In vivo Multi-perspective 3D + t Ultrasound Imaging and Motion Estimation of Abdominal Aortic Aneurysms.

Time-resolved three-dimensional ultrasound (3D + t US) is a promising imaging modality for monitoring abdominal aortic aneurysms (AAAs), providing their 3D geometry and motion. The lateral contrast of US is poor, a well-documented drawback which multi-perspective (MP) imaging could resolve. This study aims to show the feasibility of in vivo multi-perspective 3D + t ultrasound imaging of AAAs for improving the image contrast and displacement accuracy. To achieve this, single-perspective (SP) aortic ultrasound images from three different angles were spatiotemporally registered and fused, and the displacements were compounded. The fused MP had a significantly higher wall-lumen contrast than the SP images, for both patients and volunteers (P < .001). MP radial displacements patterns are smoother than SP patterns in 67% of volunteers and 92% of patients. The MP images from three angles have a decreased tracking error (P < .001 for all participants), and an improved SNRe compared to two out of three SP images (P < .05). This study has shown the added value of MP 3D + t US, improving both image contrast and displacement accuracy in AAA imaging. This is a step toward using multiple or large transducers in the clinic to capture the 3D geometry and strain more accurately, for patient-specific characterization of AAAs.

Contactless Cardiovascular Assessment by Imaging Photoplethysmography: A Comparison with Wearable Monitoring.

Despite the notable recent developments in the field of remote photoplethysmography (rPPG), extracting a reliable pulse rate variability (PRV) signal still remains a challenge. In this study, eight image-based photoplethysmography (iPPG) extraction methods (GRD, AGRD, PCA, ICA, LE, SPE, CHROM, and POS) were compared in terms of pulse rate (PR) and PRV features. The algorithms were made robust for motion and illumination artifacts by using ad hoc pre- and postprocessing steps. Then, they were systematically tested on the public dataset UBFC-RPPG, containing data from 42 subjects sitting in front of a webcam (30 fps) while playing a time-sensitive mathematical game. The performances of the algorithms were evaluated by statistically comparing iPPG-based and finger-PPG-based PR and PRV features in terms of Spearman's correlation coefficient, normalized root mean square error (NRMSE), and Bland-Altman analysis. The study revealed POS and CHROM techniques to be the most robust for PR estimation and the assessment of overall autonomic nervous system (ANS) dynamics by using PRV features in time and frequency domains. Furthermore, we demonstrated that a reliable characterization of the vagal tone is made possible by computing the Poincaré map of PRV series derived from the POS and CHROM methods. This study supports the use of iPPG systems as promising tools to obtain clinically useful and specific information about ANS dynamics.

Strain-based discoordination imaging during exercise in heart failure with reduced ejection fraction: Feasibility and reproducibility.

PURPOSE: Various parameters of mechanical dyssynchrony have been proposed to improve patient selection criteria for cardiac resynchronization therapy, but sensitivity and specificity are lacking. However, echocardiographic parameters are consistently investigated at rest, whereas heart failure (HF) symptoms predominately manifest during submaximal exertion. Although strain-based predictors of response are promising, feasibility and reproducibility during exercise has yet to be demonstrated. METHODS: Speckle-tracking echocardiography was performed in patients with HF at two separate visits. Echocardiography was performed at rest, during various exercise intensity levels, and during recovery from exercise. Systolic rebound stretch of the septum (SRSsept), systolic shortening, and septal discoordination index (SDI) were calculated. RESULTS: Echocardiography was feasible in about 70-80% of all examinations performed during exercise. Of these acquired views, 84% of the cine-loops were suitable for analysis of strain-based mechanical dyssynchrony. Test-retest variability and intra- and inter-operator reproducibility at 30% and 60% of the ventilatory threshold (VT) were about 2.5%. SDI improved in the majority of patients at 30% and 60% of the VT, with moderate to good agreement between both intensity levels. CONCLUSION: Although various challenges remain, exercise echocardiography with strain analysis appears to be feasible in the majority of patients with dyssynchronous heart failure. Inter- and intra-observer agreement of SRSsept and SDI up to 60% of the VT were comparable to resting values. During exercise, the extent of SDI was variable, suggesting a heterogeneous response to exercise. Further research is warranted to establish its clinical significance.

Advanced Ultrasound and Photoacoustic Imaging in Cardiology.

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide. An effective management and treatment of CVDs highly relies on accurate diagnosis of the disease. As the most common imaging technique for clinical diagnosis of the CVDs, US imaging has been intensively explored. Especially with the introduction of deep learning (DL) techniques, US imaging has advanced tremendously in recent years. Photoacoustic imaging (PAI) is one of the most promising new imaging methods in addition to the existing clinical imaging methods. It can characterize different tissue compositions based on optical absorption contrast and thus can assess the functionality of the tissue. This paper reviews some major technological developments in both US (combined with deep learning techniques) and PA imaging in the application of diagnosis of CVDs.

Ultrasound Based Wall Stress Analysis of Abdominal Aortic Aneurysms using Multiperspective Imaging.

BACKGROUND: Current clinical guidelines for surgical repair of abdominal aortic aneurysms (AAAs) are primarily based on maximum diameter assessment. From a biomechanical point of view, not only the diameter but also peak wall stresses will play an important role in rupture risk assessment. These methods require patient specific geometry which typically uses computed tomography (CT) or magnetic resonance imaging. Recently, wall stress analysis based on 3D ultrasound (US) has been proposed, and shows promising results. However, the major limitations in these studies were the use of manual segmentation and the limiting field of view of US. Therefore in this study, the AAA is imaged with multiperspective 3D ultrasound, merged to obtain a large field of view, and afterwards automatically segmented. Geometry and wall stress results were validated using CT imaging. METHODS: Three dimensional US and CT data were available for 40 AAA patients (maximum diameter 34-61 mm). The full US based AAA geometry was determined using automatic segmentation, and when the aneurysm exceeded a single 3D volume, automatic fusion of multiple 3D US volumes was used. Wall stress analysis was performed for all AAA patients and percentile wall stresses were derived. The accuracy of the US based geometry and wall stress prediction was measured by comparison with CT data. RESULTS: Estimated geometries derived from 3D US and CT data showed good similarity, with an overall median similarity index (SI) of 0.89 and interquartile range of 0.87-0.92, whereas the median Hausdorff distances (HD), a measure for the maximum local mismatch, was 4.6 (4.0-5.9) mm for all AAA geometries. Thereby, the wall stress results based on merged multiperspective 3D US data revealed a greater similarity to CT than single 3D US data. CONCLUSION: This study showed that large volume geometry assessment of AAAs using multiperspective 3D ultrasound, segmentation and fusion, and wall stress analysis is feasible in a robust and labour efficient manner.

Ultrasound-based estimation of remaining cardiac function in LVAD-supported ex vivo hearts.

Left ventricular assist devices (LVAD) provide cardiac support to patients with advanced heart failure. Methods that can directly measure remaining LV function following device implantation do not currently exist. Previous studies have shown that a combination of loading (LV pressure) and deformation (strain) measurements enables quantitation of myocardial work. We investigated the use of ultrasound (US) strain imaging and pressure-strain loop analysis in LVAD-supported hearts under different hemodynamic and pump unloading conditions, with the aim of determining LV function with and without LVAD support. Ex vivo porcine hearts (n = 4) were implanted with LVADs and attached to a mock circulatory loop. Measurements were performed at hemodynamically defined "heart conditions" as the hearts deteriorated from baseline. Hemodynamic (including LV pressure) and radio-frequency US data were acquired during a pump-ramp protocol at speeds from 0 (with no pump outflow) to 10 000 revolutions per minute (rpm). Regional circumferential (εcirc ) and radial (εrad ) strains were estimated over each heart cycle. Regional ventricular dyssynchrony was quantitated through time-to-peak strain. Mean change in LV pulse pressure and εcirc between 0 and 10 krpm were -21.8 mm Hg and -7.24% in the first condition; in the final condition -46.8 mm Hg and -19.2%, respectively. εrad was not indicative of changes in pump speed or heart condition. Pressure-strain loops showed a degradation in the LV function and an increased influence of LV unloading: loop area reduced by 90% between 0 krpm in the first heart condition and 10 krpm in the last condition. High pump speeds and degraded condition led to increased dyssynchrony between the septal and lateral LV walls. Functional measurement of the LV while undergoing LVAD support is possible by using US strain imaging and pressure-strain loops. This can provide important information about remaining pump function. Use of novel LV pressure estimation or measurement techniques would be required for any future use in LVAD patients.

Carotid Artery Plaque Vulnerability Assessment Using Noninvasive Ultrasound Elastography: Validation With MRI.

OBJECTIVE: Vulnerable and nonvulnerable carotid artery plaques have different tissue morphology and composition that may affect plaque biomechanics. The objective of this study is to evaluate plaque vulnerability with the use of ultrasound noninvasive vascular elastography (NIVE). MATERIALS AND METHODS: Thirty-one patients (mean [± SD] age, 69 ± 7 years) with stenosis of the internal carotid artery of 50% or greater were enrolled in this cross-sectional study. Elastography parameters quantifying axial strain, shear strain, and translation motion were used to characterize carotid artery plaques as nonvulnerable, neovascularized, and vulnerable. Maximum axial strain, cumulated axial strain, mean shear strain, cumulated shear strain, cumulated axial translation, and cumulated lateral translations were measured. Cumulated measurements were summed over a cardiac cycle. The ratio of cumulated axial strain to cumulated axial translation was also evaluated. The reference method used to characterize plaques was high-resolution MRI. RESULTS: According to MRI, seven plaques were vulnerable, 12 were nonvulnerable without neovascularity, and 12 were nonvulnerable with neovascularity (a precursor of vulnerability). The two parameters cumulated axial translation and the ratio of cumulated axial strain to cumulated axial translation could discriminate between nonvulnerable plaques and vulnerable plaques or determine the presence of neovascularity in nonvulnerable plaques (which was also possible with the mean shear strain parameter). All parameters differed between the non-vulnerable plaque group and the group that combined vulnerable plaques and plaques with neovascularity. The most discriminating parameter for the detection of vulnerable neovascularized plaques was the ratio of cumulated axial strain to cumulated axial translation (expressed as percentage per millimeter) (mean ratio, 39.30%/mm ± 12.80%/mm for nonvulnerable plaques without neovascularity vs 63.79%/mm ± 17.59%/mm for vulnerable plaques and nonvulnerable plaques with neovascularity, p = 0.002), giving an AUC value of 0.886. CONCLUSION: The imaging parameters cumulated axial translation and the ratio of cumulated axial strain to cumulated axial translation, as computed using NIVE, were able to discriminate vulnerable carotid artery plaques characterized by MRI from nonvulnerable carotid artery plaques. Consideration of neovascularized plaques improved the performance of NIVE. NIVE may be a valuable alternative to MRI for carotid artery plaque assessment.

Application of an Adaptive Polynomial Chaos Expansion on Computationally Expensive Three-Dimensional Cardiovascular Models for Uncertainty Quantification and Sensitivity Analysis.

When applying models to patient-specific situations, the impact of model input uncertainty on the model output uncertainty has to be assessed. Proper uncertainty quantification (UQ) and sensitivity analysis (SA) techniques are indispensable for this purpose. An efficient approach for UQ and SA is the generalized polynomial chaos expansion (gPCE) method, where model response is expanded into a finite series of polynomials that depend on the model input (i.e., a meta-model). However, because of the intrinsic high computational cost of three-dimensional (3D) cardiovascular models, performing the number of model evaluations required for the gPCE is often computationally prohibitively expensive. Recently, Blatman and Sudret (2010, "An Adaptive Algorithm to Build Up Sparse Polynomial Chaos Expansions for Stochastic Finite Element Analysis," Probab. Eng. Mech., 25(2), pp. 183-197) introduced the adaptive sparse gPCE (agPCE) in the field of structural engineering. This approach reduces the computational cost with respect to the gPCE, by only including polynomials that significantly increase the meta-model's quality. In this study, we demonstrate the agPCE by applying it to a 3D abdominal aortic aneurysm (AAA) wall mechanics model and a 3D model of flow through an arteriovenous fistula (AVF). The agPCE method was indeed able to perform UQ and SA at a significantly lower computational cost than the gPCE, while still retaining accurate results. Cost reductions ranged between 70-80% and 50-90% for the AAA and AVF model, respectively.

Feasibility of wall stress analysis of abdominal aortic aneurysms using three-dimensional ultrasound.

OBJECTIVE: Abdominal aortic aneurysms (AAAs) are local dilations that can lead to a fatal hemorrhage when ruptured. Wall stress analysis of AAAs is a novel tool that has proven high potential to improve risk stratification. Currently, wall stress analysis of AAAs is based on computed tomography (CT) and magnetic resonance imaging; however, three-dimensional (3D) ultrasound (US) has great advantages over CT and magnetic resonance imaging in terms of costs, speed, and lack of radiation. In this study, the feasibility of 3D US as input for wall stress analysis is investigated. Second, 3D US-based wall stress analysis was compared with CT-based results. METHODS: The 3D US and CT data were acquired in 12 patients (diameter, 35-90 mm). US data were segmented manually and compared with automatically acquired CT geometries by calculating the similarity index and Hausdorff distance. Wall stresses were simulated at P = 140 mm Hg and compared between both modalities. RESULTS: The similarity index of US vs CT was 0.75 to 0.91 (n = 12), with a median Hausdorff distance ranging from 4.8 to 13.9 mm, with the higher values found at the proximal and distal sides of the AAA. Wall stresses were in accordance with literature, and a good agreement was found between US- and CT-based median stresses and interquartile stresses, which was confirmed by Bland-Altman and regression analysis (n = 8). Wall stresses based on US were typically higher (+23%), caused by geometric irregularities due to the registration of several 3D volumes and manual segmentation. In future work, an automated US registration and segmentation approach is the essential point of improvement before pursuing large-scale patient studies. CONCLUSIONS: This study is a first step toward US-based wall stress analysis, which would be the modality of choice to monitor wall stress development over time because no ionizing radiation and contrast material are involved.

Increasing abdominal aortic aneurysm curvature visibility using 3D dual probe bistatic ultrasound imaging combined with probe translation.

High frame rate ultrasound (US) imaging techniques in 3D are promising tools for capturing abdominal aortic aneurysms (AAAs) over time, however, with the limited number of channel-to-element connections current footprints are small, which limits the field of view. Moreover, the maximal steering angle of the ultrasound beams in transmit and the maximal receptance angle in receive are insufficient for capturing the curvy shape of the AAA. Therefore, an approach is needed towards large arrays. In this study, high frame rate bistatic 3D US data (17 Hz) were acquired with two synchronized matrix arrays positioned at different locations (multi-aperture imaging) using a translation stage to simulate what a larger array with limited channel-to-element connections can potentially achieve. Acquisitions were performed along an AAA shaped phantom with different probe tilting angles (0 up to ± 30°). The performance of different multi-aperture configurations was quantified using the generalized contrast-to-noise ratio of the wall and lumen (gCNR). Furthermore, a parametric model of the multi-aperture system was used to estimate in which AAA wall regions the contrast is expected to be high. This was evaluated for AAAs with increasing diameters and curvature. With an eight-aperture 0° probe angle configuration a 69 % increase in field of view was measured in the longitudinal direction compared to the field of view of a single aperture configuration. When increasing the number of apertures from two to eight, the gCNR improved for the upper wall and lower wall by 35 % and 13 % (monostatic) and by 36 % and 13 % (bistatic). Contrast improvements up to 22 % (upper wall) and 12 % (lower wall) are achieved with tilted probe configurations compared to non-tilted configurations. Moreover, with bistatic imaging with tilted probe configurations gCNR improvements up to 4 % (upper wall) and 7 % (lower wall) are achieved compared to monostatic imaging. Furthermore, imaging with a larger inter-probe distance improved the gCNR for a ± 15° probe angle configuration. The gCNR has an expected pattern over time, where the contrast is lower when there is more wall motion (systole) and higher when motion is reduced (diastole). Furthermore, a higher frame rate (45 Hz) yields a lower gCNR, because fewer compound angles are used. The results of the parametric model suggest that a flat array is suitable for imaging AAA shapes with limited curvature, but that it is not suitable for imaging larger AAA shapes with more curvature. According to the model, tilted multi-aperture configurations combined with bistatic imaging can achieve a larger region with high contrast compared to non-tilted configurations. The findings of the model are in agreement with experimental findings. To conclude, this study demonstrates the vast improvements in field of view and AAA wall visibility that a large, sparsely populated 3D array can potentially achieve when imaging AAAs compared to single or dual aperture imaging. In the future, larger arrays, less thermal noise, more steering, and more channel-to-element connections combined with carefully chosen orientations of (sub-) apertures will likely advance 3D imaging of AAAs.

In vivo bistatic dual-aperture ultrasound imaging and elastography of the abdominal aorta.

Introduction: In this paper we introduce in vivo multi-aperture ultrasound imaging and elastography of the abdominal aorta. Monitoring of the geometry and growth of abdominal aortic aneurysms (AAA) is paramount for risk stratification and intervention planning. However, such an assessment is limited by the lateral lumen-wall contrast and resolution of conventional ultrasound. Here, an in vivo dual-aperture bistatic imaging approach is shown to improve abdominal ultrasound and strain imaging quality significantly. By scanning the aorta from different directions, a larger part of the vessel circumference can be visualized. Methods: In this first-in-man volunteer study, the performance of multi-aperture ultrasound imaging and elastography of the abdominal aortic wall was assessed in 20 healthy volunteers. Dual-probe acquisition was performed in which two curved array transducers were aligned in the same imaging plane. The transducers alternately transmit and both probes receive simultaneously on each transmit event, which allows for the reconstruction of four ultrasound signals. Automatic probe localization was achieved by optimizing the coherence of the trans-probe data, using a gradient descent algorithm. Speckle-tracking was performed on the four individual bistatic signals, after which the respective axial displacements were compounded and strains were calculated. Results: Using bistatic multi-aperture ultrasound imaging, the image quality of the ultrasound images, i.e., the angular coverage of the wall, was improved which enables accurate estimation of local motion dynamics and strain in the abdominal aortic wall. The motion tracking error was reduced from 1.3 mm ± 0.63 mm to 0.16 mm ± 0.076 mm, which increased the circumferential elastographic signal-to-noise ratio (SNRe) by 12.3 dB ± 8.3 dB on average, revealing more accurate and homogeneous strain estimates compared to single-perspective ultrasound. Conclusion: Multi-aperture ultrasound imaging and elastography is feasible in vivo and can provide the clinician with vital information about the anatomical and mechanical state of AAAs in the future.

Automatic Segmentation of Abdominal Aortic Aneurysms from Time-Resolved 3D Ultrasound Images Using Deep Learning.

Abdominal aortic aneurysms (AAAs) are rupture-prone dilatations of the aorta. In current clinical practice, the maximal diameter of AAAs is monitored with 2D ultrasound to estimate their rupture risk. Recent studies have shown that 3-dimensional and mechanical AAA parameters might be better predictors for aneurysm growth and rupture than the diameter. These parameters can be obtained with time-resolved 3D ultrasound (3D+t US), which requires robust and automatic segmentation of AAAs from 3D+t US. This study proposes and validates a deep learning (DL) approach for automatic segmentation of AAAs. 500 AAA patients were included for follow-up 3D+t US imaging, resulting in 2495 3D+t US images. Segmentation masks for model training were obtained using a conventional automatic segmentation algorithm ('nonDL'). Four different DL models were trained and validated by (1) comparison to CT and (2) reader scoring. Performance of the nonDL and different DL segmentation strategies were evaluated by comparing Hausdorff distance, Dice scores, accuracy, sensitivity, and specificity with a sign test. All DL models had higher median Dice scores, accuracy, and sensitivity (all p < 0.003) compared to nonDL segmentation. The full image-resolution model without data augmentation showed the highest median Dice score and sensitivity (p < 0.001). Applying the DL model on an independent test group produced fewer poor segmentation scores of 1 to 2 on a five-point scale (8% for DL, 18% for nonDL). This demonstrates that a robust and automatic segmentation algorithm for segmenting abdominal aortic aneurysms from 3D+t US images was developed, showing improved performance compared to conventional segmentation.

Effect of urban environment on cardiovascular health: a feasibility pilot study using machine learning to predict heart rate variability in patients with heart failure.

Aims: Urbanization is related to non-communicable diseases such as congestive heart failure (CHF). Understanding the influence of diverse living environments on physiological variables such as heart rate variability (HRV) in patients with chronic cardiac disease may contribute to more effective lifestyle advice and telerehabilitation strategies. This study explores how machine learning (ML) models can predict HRV metrics, which measure autonomic nervous system responses to environmental attributes in uncontrolled real-world settings. The goal is to validate whether this approach can ascertain and quantify the connection between environmental attributes and cardiac autonomic response in patients with CHF. Methods and results: A total of 20 participants (10 healthy individuals and 10 patients with CHF) wore smartwatches for 3 weeks, recording activities, locations, and heart rate (HR). Environmental attributes were extracted from Google Street View images. Machine learning models were trained and tested on the data to predict HRV metrics. The models were evaluated using Spearman's correlation, root mean square error, prediction intervals, and Bland-Altman analysis. Machine learning models predicted HRV metrics related to vagal activity well (R > 0.8 for HR; 0.8 > R > 0.5 for the root mean square of successive interbeat interval differences and the Poincaré plot standard deviation perpendicular to the line of identity; 0.5 > R > 0.4 for the high frequency power and the ratio of the absolute low- and high frequency power induced by environmental attributes. However, they struggled with metrics related to overall autonomic activity, due to the complex balance between sympathetic and parasympathetic modulation. Conclusion: This study highlights the potential of ML-based models to discern vagal dynamics influenced by living environments in healthy individuals and patients diagnosed with CHF. Ultimately, this strategy could offer rehabilitation and tailored lifestyle advice, leading to improved prognosis and enhanced overall patient well-being in CHF.

Automatic Probe Localization in Multiaperture Ultrasound by Radon Domain Signal Matching.

In multiaperture ultrasound, several ultrasound probes with different insonification angles are combined to increase the field of view and angular coverage of image structures. A full reconstruction incorporating all possible combinations of transmitting and receiving probes has been shown to improve resolution, contrast, and angular coverage beyond what can be achieved by the registration of single images from different probes. A major challenge in multiaperture imaging is the correct determination of relative probe locations. A registration based on the content of images from different probes is challenging due to the decorrelation of image structures and speckle with increasing angle between the probes. We propose a probe localization method for plane-wave ultrasound that uses solely the receive dataset of a nontransmitting probe. The localization is performed by signal tracking in the Radon domain. To demonstrate that the method does not rely on common structures in the individual images, we show that a satisfying localization can be performed in pure speckle for angles, where the speckle patterns have completely decorrelated. The method shows potential for real-time probe localization in free-hand multiprobe ultrasound imaging or for flexible and wearable multiarray combination of multiple capacitive micromachined (CMUT)-based systems in the future.

Coherent Bistatic 3-D Ultrasound Imaging Using Two Sparse Matrix Arrays.

In the last decade, many advances have been made in high frame rate 3-D ultrasound imaging, including more flexible acquisition systems, transmit (TX) sequences, and transducer arrays. Compounding multiangle transmits of diverging waves has shown to be fast and effective for 2-D matrix arrays, where heterogeneity between transmits is key in optimizing the image quality. However, the anisotropy in contrast and resolution remains a drawback that cannot be overcome with a single transducer. In this study, a bistatic imaging aperture is demonstrated that consists of two synchronized matrix ( 32×32 ) arrays, allowing for fast interleaved transmits with a simultaneous receive (RX). First, for a single array, the aperture efficiency for high volume rate imaging was evaluated between sparse random arrays and fully multiplexed arrays. Second, the performance of the bistatic acquisition scheme was analyzed for various positions on a wire phantom and was showcased in a dynamic setup mimicking the human abdomen and aorta. Sparse array volume images were equal in resolution and lower in contrast compared to fully multiplexed arrays but can efficiently minimize decorrelation during motion for multiaperture imaging. The dual-array imaging aperture improved the spatial resolution in the direction of the second transducer, reducing the average volumetric speckle size with 72% and the axial-lateral eccentricity with 8%. In the aorta phantom, the angular coverage increased by a factor of 3 in the axial-lateral plane, raising the wall-lumen contrast with 16% compared to single-array images, despite accumulation of thermal noise in the lumen.

An aberration correction approach for single and dual aperture ultrasound imaging of the abdomen.

Abdominal ultrasound image quality is hampered by phase aberration, that is mainly caused by the large speed-of-sound (SoS) differences between fat and muscle tissue in the abdominal wall. The mismatch between the assumed and actual SoS distribution introduces general blurring of the ultrasound images, and acoustic refraction can lead to geometric distortion of the imaged features. Large aperture imaging or dual-transducer imaging can improve abdominal imaging at deep locations by providing increased contrast and resolution. However, aberration effects for large aperture imaging can be even more severe, which limits its full potential. In this study, a model-based aberration correction method for arbitrary acquisition schemes is introduced for delay-and-sum (DAS) beamforming and its performance was analyzed for both single- and dual-transducer ultrasound imaging. The method employs aberration corrected wavefront arrival times, using manually assigned local SoS values. Two wavefront models were compared. The first model is based on a straight ray approximation, and the second model on the Eikonal equation, which is solved by a multi-stencils fast marching method. Their accuracy for abdominal imaging was evaluated in acoustic simulations and phantom experiments involving tissue-mimicking and porcine material with large SoS contrast (∼100 m/s). The lateral resolution was improved by up to 90% in simulations and up to 65% in experiments compared to standard DAS, in which the use of Eikonal beamforming generally outperformed straight ray beamforming. Moreover, geometric distortions were mitigated in multi-aperture imaging, leading to a reduction in position error of around 80%. A study on the sensitivity of the aberration correction to shape and SoS of aberrating layers was performed, showing that even with imperfect segmentations or SoS values, aberration correction still outperforms standard DAS.

Enabling strain imaging in realistic Eulerian ultrasound simulation methods.

Cardiovascular strain imaging is continually improving due to ongoing advances in ultrasound acquisition and data processing techniques. The phantoms used for validation of new methods are often burdensome to make and lack flexibility to vary mechanical and acoustic properties. Simulations of US imaging provide an alternative with the required flexibility and ground truth strain data. However, the current Lagrangian US strain imaging models cannot simulate heterogeneous speed of sound distributions and higher-order scattering, which limits the realism of the simulations. More realistic Eulerian modelling techniques exist but have so far not been used for strain imaging. In this research, a novel sampling scheme was developed based on a band-limited interpolation of the medium, which enables accurate strain simulation in Eulerian methods. The scheme was validated in k-Wave using various numerical phantoms and by a comparison with Field II. The method allows for simulations with a large range in strain values and was accurate with errors smaller than -60 dB. Furthermore, an excellent agreement with the Fourier theory of US scattering was found. The ability to perform simulations with heterogeneous speed of sound distributions was demonstrated using a pulsating artery model. The developed sampling scheme contributes to more realistic strain imaging simulations, in which the effect of heterogenous acoustic properties can be taken into account.

Spatiotemporal Registration of 3-D Multi-perspective Ultrasound Images of Abdominal Aortic Aneurysms.

Methods for patient-specific abdominal aortic aneurysm (AAA) progression monitoring and rupture risk assessment are widely investigated. Three-dimensional ultrasound can visualize the AAA's complex geometry and displacement fields. However, ultrasound has a limited field of view and low frame rate (i.e., 3-8 Hz). This article describes an approach to enhance the temporal resolution and the field of view. First, the frame rate was increased for each data set by sequencing multiple blood pulse cycles into one cycle. The sequencing method uses the original frame rate and the estimated pulse wave rate obtained from AAA distension curves. Second, the temporal registration was applied to multi-perspective acquisitions of the same AAA. Third, the field of view was increased through spatial registration and fusion using an image feature-based phase-only correlation method and a wavelet transform, respectively. Temporal sequencing was fully correct in aortic phantoms and was successful in 51 of 62 AAA patients, yielding a factor 5 frame rate increase. Spatial registration of proximal and distal ultrasound acquisitions was successful in 32 of 37 different AAA patients, based on the comparison between the fused ultrasound and computed tomography segmentation (95th percentile Haussdorf distances and similarity indices of 4.2 ± 1.7 mm and 0.92 ± 0.02 mm, respectively). Furthermore, the field of view was enlarged by 9%-49%.

Ultrasound-Based Estimation of Fibre-Directional Strain: A Simulation Study.

Left ventricular (LV) strains are typically represented with respect to the imaging axes. Contraction within the myocardium occurs along myofibres, which vary in orientation. Therefore, a mismatch exists between the direction in which strain is calculated and that in which contraction occurs. In this study, ultrasound-based fibre orientation and 3-D strain estimation were combined to calculate the fibre-directional strain. Three-dimensional ultrasound volumes were created by simulating simple geometrical phantoms and a phantom based on a finite-element (FE) model of LV mechanics. Fibre-like structures were embedded within tissue-mimicking scatterers. Strains were applied to the numerical phantom, whereas the FE phantom was deformed based on the LV model. Fibre orientation was accurately estimated for both phantoms. There was poor agreement in axial and elevational strains (root mean square error = 29.9% and 12.3%), but good agreement in lateral and fibre-directional strains (root mean square error = 6.4% and 5.9% respectively), which aligned in the midwall. Simplifications to reduce computational complexity caused poor axial and elevational strain estimation. However, calculation of fibre-directional strain from single-modality ultrasound volumes was successful. Further studies, in ex vivo setups because of the fundamental limitations of currently available transducers, are needed to verify real-world performance of the method.

LVNet: Lightweight Model for Left Ventricle Segmentation for Short Axis Views in Echocardiographic Imaging.

Lightweight segmentation models are becoming more popular for fast diagnosis on small and low cost medical imaging devices. This study focuses on the segmentation of the left ventricle (LV) in cardiac ultrasound (US) images. A new lightweight model [LV network (LVNet)] is proposed for segmentation, which gives the benefits of requiring fewer parameters but with improved segmentation performance in terms of Dice score (DS). The proposed model is compared with state-of-the-art methods, such as UNet, MiniNetV2, and fully convolutional dense dilated network (FCdDN). The model proposed comes with a post-processing pipeline that further enhances the segmentation results. In general, the training is done directly using the segmentation mask as the output and the US image as the input of the model. A new strategy for segmentation is also introduced in addition to the direct training method used. Compared with the UNet model, an improvement in DS performance as high as 5% for segmentation with papillary (WP) muscles was found, while showcasing an improvement of 18.5% when the papillary muscles are excluded. The model proposed requires only 5% of the memory required by a UNet model. LVNet achieves a better trade-off between the number of parameters and its segmentation performance as compared with other conventional models. The developed codes are available at https://github.com/navchetanawasthi/Left_Ventricle_Segmentation.