-multiply-and-sum (TMAS) of high-lag autocorrelation can effectively suppress the uncorrelated noises but at the cost of signal power due to the blood flow decorrelation.

METHODS

In this study, the TMAS PD estimation is incorporated with complementary subset transmit in nonlinear compounding (DMAS-CST) to leverage the signal coherence in both angular and temporal dimensions for improvement of PD image quality. The CST correlation can be performed not only within the same Doppler ensemble (i.e., intra-correlation) but also across the adjacent Doppler ensembles (i.e., inter-correlation) to increase the number of correlation pairs in TMAS PD estimation.

RESULTS

In both simulations and experiments, DMAS-CST is capable of improving the contrast of TMAS PD image by over 10 dB compared to the nonlinear compounding alone by enhanced noise suppression and lower flow decorrelation. When the CST correlations are performed both intra and inter Doppler ensembles, the noise level further reduces in DMAS-CST.

CONCLUSION

Since the TMAS PD estimation is often limited by the loss of signal power due to temporal decorrelation, the design of complementary subsets in DMAS-CST should be carefully examined to preserve the blood flow signal. Future work of this study will focus on how to combine the conventional PD and the TMAS PD for better signal preservation and effective noise suppression.

A beamforming algorithm with composite multi-conditional cross correlation and range standard deviation factor for high quality ultrasound imaging

The traditional delay-and-sum (DAS) beamformer is inadequate for clutter suppression in ultrasound imaging. The coherence factor beamformer can effectively suppress clutter, but it is prone to cause dark-region artifacts during incoherent signal suppression. In this paper, the resolution and contrast of ultrasound images are improved by using a novel adaptive beamforming algorithm called composite multi-conditional cross correlation (MCC) and range standard deviation factor (CMCC-RSF). The MCC algorithm is obtained by integrating the conditional coherence and cross correlation factor, which can better balance clutter suppression ability inside the anechoic cyst and speckle background quality. Then, we propose a range standard deviation factor (RSF) to improve the resolution of MCC without destroying the speckle background. The simulation and experiment results show that compared with traditional DAS, the full-width at half-maximum of CMCC-RSF is improved by 75.14%, 47.62%, 49.27%, and 55.97%, respectively. According to the experiment results, the contrast ratio and speckle signal-to-noise ratio of CMCC-RSF are maximally improved by 154.38%, and 124.53%, respectively. In general, the proposed CMCC-RSF algorithm can improve comprehensive image quality with low relative computational complexity.

Ultrasound imaging with flexible transducers based on real-time and high-accuracy shape estimation

Ultrasound imaging with flexible transducers requires the knowledge of shape geometry for effective beamforming, which such geometry is variable and often unknown. The conventional iteration-based shape estimation methods estimate transducer shape with high computational expense. Although deep-learning-based methods are introduced to reduce computation time, their low shape estimation accuracy limits the practical applications. In this paper, we propose a novel deep-learning-based approach, called FlexSANet, for shape estimation in ultrasound imaging with flexible transducers, which rapidly achieves precise shape estimation and then reconstructs high-quality images. First, in-phase/quadrature (I/Q) data are demodulated from raw radio frequency (RF) data to provide comprehensive guidance for the estimation task. A sparse processing mechanism is employed to extract crucial channel signals, resulting in sparse I/Q data and reducing the estimation time. Then, a spatial-aware shape estimation network establishes a one-shot mapping between the sparse I/Q data and the flexible probe shape. Finally, the ultrasound image is reconstructed using the delay-and-sum (DAS) beamformer with estimated shape. Massive comparisons on simulation datasets and in vivo datasets demonstrate the superiority of the proposed shape estimation method in rapidly and accurately estimating the transducer shape, leading to real-time and high-quality imaging. The mean absolute error of element position in shape estimation is below 1/8 wavelengths for simulation and in vivo experiments, indicating minimal element position error. The structural similarity between the ultrasound images reconstructed with real and estimated shapes is above 0.84 for simulation experiments and 0.80 for in vivo experiments, demonstrating superior image quality. More significantly, its estimation time on CPU of only 0.12 s promises clinical application potential of flexible ultrasound transducers.

Classification and risk assessment of ovarian-adnexal lesions using parametric and radiomic analysis of co-registered ultrasound-photoacoustic tomographic images

Ovarian-adnexal lesions are conventionally assessed with ultrasound (US) under the guidance of the Ovarian-Adnexal Reporting and Data System (O-RADS). However, the low specificity of O-RADS results in many unnecessary surgeries. Here, we use co-registered US and photoacoustic tomography (PAT) to improve the diagnostic accuracy of O-RADS. Physics-based parametric algorithms for US and PAT were developed to estimate the acoustic and photoacoustic properties of 93 ovarian lesions. Additionally, statistics-based radiomic algorithms were applied to quantify differences in the lesion texture on US-PAT images. A machine learning model (US-PAT KNN model) was developed based on an optimized subset of eight US and PAT imaging features to classify a lesion as either cancer, one of four subtypes of benign lesions, or a normal ovary. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.969 and a balanced six-class classification accuracy of 86.0 %.

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.

A Novel Approach to Harnessing Acoustic A-Lines to Detect Circulating Tumor Cells in Flowing Blood

Circulating tumor cells (CTCs) are associated with tumor burden and treatment response and, as hallmarks of the initiation of tumor dissemination, can predict the likelihood of metastatic progression before widespread tumors can be detected by standard anatomic imaging. However, early diagnosis of recurrence through the detection of CTCs is limited by their low prevalence in blood and the limited sensitivity of existing technologies. To address these challenges, we investigated the use of ultrasound and targeted microbubbles (MBs) for early CTC detection. While MBs have been used in cardiovascular/molecular tumor imaging, there is limited research on their acoustic properties when bound to CTCs. We developed a hydrophone system for detecting characteristic A-lines from CTCs encapsulating MBs. Our study is the first to identify distinctive characteristics in the acoustic frequency response of MBs bound to different cancer CTCs using in vitro suspensions and in vivo mice that will benefit metastatic cancer detection and management.

Deep tissue photoacoustic imaging with light and sound

Photoacoustic computed tomography (PACT) can harvest diffusive photons to image the optical absorption contrast of molecules in a scattering medium, with ultrasonically-defined spatial resolution. PACT has been extensively used in preclinical research for imaging functional and molecular information in various animal models, with recent clinical translations. In this review, we aim to highlight the recent technical breakthroughs in PACT and the emerging preclinical and clinical applications in deep tissue imaging.

Non-iterative model-based inversion for low channel-count optical ultrasound imaginga)

Ultrasound image reconstruction is typically performed using the computationally efficient delay-and-sum algorithm. However, this algorithm is suboptimal for systems of low channel counts, where it causes significant image artefacts. These artefacts can be suppressed through model-based inversion approaches; however, their computational costs typically prohibit real-time implementations. In this work, the emerging optical ultrasound (OpUS) modality is considered, where ultrasound waves are both generated and detected using light. With this modality, imaging probes comprise very low channel counts, resulting in significant image artefacts that limit the imaging dynamic range. However, this low channel counts offer an opportunity for non-iterative ("direct") model-based inversion (DMI) on modest computational resources available in a typical workstation. When applied to both synthetic and experimental OpUS data, the presented DMI method achieved substantial reduction in image artefacts and noise, improved recovery of image amplitudes, and-after one-off pre-computation of the system matrices-significantly reduced reconstruction time, even in imaging scenarios exhibiting mild spatial inhomogeneity. Whilst here applied to an OpUS imaging system, DMI can be applied to other low channel-count imaging systems, and is therefore expected to achieve better image quality, reduce system complexity, or both, in a wide range of settings.

Micron-scale imaging using bulk ultrasonics

An extraordinary resolution down to 50 microns is demonstrated for the first time for bulk ultrasonics, using novel micro-fabricated metamaterial lenses. The development and performance of the silicon-based Fabry-Perot type metalenses with an array of 10 micrometre square holes are discussed. Challenges in wave reception are addressed by a custom-developed micro-focal laser with a sub-micron spot size and an innovative experimental set-up together with physics based signal processing. The results provide a pathway for material diagnostics at greater depths with high resolution using micro-metalens-enhanced ultrasound as an alternative to expensive and radiation prone electromagnetic techniques.

Curved Toroidal Row Column Addressed Transducer for 3D Ultrafast Ultrasound Imaging

3D Imaging of the human heart at high frame rate is of major interest for various clinical applications. Electronic complexity and cost has prevented the dissemination of 3D ultrafast imaging into the clinic. Row column addressed (RCA) transducers provide volumetric imaging at ultrafast frame rate by using a low electronic channel count, but current models are ill-suited for transthoracic cardiac imaging due to field-of-view limitations. In this study, we proposed a mechanically curved RCA with an aperture adapted for transthoracic cardiac imaging ( 24×16 mm2). The RCA has a toroidal curved surface of 96 elements along columns (curvature radius rC = 4.47 cm) and 64 elements along rows (curvature radius rR = 3 cm). We implemented delay and sum beamforming with an analytical calculation of the propagation of a toroidal wave which was validated using simulations (Field II). The imaging performance was evaluated on a calibrated phantom. Experimental 3D imaging was achieved up to 12 cm deep with a total angular aperture of 30° for both lateral dimensions. The Contrast-to-Noise ratio increased by 12 dB from 2 to 128 virtual sources. Then, 3D Ultrasound Localization Microscopy (ULM) was characterized in a sub-wavelength tube diameter. Finally, 3D ULM was demonstrated on a perfused ex-vivo swine heart to image the coronary microcirculation.

In Vivo Cavitation-Based Aberration Correction of Histotripsy in Porcine Liver

Histotripsy is a noninvasive ablation technique that focuses ultrasound pulses into the body to destroy tissues via cavitation. Heterogeneous acoustic paths through tissue introduce phase errors that distort and weaken the focus, requiring additional power output from the histotripsy transducer to perform therapy. This effect, termed phase aberration, limits the safety and efficacy of histotripsy ablation. It has been shown in vitro that the phase errors from aberration can be corrected by receiving the acoustic signals emitted by cavitation. For transabdominal histotripsy in vivo, however, cavitation-based aberration correction (AC) is complicated by acoustic signal clutter and respiratory motion. This study develops a method that enables robust, effective cavitation-based AC in vivo and evaluates its efficacy in the swine liver. The method begins with a high-speed pulsing procedure to minimize the effects of respiratory motion. Then, an optimal phase correction is obtained in the presence of acoustic clutter by filtering with the singular value decomposition (SVD). This AC method reduced the power required to generate cavitation in the liver by 26% on average (range: 0%-52%) and required ~2 s for signal acquisition and processing per focus location. These results suggest that the cavitation-based method could enable fast and effective AC for transabdominal histotripsy.

Evaluation of 10 current image reconstruction algorithms for linear array photoacoustic imaging

Various reconstruction algorithms have been implemented for linear array photoacoustic imaging systems with the goal of accurately reconstructing the strength absorbers within the tissue being imaged. Since the existing algorithms have been introduced by different research groups and the context of performance evaluation was not consistent, it is difficult to make a fair comparison between them. In this study, we systematically compared the performance of 10 published image reconstruction algorithms (DAS, UBP, pDAS, DMAS, MV, EIGMV, SLSC, GSC, TR, and FD) using in-vitro phantom data. Evaluations were conducted based on lateral resolution of the reconstructed images, computational time, target detectability, and noise sensitivity. We anticipate the outcome of this study will assist researchers in selecting appropriate algorithms for their linear array PA imaging applications.

Improvement in Multi-Angle Plane Wave Image Quality Using Minimum Variance Beamforming with Adaptive Signal Coherence

For ultrasound multi-angle plane wave (PW) imaging, the coherent PW compounding (CPWC) method provides limited image quality because of its conventional delay-and-sum beamforming. The delay-multiply-and-sum (DMAS) method is a coherence-based algorithm that improves image quality by introducing signal coherence among either receiving channels or PW transmit angles into the image output. The degree of signal coherence in DMAS is conventionally a global value for the entire image and thus the image resolution and contrast in the target region improves at the cost of speckle quality in the background region. In this study, the adaptive DMAS (ADMAS) is proposed such that the degree of signal coherence relies on the local characteristics of the image region to maintain the background speckle quality and the corresponding contrast-to-noise ratio (CNR). Subsequently, the ADMAS algorithm is further combined with minimum variance (MV) beamforming to increase the image resolution. The optimal MV estimation is determined to be in the direction of the PW transmit angle (Tx) for multi-angle PW imaging. Our results show that, using the PICMUS dataset, TxMV-ADMAS beamforming significantly improves the image quality compared with CPWC. When the p value is globally fixed to 2 as in conventional DMAS, though the main-lobe width and the image contrast in the experiments improve from 0.57 mm and 27.0 dB in CPWC, respectively, to 0.24 mm and 38.0 dB, the corresponding CNR decreases from 12.8 to 11.3 due to the degraded speckle quality. With the proposed ADMAS algorithm, however, the adaptive p value in DMAS beamforming helps to restore the CNR value to the same level of CPWC while the improvement in image resolution and contrast remains evident.

Shape estimation of flexible ultrasound arrays using spatial coherence: A preliminary study

A flexible ultrasound array can potentially provide a larger field-of-view, enhanced imaging resolution, and less operator dependency compared to conventional rigid transducer arrays. However, such transducer arrays require information about relative element positions for beamforming and reconstructing geometrically accurate sonograms. In this study, we assess the potential utility of using spatial coherence of backscattered radiofrequency data to estimate transducer array shape (inverse problem). The methodology is evaluated through 1) simulation of flexible arrays and 2) blinded in vivo experiments using commercial rigid transducer arrays on various anatomical targets (shoulder, forearm, scapular, posterior calf muscles, and abdomen) and multi-purpose ultrasound phantoms. The average Euclidean error of shape estimation is below 0.1 wavelengths for simulated arrays and below 1.4 wavelengths (median: 0.58 wavelengths) for real arrays. The complex wavelet structural similarity index between the B-mode images reconstructed with estimated and ground truth array shapes is above 99 % and 96 %, for simulations and experiments, respectively. These findings suggest that optimizing for spatial coherence may be an effective way to estimate the unknown shape of conformal ultrasound arrays.

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.

Improvement of LED-based photoacoustic imaging using lag-coherence factor (LCF) beamforming

BACKGROUND

Owing to its portability, affordability, and energy-efficiency, LED-based photoacoustic (PA) imaging is increasingly becoming popular when compared to its laser-based alternative, mainly for superficial vascular imaging applications. However, this technique suffers from low SNR and thereby limited imaging depth. As a result, visual image quality of LED-based PA imaging is not optimal, especially in sub-surface vascular imaging applications.

PURPOSE

Combination of linear ultrasound (US) probes and LED arrays are the most common implementation in LED-based PA imaging, which is currently being explored for different clinical imaging applications. Traditional delay-and-sum (DAS) is the most common beamforming algorithm in linear array-based PA detection. Side-lobes and reconstruction-related artifacts make the DAS performance unsatisfactory and poor for a clinical-implementation. In this work, we explored a new weighting-based image processing technique for LED-based PAs to yield improved image quality when compared to the traditional methods.

METHODS

We are proposing a lag-coherence factor (LCF), which is fundamentally based on the combination of the spatial auto-correlation of the detected PA signals. In LCF, the numerator contains lag-delay-multiply-and-sum (DMAS) beamformer instead of a conventional DAS beamformer. A spatial auto-correlation operation is performed between the detected US array signals before using DMAS beamformer. We evaluated the new method on both tissue-mimicking phantom (2D) and human volunteer imaging (3D) data acquired using a commercial LED-based PA imaging system.

RESULTS

Our novel correlation-based weighting technique showed LED-based PA image quality improvement when it is combined with conventional DAS beamformer. Both phantom and human volunteer imaging results gave a direct confirmation that by introducing LCF, image quality was improved and this method could reduce side-lobes and artifacts when compared to the DAS and coherence-factor (CF) approaches. Signal-to-noise ratio, generalized contrast-to-noise ratio, contrast ratio and spatial resolution were evaluated and compared with conventional beamformers to assess the reconstruction performance in a quantitative way. Results show that our approach offered image quality enhancement with an average signal-to-noise ratio and spatial resolution improvement of around 20% and 25% respectively, when compared with conventional CF based DAS algorithm.

CONCLUSIONS

Our results demonstrate that the proposed LCF based algorithm performs better than the conventional DAS and CF algorithms by improving signal-to-noise ratio and spatial resolution. Therefore, our new weighting technique could be a promising tool to improve the performance of LED-based PA imaging and thus accelerate its clinical translation.

A Very Large Cardiac Channel Data Database (VLCD) Used to Evaluate Global Image Coherence (GIC) as an In Vivo Image Quality Metric

Ultrasound image quality is of utmost importance for a clinician to reach a correct diagnosis. Conventionally, image quality is evaluated using metrics to determine the contrast and resolution. These metrics require localization of specific regions and targets in the image such as a region of interest (ROI), a background region, and/or a point scatterer. Such objects can all be difficult to identify in in-vivo images, especially for automatic evaluation of image quality in large amounts of data. Using a matrix array probe, we have recorded a Very Large cardiac Channel data Database (VLCD) to evaluate coherence as an in vivo image quality metric. The VLCD consists of 33280 individual image frames from 538 recordings of 106 patients. We also introduce a global image coherence (GIC), an in vivo image quality metric that does not require any identified ROI since it is defined as an average coherence value calculated from all the data pixels used to form the image, below a preselected range. The GIC is shown to be a quantitative metric for in vivo image quality when applied to the VLCD. We demonstrate, on a subset of the dataset, that the GIC correlates well with the conventional metrics contrast ratio (CR) and the generalized contrast-to-noise ratio (gCNR) with R = 0.74 ( ) and R = 0.62 ( ), respectively. There exist multiple methods to estimate the coherence of the received signal across the ultrasound array. We further show that all coherence measures investigated in this study are highly correlated ( 0.9 and ) when applied to the VLCD. Thus, even though there are differences in the implementation of coherence measures, all quantify the similarity of the signal across the array and can be averaged into a GIC to evaluate image quality automatically and quantitatively.

Mitigating skin tone bias in linear array in vivo photoacoustic imaging with short-lag spatial coherence beamforming

Photoacoustic (PA) imaging has the potential to deliver non-invasive diagnostic information. However, skin tone differences bias PA target visualization, as the elevated optical absorption of melanated skin decreases optical fluence within the imaging plane and increases the presence of acoustic clutter. This paper demonstrates that short-lag spatial coherence (SLSC) beamforming mitigates this bias. PA data from the forearm of 18 volunteers were acquired with 750-, 810-, and 870-nm wavelengths. Skin tones ranging from light to dark were objectively quantified using the individual typology angle (ITA° ). The signal-to-noise ratio (SNR) of the radial artery (RA) and surrounding clutter were measured. Clutter was minimal (e.g., -16 dB relative to the RA) with lighter skin tones and increased to -8 dB with darker tones, which compromised RA visualization in conventional PA images. SLSC beamforming achieved a median SNR improvement of 3.8 dB, resulting in better RA visualization for all skin tones.

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.

Improved Ultrafast Power Doppler Imaging Using United Spatial-Angular Adaptive Scaling Wiener Postfilter

Ultrafast power Doppler imaging (uPDI) using high-frame-rate plane-wave transmission is a new microvascular imaging modality that offers high Doppler sensitivity. However, due to the unfocused transmission of plane waves, the echo signal is subject to interference from noise and clutter, resulting in a low signal-to-noise ratio (SNR) and poor image quality. Adaptive beamforming techniques are effective in suppressing noise and clutter for improved image quality. In this study, an adaptive beamformer based on a united spatial-angular adaptive scaling Wiener (uSA-ASW) postfilter is proposed to improve the resolution and contrast of uPDI. In the proposed method, the signal power and noise power of the Wiener postfilter are estimated by uniting spatial and angular signals, and a united generalized coherence factor (uGCF) is introduced to dynamically adjust the noise power estimation and enhance the robustness of the method. Simulation and in vivo data were used to verify the effectiveness of the proposed method. The results show that the uSA-ASW can achieve higher resolution and significant improvements in image contrast and background noise suppression compared with conventional delay-and-sum (DAS), coherence factor (CF), spatial-angular CF (SACF), and adaptive scaling Wiener (ASW) postfilter methods. In the simulations, uSA-ASW improves contrast-to-noise ratio (CNR) by 34.7 dB (117.3%) compared with DAS, while reducing background noise power (BNP) by 52 dB (221.4%). The uSA-ASW method provides full-width at half-maximum (FWHM) reductions of [Formula: see text] (59.5%) and [Formula: see text] (56.9%), CNR improvements of 25.6 dB (199.9%) and 42 dB (253%), and BNP reductions of 46.1 dB (319.3%) and 12.9 dB (289.1%) over DAS in the experiments of contrast-free human neonatal brain and contrast-free human liver, respectively. In the contrast-free experiments, uSA-ASW effectively balances the performance of noise and clutter suppression and enhanced microvascular visualization. Overall, the proposed method has the potential to become a reliable microvascular imaging technique for aiding in more accurate diagnosis and detection of vascular-related diseases in clinical contexts.

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 lateral resolution and contrast by combining coherent plane-wave compounding with adaptive weighting for medical ultrasound imaging

Due to the severe lateral lobe artifact by coherent plane-wave compounding (CPWC) and the low signal-to-noise ratio of radiofrequency (RF) data collected from the plane wave, the adaptive beamforming methods based on focused wave imaging (FWI) are improper to be directly applied to CPWC. To obtain a high-quality image with high resolution and contrast, this study combined the threshold phase coherence factor (THR-PCF) with the reconstructed covariance matrix minimum variance (RCM-MV) and then proposed a novel CPWC-based adaptive beamforming algorithm, THR-PCF + RCM-MV. The simulation, phantom, and in-vivo experiments were performed to investigate the performance of the proposed methods in comparison with the CPWC and the classical adaptive methods including the minimum variance (MV), generalized coherence factor (GCF) and their combination GCF + MV. The simulation results demonstrated that the THR-PCF + RCM-MV beamformer improved contrast ratio (CR) by 28.14%, contrast noise ratio (CNR) by 22.01%, speckle signal-to-noise ratio (s_SNR) by 23.58%, generalized contrast-to-noise ratio (GCNR) by 0.3%, and the full width at half maximum (FWHM) by 43.38% on average, compared with the GCF + MV method. The phantom experimental results showed a better performance of the THR-PCF + RCM-MV beamformer with an average improvement by 21.95% in CR, 2.62% in s_SNR, and 48.64% in FWHM compared with the GCF + MV. Meanwhile, the results showed that the image quality of the near and far fields was enhanced by the THR-PCF + RCM-MV. The in-vivo imaging results showed that our new method had potential for clinical application. In conclusion, the lateral resolution and contrast of medical ultrasound imaging could be improved greatly with our proposed method.

Low complexity adaptive ultrasound image beamformer combined with improved multiphase apodization with cross-correlation

In this paper, an ultrasound imaging method combined with low-complexity adaptive beamformer (LCA) and improved multiphase apodization with cross-correlation (IMPAX) is proposed to improve image resolution and contrast with low hardware cost. Firstly, the delayed echo signal is apodized by the LCA to obtain a narrow mainlobe width echo signal and LCA output. Then, multiple pairs of complementary square-wave phase apodizations are applied to the apodized echo signal to obtain corresponding signal pairs, which are used to calculate the normalized cross-correlation (NCC) matrix. Finally, the average value of the NCC matrices is filtered by 2-D means, and the filtered result is introduced as the weighting factor for the LCA output. The simulation and experimental results show that the proposed LCA-IMPAX can effectively reduce the mainlobe width, suppress clutter, and be robust to noise. Compared with DAS, LCA, and MPAX, for simulated point targets, the full-width at half-maximum (FWHM, -6dB) of LCA-IMPAX is reduced by 49.22%, 10.06%, and 48.67%, respectively. For simulated cyst, the CR is improved by 219.91%, 138.08%, and 103.44%, respectively. For experimental cysts, the CR is improved by an average of 145.00%, 136.14%, and 55.09%, respectively. The results of human heart data indicate that LCA-IMPAX has good imaging quality in vivo. Since the proposed method does not involve covariance matrix inversion, it can be applied in real-time imaging systems.

Enhancing sizing accuracy in ultrasound images with an alternative ADMIRE model and dynamic range considerations

Ultrasound imaging can struggle with sizing accuracy, especially when the targets have a significantly different amplitude compared to the surrounding background. In this work, we consider the challenging task of accurately sizing hyperechoic structures, and specifically kidney stones, where accurate sizing is critical for determining medical intervention. AD-Ex, an extended alternative model of our aperture domain model image reconstruction (ADMIRE) pre-processing method, is introduced and is designed to improve clutter removal and improve sizing accuracy. We compare this method against other resolution enhancing methods such as minimum variance (MV) and generalized coherence factor (GCF), and against those methods using AD-Ex as a pre-processing tool. These methods are evaluated among patients with kidney stone disease, with the task of accurately sizing the stones against the gold standard, computed tomography (CT). Stone ROI's were selected using contour maps as reference from which the lateral stone size was estimated. Among the in vivo kidney stone cases we processed, AD-Ex+MV had the overall lowest sizing error among the methods, with an average error of 10.8% compared to the next best method AD-Ex which had an average error of 23.4%. For reference, DAS had an average error of 82.4%. Though dynamic range was evaluated to determine optimal thresholding for sizing applications, variability between stone cases was too high for any conclusions to be drawn at this time.

High-Quality Ultrafast Power Doppler Imaging Based on Spatial Angular Coherence Factor

The morphological and hemodynamic changes of microvessels are demonstrated to be related to the diseased conditions in tissues. Ultrafast power Doppler imaging (uPDI) is a novel modality with a significantly increased Doppler sensitivity, benefiting from the ultrahigh frame rate plane-wave imaging (PWI) and advanced clutter filtering. However, unfocused plane-wave transmission often leads to a low imaging quality, which degrades the subsequent microvascular visualization in power Doppler imaging. Coherence factor (CF)-based adaptive beamformers have been widely studied in conventional B-mode imaging. In this study, we propose a spatial and angular coherence factor (SACF) beamformer for improved uPDI (SACF-uPDI) by calculating the spatial CF across apertures and the angular CF across transmit angles, respectively. To identify the superiority of SACF-uPDI, simulations, in vivo contrast-enhanced rat kidney, and in vivo contrast-free human neonatal brain studies were conducted. Results demonstrate that SACF-uPDI can effectively enhance contrast and resolution and suppress background noise simultaneously, compared with conventional uPDI methods based on delay-and-sum (DAS) (DAS-uPDI) and CF (CF-uPDI). In the simulations, SACF-uPDI can improve the lateral and axial resolutions compared with those of DAS-uPDI, from 176 to [Formula: see text] of lateral resolution, and from 111 to [Formula: see text] of axial resolution. In the in vivo contrast-enhanced experiments, SACF achieves 15.14- and 5.6-dB higher contrast-to-noise ratio (CNR), 15.25- and 3.68-dB lower noise power, and 240- and 15- [Formula: see text] narrower full-width at half-maximum (FWHM) than DAS-uPDI and CF-uPDI, respectively. In the in vivo contrast-free experiments, SACF achieves 6.11- and 1.09-dB higher CNR, 11.93- and 4.01-dB lower noise power, and 528- and 160- [Formula: see text] narrower FWHM than DAS-uPDI and CF-uPDI, respectively. In conclusion, the proposed SACF-uPDI method can efficiently improve the microvascular imaging quality and has the potential to facilitate clinical applications.

Synthetic Aperture Ultrasound Imaging through Adaptive Integrated Transmitting-Receiving Beamformer

Synthetic aperture (SA) technique is very attractive for ultrafast ultrasound imaging, as the entire medium can be insonified by a single emission. It also permits applying the dynamic focusing as well as adaptive beamforming both in transmission and reception, which results in an enhanced image. In this paper, we firstly show that the problem of designing the transmit and receive beamformers in SA structure can be formulated as a problem of designing a one-way beamformer on a virtual array with a lateral response equal to that of the two-way beamformer on SA. It is also demonstrated that the length of the virtual aperture is increased to the sum of the transmit aperture length and the receive one, which can result in an enhanced resolution. Moreover, a better estimation of the covariance matrix can be obtained which can be utilized for applying adaptive minimum variance (MV) beamforming method on the virtual array, and consequently the resolution and contrast properties would be enhanced. The performance of the new method is compared with other existing MV-based methods and is quantified by some metrics such as the full width at half maximum (FWHM) and generalized contrast to noise ratio (GCNR). Our validations on simulations and experimental data have shown that the new method is capable of obtaining higher GCNR values while retaining or decreasing FWHM values almost all the time. Moreover, for the same subarray length for estimating the covariance matrices, the computational burden of the new method is significantly lower than those of the existing rival methods.

Costas Sparse 2-D Arrays for High-Resolution Ultrasound Imaging

Two-dimensional arrays enable volumetric ultrasound imaging but have been limited to small aperture size and hence low resolution due to the high cost and complexity of fabrication, addressing, and processing associated with large fully addressed arrays. Here, we propose Costas arrays as a gridded sparse 2-D array architecture for volumetric ultrasound imaging. Costas arrays have exactly one element for every row and column, such that the vector displacement between any pair of elements is unique. These properties ensure aperiodicity, which helps eliminate grating lobes. Compared with previously reported works, we studied the distribution of active elements based on an order-256 Costas layout on a wider aperture ( 96 λ×96 λ at 7.5 MHz center frequency) for high-resolution imaging. Our investigations with focused scanline imaging of point targets and cyst phantoms showed that Costas arrays exhibit lower peak sidelobe levels compared with random sparse arrays of the same size and offer comparable performance in terms of contrast compared with Fermat spiral arrays. In addition, Costas arrays are gridded, which could ease the manufacturing and has one element for each row/column, which enables simple interconnection strategies. Compared with state-of-the-art matrix probes, which are commonly 32×32 , the proposed sparse arrays achieve higher lateral resolution and a wider field of view.

Transcranial 3D ultrasound localization microscopy using a large element matrix array with a multi-lens diffracting layer: anin vitrostudy

Objective. Early diagnosis and acute knowledge of cerebral disease require to map the microflows of the whole brain. Recently, ultrasound localization microscopy (ULM) was applied to map and quantify blood microflows in 2D in the brain of adult patients down to the micron scale. Whole brain 3D clinical ULM remains challenging due to the transcranial energy loss which reduces significantly the imaging sensitivity.Approach. Large aperture probes with a large surface can increase both the field of view and sensitivity. However, a large active surface implies thousands of acoustic elements, which limits clinical translation. In a previous simulation study, we developed a new probe concept combining a limited number of elements and a large aperture. It is based on large elements, to increase sensitivity, and a multi-lens diffracting layer to improve the focusing quality. In this study, a 16 elements prototype, driven at 1 MHz frequency, was made andin vitroexperiments were performed to validate the imaging capabilities of this new probe concept.Main results. First, pressure fields emitted from a large single transducer element without and with diverging lens were compared. Low directivity was measured for the large element with the diverging lens while maintaining high transmit pressure. The focusing quality of 4 × 3cm matrix arrays of 16 elements without/with lenses were compared.In vitroexperiments in a water tank and through a human skull were achieved to localize and track microbubbles in tubes.Significance.ULM was achieved demonstrating the strong potential of multi-lens diffracting layer to enable microcirculation assessment over a large field of view through the bones.

A Radon diffraction theorem for plane wave ultrasound imaging

The rising demand on high frame rate ultrasound imaging applications necessitates the development of fast algorithms for plane wave image reconstruction. We introduce a new class of plane wave reconstructions that relies on a relation between receive data and image data in the Radon domain. This relation is derived for arbitrary dimensions and validated on multiple two-dimensional plane wave data sets. We further present a mathematical relation between conventional delay-and-sum and Fourier domain reconstruction methods and the method proposed. Our analysis shows that they all rely on the same physical model with slight variations in certain filtering steps and, therefore, the new Radon domain reconstruction yields similar results as other methods in terms of image quality. However, we show that our method offers a huge potential to improve computation time by reducing the number of applied projections and to improve image quality by introducing nonlinear operations in the Radon domain, e.g., for edge enhancement. As the Radon transform retains both angular and temporal information, the relation also provides new insights on the fundamentals of plane wave imaging that can be leveraged for optimizing acquisition schemes or for developing novel compounding strategies in the future.

Coherence Metrics for Reader-Independent Differentiation of Cystic From Solid Breast Masses in Ultrasound Images

Traditional breast ultrasound imaging is a low-cost, real-time and portable method to assist with breast cancer screening and diagnosis, with particular benefits for patients with dense breast tissue. We previously demonstrated that incorporating coherence-based beamforming additionally improves the distinction of fluid-filled from solid breast masses, based on qualitative image interpretation by board-certified radiologists. However, variable sensitivity (range: 0.71-1.00 when detecting fluid-filled masses) was achieved by the individual radiologist readers. Therefore, we propose two objective coherence metrics, lag-one coherence (LOC) and coherence length (CL), to quantitatively determine the content of breast masses without requiring reader assessment. Data acquired from 31 breast masses were analyzed. Ideal separation (i.e., 1.00 sensitivity and specificity) was achieved between fluid-filled and solid breast masses based on the mean or median LOC value within each mass. When separated based on mean and median CL values, the sensitivity/specificity decreased to 1.00/0.95 and 0.92/0.89, respectively. The greatest sensitivity and specificity were achieved in dense, rather than non-dense, breast tissue. These results support the introduction of an objective, reader-independent method for automated diagnoses of cystic breast masses.

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.

Ultrasound Matrix Imaging-Part I: The Focused Reflection Matrix, the F-Factor and the Role of Multiple Scattering

This is the first article in a series of two dealing with a matrix approach for aberration quantification and correction in ultrasound imaging. Advanced synthetic beamforming relies on a double focusing operation at transmission and reception on each point of the medium. Ultrasound matrix imaging (UMI) consists in decoupling the location of these transmitted and received focal spots. The response between those virtual transducers form the so-called focused reflection matrix that actually contains much more information than a confocal ultrasound image. In this paper, a time-frequency analysis of this matrix is performed, which highlights the single and multiple scattering contributions as well as the impact of aberrations in the monochromatic and broadband regimes. Interestingly, this analysis enables the measurement of the incoherent input-output point spread function at any pixel of this image. A fitting process enables the quantification of the single scattering, multiple scattering and noise components in the image. From the single scattering contribution, a focusing criterion is defined, and its evolution used to quantify the amount of aberration throughout the ultrasound image. In contrast to the state-of-the-art coherence factor, this new indicator is robust to multiple scattering and electronic noise, thereby providing a contrasted map of the focusing quality at a much better transverse resolution. After a validation of the proof-of-concept based on time-domain simulations, UMI is applied to the in-vivo study of a human calf. Beyond this specific example, UMI opens a new route for speed-of-sound and scattering quantification in ultrasound imaging.

Separation of mainlobe and sidelobe contributions to B-mode ultrasound images based on the aperture spectrum

Purpose

Isolating the mainlobe and sidelobe contribution to the ultrasound image can improve imaging contrast by removing off-axis clutter. Previous work achieves this separation of mainlobe and sidelobe contributions based on the covariance of received signals. However, the formation of a covariance matrix at each imaging point can be computationally burdensome and memory intensive for real-time applications. Our work demonstrates that the mainlobe and sidelobe contributions to the ultrasound image can be isolated based on the receive aperture spectrum, greatly reducing computational and memory requirements.

Approach

The separation of mainlobe and sidelobe contributions to the ultrasound image is shown in simulation, in vitro, and in vivo using the aperture spectrum method and multicovariate imaging of subresolution targets (MIST). Contrast, contrast-to-noise-ratio (CNR), and speckle signal-to-noise-ratio are used to compare the aperture spectrum approach with MIST and conventional delay-and-sum (DAS) beamforming.

Results

The aperture spectrum approach improves contrast by 1.9 to 6.4 dB beyond MIST and 8.9 to 13.5 dB beyond conventional DAS B-mode imaging. However, the aperture spectrum approach yields speckle texture similar to DAS. As a result, the aperture spectrum-based approach has less CNR than MIST but greater CNR than conventional DAS. The CPU implementation of the aperture spectrum-based approach is shown to reduce computation time by a factor of 9 and memory consumption by a factor of 128 for a 128-element transducer.

Conclusions

The mainlobe contribution to the ultrasound image can be isolated based on the receive aperture spectrum, which greatly reduces the computational cost and memory requirement of this approach as compared with MIST.

Ultrasound Ultrafast Power Doppler Imaging with High Signal-to-Noise Ratio by Temporal Multiply-and-Sum (TMAS) Autocorrelation

Coherent plane wave compounding (CPWC) reconstructs transmit focusing by coherently summing several low-resolution plane-wave (PW) images from different transmit angles to improve its image resolution and quality. The high frame rate of CPWC imaging enables a much larger number of Doppler ensembles such that the Doppler estimation of blood flow becomes more reliable. Due to the unfocused PW transmission, however, one major limitation of the Doppler estimation in CPWC imaging is the relatively low signal-to-noise ratio (SNR). Conventionally, the Doppler power is estimated by a zero-lag autocorrelation which reduces the noise variance, but not the noise level. A higher-lag autocorrelation method such as the first-lag (R(1)) power Doppler image has been developed to take advantage of the signal coherence in the temporal direction for suppressing uncorrelated random noises. In this paper, we propose a novel Temporal Multiply-and-Sum (TMAS) power Doppler detection method to further improve the noise suppression of the higher-lag method by modulating the signal coherence among the temporal correlation pairs in the higher-lag autocorrelation with a tunable pt value. Unlike the adaptive beamforming methods which demand for either receive-channel-domain or transmit-domain processing to exploit the spatial coherence of the blood flow signal, the proposed TMAS power Doppler can share the routine beamforming architecture with CPWC imaging. The simulated results show that when it is compared to the original R(1) counterpart, the TMAS power Doppler image with the pt value of 2.5 significantly improves the SNR by 8 dB for the cross-view flow velocity within the Nyquist rate. The TMAS power Doppler, however, suffers from the signal decorrelation of the blood flow, and thus, it relies on not only the pt value and the flow velocity, but also the flow direction relative to the geometry of acoustic beam. The experimental results in the flow phantom and in vivo dataset also agree with the simulations.

Image Quality Improvement Techniques and Assessment Adequacy in Clinical Optoacoustic Imaging: A Systematic Review

Optoacoustic imaging relies on the detection of optically induced acoustic waves to offer new possibilities in morphological and functional imaging. As the modality matures towards clinical application, research efforts aim to address multifactorial limitations that negatively impact the resulting image quality. In an endeavor to obtain a clear view on the limitations and their effects, as well as the status of this progressive refinement process, we conduct an extensive search for optoacoustic image quality improvement approaches that have been evaluated with humans in vivo, thus focusing on clinically relevant outcomes. We query six databases (PubMed, Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and Google Scholar) for articles published from 1 January 2010 to 31 October 2021, and identify 45 relevant research works through a systematic screening process. We review the identified approaches, describing their primary objectives, targeted limitations, and key technical implementation details. Moreover, considering comprehensive and objective quality assessment as an essential prerequisite for the adoption of such approaches in clinical practice, we subject 36 of the 45 papers to a further in-depth analysis of the reported quality evaluation procedures, and elicit a set of criteria with the intent to capture key evaluation aspects. Through a comparative criteria-wise rating process, we seek research efforts that exhibit excellence in quality assessment of their proposed methods, and discuss features that distinguish them from works with similar objectives. Additionally, informed by the rating results, we highlight areas with improvement potential, and extract recommendations for designing quality assessment pipelines capable of providing rich evidence.

Deep reconstruction of high-quality ultrasound images from raw plane-wave data: A simulation and in vivo study

This paper presents a novel beamforming approach based on deep learning to get closer to the ideal Point Spread Function (PSF) in Plane-Wave Imaging (PWI). The proposed approach is designed to reconstruct a high-quality version of Tissue Reflectivity Function (TRF) from echo traces acquired by transducer elements using only a single plane-wave transmission. In this approach, first, a model for the TRF is introduced by setting the imaging PSF as an isotropic (i.e., circularly symmetric) 2D Gaussian kernel convolved with a cosine function. Then, a mapping function between the pre-beamformed Radio-Frequency (RF) channel data and the proposed output is constructed using deep learning. Network architecture contains multi-resolution decomposition and reconstruction using wavelet transform for effective recovery of high-frequency content of the desired output. We exploit step by step training from coarse (mean square error) to fine (ℓ_0.2) loss functions. The proposed method is trained on 1174 simulation ultrasound data with the ground-truth echogenicity map extracted from real photographic images. The performance of the trained network is evaluated on the publicly available simulation and in vivo test data without any further fine-tuning. Simulation test results show an improvement of 37.5% and 65.8% in terms of axial and lateral resolution as compared to Delay-And-Sum (DAS) results, respectively. The contrast is also improved by 33.7% in comparison to DAS. Furthermore, the reconstructed in vivo images confirm that the trained mapping function does not need any fine-tuning in the new domain. Therefore, the proposed approach maintains high resolution, contrast, and framerate simultaneously.

Elevational Synthetic Aperture Focusing for Three-Dimensional Photoacoustic Imaging Using a Clinical One-Dimensional Array Transducer

OBJECTIVE

Two-dimensional (2D) photoacoustic (PA) imaging based on array transducers provide high spatial resolution in the lateral direction by adopting receive dynamic focusing. However, the quality of PA image is often deteriorated by poor elevational resolution which is achieved by an acoustic lens. To overcome this limitation, we present a three-dimensional (3D) image reconstruction method using a commercial one-dimensional (1D) array transducer.

METHODS

In the method, the elevational resolution is improved by applying synthetic aperture focusing (SAF) technique along the elevational direction. For this, a commercially available 1D array transducer with an acoustic lens is modeled and appropriate synthetic focusing delay that can minimize the effect of the acoustic lens is derived by mathematical analysis.

RESULTS

From the simulation and experiment results, it was demonstrated that the proposed method can enhance the image quality of PA imaging, i.e., elevational resolution and signal-to-noise ratio (SNR).

CONCLUSION

3D PA images with improved elevational resolution were achieved using a clinical 1D array transducer.

SIGNIFICANCE

The presented method may be useful for clinical application such as detecting microcalcification, imaging of tumor vasculature and guidance of biopsy in real time.

Adaptive Models for Multi-Covariate Imaging of Sub-Resolution Targets (MIST)

Multi-covariate imaging of sub-resolution targets (MIST) is a statistical, model-based image formation technique that smooths speckles and reduces clutter. MIST decomposes the measured covariance of the element signals into modeled contributions from mainlobe, sidelobes, and noise. MIST covariance models are derived from the well-known autocorrelation relationship between transmit apodization and backscatter covariance. During in vivo imaging, the effective transmit aperture often deviates from the applied apodization due to nonlinear propagation and wavefront aberration. Previously, the backscatter correlation length provided a first-order measure of these patient-specific effects. In this work, we generalize and extend this approach by developing data-adaptive covariance estimation, parameterization, and model-formation techniques. We performed MIST imaging using these adaptive models and evaluated the performance gains using 152 tissue-harmonic scans of fetal targets acquired from 15 healthy pregnant subjects. Compared to standard MIST imaging, the contrast-to-noise ratio (CNR) is improved by a median of 8.3%, and the speckle signal-to-noise ratio (SNR) is improved by a median of 9.7%. The median CNR and SNR gains over B-mode are improved from 29.4% to 40.4% and 24.7% to 38.3%, respectively. We present a versatile empirical function that can parameterize an arbitrary speckle covariance and estimate the effective coherent aperture size and higher order coherence loss. We studied the performance of the proposed methods as a function of input parameters. The implications of system-independent MIST implementation are discussed.

Investigation on improving performance of adaptive beamformer by statistical analysis of ultrasonic echoes

Minimum variance (MV) beamformers have been introduced in medical ultrasound imaging to improve image quality. In most cases, the MV beamformers have been investigated in terms of resolution improvement. However, the contrast-to-noise ratio (CNR) is also a clinically important metrics and gathers attention recently. In this study, we examined the diagonal loading parameter σ in MV beamforming and determined its appropriate value by evaluating image quality evaluation metrics including CNR. In order to further improve the image quality, a method for determining the value of σ based on the difference in statistical properties of received ultrasonic echo signals was also investigated. The phantom experimental results showed that the proposed method achieved a better CNR than the conventional MV beamformer while keeping resolution significantly better than that in delay-and-sum beamforming.

Minimum variance beamforming combined with covariance matrix-based adaptive weighting for medical ultrasound imaging

BACKGROUND

The minimum variance (MV) beamformer can significantly improve the image resolution in ultrasound imaging, but it has limited performance in noise reduction. We recently proposed the covariance matrix-based statistical beamforming (CMSB) for medical ultrasound imaging to reduce sidelobes and incoherent clutter.

METHODS

In this paper, we aim to improve the imaging performance of the MV beamformer by introducing a new pixel-based adaptive weighting approach based on CMSB, which is named as covariance matrix-based adaptive weighting (CMSAW). The proposed CMSAW estimates the mean-to-standard-deviation ratio (MSR) of a modified covariance matrix reconstructed by adaptive spatial smoothing, rotary averaging, and diagonal reducing. Moreover, adaptive diagonal reducing based on the aperture coherence is introduced in CMSAW to enhance the performance in speckle preservation.

RESULTS

The proposed CMSAW-weighted MV (CMSAW-MV) was validated through simulation, phantom experiments, and in vivo studies. The phantom experimental results show that CMSAW-MV obtains resolution improvement of 21.3% and simultaneously achieves average improvements of 96.4% and 71.8% in average contrast and generalized contrast-to-noise ratio (gCNR) for anechoic cyst, respectively, compared with MV. in vivo studies indicate that CMSAW-MV improves the noise reduction performance of MV beamformer.

CONCLUSION

Simulation, experimental, and in vivo results all show that CMSAW-MV can improve resolution and suppress sidelobes and incoherent clutter and noise. These results demonstrate the effectiveness of CMSAW in improving the imaging performance of MV beamformer. Moreover, the proposed CMSAW with a computational complexity of [Formula: see text] has the potential to be implemented in real time using the graphics processing unit.

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.

Theoretical Framework to Predict Generalized Contrast-to-Noise Ratios of Photoacoustic Images With Applications to Computer Vision

The successful integration of computer vision, robotic actuation, and photoacoustic imaging to find and follow targets of interest during surgical and interventional procedures requires accurate photoacoustic target detectability. This detectability has traditionally been assessed with image quality metrics, such as contrast, contrast-to-noise ratio, and signal-to-noise ratio (SNR). However, predicting target tracking performance expectations when using these traditional metrics is difficult due to unbounded values and sensitivity to image manipulation techniques like thresholding. The generalized contrast-to-noise ratio (gCNR) is a recently introduced alternative target detectability metric, with previous work dedicated to empirical demonstrations of applicability to photoacoustic images. In this article, we present theoretical approaches to model and predict the gCNR of photoacoustic images with an associated theoretical framework to analyze relationships between imaging system parameters and computer vision task performance. Our theoretical gCNR predictions are validated with histogram-based gCNR measurements from simulated, experimental phantom, ex vivo, and in vivo datasets. The mean absolute errors between predicted and measured gCNR values ranged from 3.2 ×10^-3 to 2.3 ×10^-2 for each dataset, with channel SNRs ranging -40 to 40 dB and laser energies ranging 0.07 [Formula: see text] to 68 mJ. Relationships among gCNR, laser energy, target and background image parameters, target segmentation, and threshold levels were also investigated. Results provide a promising foundation to enable predictions of photoacoustic gCNR and visual servoing segmentation accuracy. The efficiency of precursory surgical and interventional tasks (e.g., energy selection for photoacoustic-guided surgeries) may also be improved with the proposed framework.

Improved Ultrafast Power Doppler Imaging by Using Spatiotemporal Non-Local Means Filtering

The change of microvasculature is associated with the occurrence and development of many diseases. Ultrafast power Doppler imaging (uPDI) is an emerging technology for the visualization of microvessels due to the development of ultrafast plane wave (PW) imaging and advanced clutter filters. However, the low signal-to-noise ratio (SNR) caused by unfocused transmit of PW imaging deteriorates the subsequent imaging of microvasculature. Nonlocal means (NLM) filtering has been demonstrated to be effective in the denoising of both natural and medical images, including ultrasound power Doppler images. However, the feasibility and performance of applying an NLM filter on the ultrasound radio frequency (RF) data have not been investigated so far. In this study, we propose to apply an NLM filter on the spatiotemporal domain of clutter filtered blood flow RF data (St-NLM) to improve the quality of uPDI. Experiments were conducted to compare the proposed method with three different methods (under various similarity window sizes), including conventional uPDI without NLM filtering (Non-NLM), NLM filtering on the obtained power Doppler images (PD-NLM), and NLM filtering on the spatial domain of clutter filtered blood flow RF data (S-NLM). Phantom experiments, in vivo contrast-enhanced human spinal cord tumor experiments, and in vivo contrast-free human liver experiments were performed to demonstrate the superiority of the proposed St-NLM method over the other three methods. Qualitative and quantitative results show that the proposed St-NLM method can effectively suppress the background noise, improve the contrast between vessels and background, and preserve the details of small vessels at the same time. In the human liver study, the proposed St-NLM method achieves 31.05-, 24.49-, and 11.15-dB higher contrast-to-noise ratios (CNRs) and 36.86-, 36.86-, and 15.22-dB lower noise powers than Non-NLM, PD-NLM, and S-NLM, respectively. In the human spinal cord tumor, the full-width at half-maximums (FWHMs) of vessel cross Section are 76, 201, and [Formula: see text] for St-NLM, Non-NLM, and S-NLM, respectively. The proposed St-NLM method can enhance the microvascular visualization in uPDI and has the potential for the diagnosis of many microvessel-change-related diseases.

Spatiotemporal Coherence to Quantify Sources of Image Degradation in Ultrasonic Imaging

Thermal noise and acoustic clutter signals degrade ultrasonic image quality and contribute to unreliable clinical assessment. When both noise and clutter are prevalent, it is difficult to determine which one is a more significant contributor to image degradation because there is no way to separately measure their contributions in vivo. Efforts to improve image quality often rely on an understanding of the type of image degradation at play. To address this, we derived and validated a method to quantify the individual contributions of thermal noise and acoustic clutter to image degradation by leveraging spatial and temporal coherence characteristics. Using Field II simulations, we validated the assumptions of our method, explored strategies for robust implementation, and investigated its accuracy and dynamic range. We further proposed a novel robust approach for estimating spatial lag-one coherence. Using this robust approach, we determined that our method can estimate the signal-to-thermal noise ratio (SNR) and signal-to-clutter ratio (SCR) with high accuracy between SNR levels of -30 to 40 dB and SCR levels of -20 to 15 dB. We further explored imaging parameter requirements with our Field II simulations and determined that SNR and SCR can be estimated accurately with as few as two frames and sixteen channels. Finally, we demonstrate in vivo feasibility in brain imaging and liver imaging, showing that it is possible to overcome the constraints of in vivo motion using high-frame rate M-Mode imaging.

Refraction-Corrected Transcranial Ultrasound Imaging Through the Human Temporal Window Using a Single Probe

Transcranial ultrasound imaging (TUI) is a diagnostic modality with numerous applications, but unfortunately, it is hindered by phase aberration caused by the skull. In this article, we propose to reconstruct a transcranial B-mode image with a refraction-corrected synthetic aperture imaging (SAI) scheme. First, the compressional sound velocity of the aberrator (i.e., the skull) is estimated using the bidirectional headwave technique. The medium is described with four layers (i.e., lens, water, skull, and water), and a fast marching method calculates the travel times between individual array elements and image pixels. Finally, a delay-and-sum algorithm is used for image reconstruction with coherent compounding. The point spread function (PSF) in a wire phantom image and reconstructed with the conventional technique (using a constant sound speed throughout the medium), and the proposed method was quantified with numerical synthetic data and experiments with a bone-mimicking plate and a human skull, compared with the PSF achieved in a ground truth image of the medium without the aberrator (i.e., the bone plate or skull). A phased-array transducer (P4-1, ATL/Philips, 2.5 MHz, 96 elements, pitch = 0.295 mm) was used for the experiments. The results with the synthetic signals, the bone-mimicking plate, and the skull indicated that the proposed method reconstructs the scatterers with an average lateral/axial localization error of 0.06/0.14 mm, 0.11/0.13 mm, and 1.0/0.32 mm, respectively. With the human skull, an average contrast ratio (CR) and full-width-half-maximum (FWHM) of 37.1 dB and 1.75 mm were obtained with the proposed approach, respectively. This corresponds to an improvement of CR and FWHM by 7.1 dB and 36% compared with the conventional method, respectively. These numbers were 12.7 dB and 41% with the bone-mimicking plate.