A Forked Microvascular Phantom for Ultrasound Localization Microscopy Investigations.

In the development of ultrasound localization microscopy (ULM) methods, appropriate test beds are needed to facilitate algorithmic performance calibration. Here, we present the design of a new ULM-compatible microvascular phantom with a forked, V-shaped wall-less flow channel pair (250 µm channel width) that bifurcated at a separation rate of 50 µm/mm. The lumen core was fabricated using additive manufacturing, and it was molded within a polyvinyl alcohol (PVA) tissue mimicking slab using the lost-core casting method. Measured using optical microscopy, the lumen core's flow channel width was found to be 252±15 µm with a regression-derived flow channel separation gradient of 50.89 µm/mm. The new phantom's applicability in ULM performance analysis was demonstrated by feeding microbubble contrast flow (1.67 to 167 µl/s flow rates) through the phantom's inlet and generating ULM images with a previously reported method. Results showed that, with longer acquisition times (10 s or longer), ULM image quality was expectedly improved, and the variance of ULM-derived flow channel measurements was reduced. Also, at axial depths near the lumen's bifurcation point, the current ULM algorithm showed difficulty in properly discerning between the two flow channels because of the narrow channel-to-channel separation distance. Overall, the new phantom serves well as a calibration tool to test the performance of ULM methods in resolving small vasculature.

Branched Convolutional Neural Networks for Receiver Channel Recovery in High-Frame-Rate Sparse-Array Ultrasound Imaging.

For high-frame-rate ultrasound imaging, it remains challenging to implement on compact systems as a sparse imaging configuration with limited array channels. One key issue is that the resulting image quality is known to be mediocre not only because unfocused plane-wave excitations are used but also because grating lobes would emerge in sparse-array configurations. In this article, we present the design and use of a new channel recovery framework to infer full-array plane-wave channel datasets for periodically sparse arrays that operate with as few as one-quarter of the full-array aperture. This framework is based on a branched encoder-decoder convolutional neural network (CNN) architecture, which was trained using full-array plane-wave channel data collected from human carotid arteries (59 864 training acquisitions; 5-MHz imaging frequency; 20-MHz sampling rate; plane-wave steering angles between -15° and 15° in 1° increments). Three branched encoder-decoder CNNs were separately trained to recover missing channels after differing degrees of channelwise downsampling (2, 3, and 4 times). The framework's performance was tested on full-array and downsampled plane-wave channel data acquired from an in vitro point target, human carotid arteries, and human brachioradialis muscle. Results show that when inferred full-array plane-wave channel data were used for beamforming, spatial aliasing artifacts in the B-mode images were suppressed for all degrees of channel downsampling. In addition, the image contrast was enhanced compared with B-mode images obtained from beamforming with downsampled channel data. When the recovery framework was implemented on an RTX-2080 GPU, the three investigated degrees of downsampling all achieved the same inference time of 4 ms. Overall, the proposed framework shows promise in enhancing the quality of high-frame-rate ultrasound images generated using a sparse-array imaging setup.

Deep-learning-assisted and GPU-accelerated vector Doppler imaging with aliasing-resistant velocity estimation.

Vector flow imaging is a diagnostic ultrasound modality that is suited for the visualization of complex blood flow dynamics. One popular way of realizing vector flow imaging at high frame rates over 1000 fps is to apply multi-angle vector Doppler estimation principles in conjunction with plane wave pulse-echo sensing. However, this approach is susceptible to flow vector estimation errors attributed to Doppler aliasing, which is prone to arise when a low pulse repetition frequency (PRF) is inevitably used due to the need for finer velocity resolution or because of hardware constraints. Existing dealiasing solutions tailored for vector Doppler may have high computational demand that makes them unfeasible for practical applications. In this paper, we present the use of deep learning and graphical processing unit (GPU) computing principles to devise a fast vector Doppler estimation framework that is resilient against aliasing artifacts. Our new framework works by using a convolutional neural network (CNN) to detect aliased regions in vector Doppler images and subsequently applying an aliasing correction algorithm only at these affected regions. The framework's CNN was trained using 15,000 in vivo vector Doppler frames acquired from the femoral and carotid arteries, including healthy and diseased conditions. Results show that our framework can perform aliasing segmentation with an average precision of 90 % and can render aliasing-free vector flow maps with real-time processing throughputs (25-100 fps). Overall, our new framework can improve the visualization quality of vector Doppler imaging in real-time.

Efficacy of ultrasound vector flow imaging in tracking omnidirectional pulsatile flow.

BACKGROUND: Ultrasound vector flow imaging (VFI) shows potential as an emerging non-invasive modality for time-resolved flow mapping. However, its efficacy in tracking multidirectional pulsatile flow with temporal resolvability has not yet been systematically evaluated because of the lack of an appropriate test protocol. PURPOSE: We present the first systematic performance investigation of VFI in tracking pulsatile flow in a meticulously designed scenario with time-varying, omnidirectional flow fields (with flow angles from 0° to 360°). METHODS: Ultrasound VFI was performed on a three-loop spiral flow phantom (4 mm diameter; 5 mm pitch) that was configured to operate under pulsatile flow conditions (10 ml/s peak flow rate; 1 Hz pulse rate; carotid pulse shape). The spiral lumen geometry was designed to simulate recirculatory flow dynamics observed in the heart and in curvy blood vessel segments such as the carotid bulb. The imaging sequence was based on steered plane wave pulsing (-10°, 0°, +10° steering angles; 5 MHz imaging frequency; 3.3 kHz interleaved pulse repetition frequency). VFI's pulsatile flow estimation performance and its ability to detect secondary flow were comparatively assessed against flow fields derived from computational fluid dynamics (CFD) simulations that included consideration of fluid-structure interactions (FSI). The mean percentage error (MPE) and the coefficient of determination (R2 ) were computed to assess the correspondence of the velocity estimates derived from VFI and CFD-FSI simulations. In addition, VFI's efficacy in tracking pulse waves was analyzed with respect to pressure transducer measurements made at the phantom's inlet and outlet. RESULTS: Pulsatile flow patterns rendered by VFI agreed with the flow profiles computed from CFD-FSI simulations (average MPE: -5.3%). The shape of the VFI-measured velocity magnitude profile generally matched the inlet flow profile. High correlation exists between VFI measurements and simulated flow vectors (lateral velocity: R2  = 0.8; axial velocity R2  = 0.89; beam-flow angle: R2  = 0.98; p < 0.0001 for all three quantities). VFI was found to be capable of consistently tracking secondary flow. It also yielded pulse wave velocity (PWV) estimates (5.72 ± 1.02 m/s) that, on average, are within 6.4% of those obtained from pressure transducer measurements (6.11 ± 1.15 m/s). CONCLUSION: VFI can consistently track omnidirectional pulsatile flow on a time-resolved basis. This systematic investigation serves well as a quality assurance test of VFI.

Time-Resolved Wall Shear Rate Mapping Using High-Frame-Rate Ultrasound Imaging.

In atherosclerosis, low wall shear stress (WSS) is known to favor plaque development, while high WSS increases plaque rupture risk. To improve plaque diagnostics, WSS monitoring is crucial. Here, we propose wall shear imaging (WASHI), a noninvasive contrast-free framework that leverages high-frame-rate ultrasound (HiFRUS) to map the wall shear rate (WSR) that relates to WSS by the blood viscosity coefficient. Our method measures WSR as the tangential flow velocity gradient along the arterial wall from the flow vector field derived using a multi-angle vector Doppler technique. To improve the WSR estimation performance, WASHI semiautomatically tracks the wall position throughout the cardiac cycle. WASHI was first evaluated with an in vitro linear WSR gradient model; the estimated WSR was consistent with theoretical values (an average error of 4.6% ± 12.4 %). The framework was then tested on healthy and diseased carotid bifurcation models. In both scenarios, key spatiotemporal dynamics of WSR were noted: 1) oscillating shear patterns were present in the carotid bulb and downstream to the internal carotid artery (ICA) where retrograde flow occurs; and 2) high WSR was observed particularly in the diseased model where the measured WSR peaked at 810 [Formula: see text] due to flow jetting. We also showed that WASHI could consistently track arterial wall motion to map its WSR. Overall, WASHI enables high temporal resolution mapping of WSR that could facilitate investigations on causal effects between WSS and atherosclerosis.

Unfocused Field Analysis of a Density-Tapered Spiral Array for High-Volume-Rate 3-D Ultrasound Imaging.

Spiral array transducers with a sparse 2-D aperture have demonstrated their potential in realizing 3-D ultrasound imaging with reduced data rates. Nevertheless, their feasibility in high-volume-rate imaging based on unfocused transmissions has yet to be established. From a metrology standpoint, it is essential to characterize the acoustic field of unfocused transmissions from spiral arrays not only to assess their safety but also to identify the root cause of imaging irregularities due to the array's sparse aperture. Here, we present a field profile analysis of unfocused transmissions from a density-tapered spiral array transducer (256 hexagonal elements, 220- [Formula: see text] element diameter, and 1-cm aperture diameter) through both simulations and hydrophone measurements. We investigated plane- and diverging-wave transmissions (five-cycle, 7.5-MHz pulses) from 0° to 10° steering for their beam intensity characteristics and wavefront arrival time profiles. Unfocused firings were also tested for B-mode imaging performance (ten compounded angles, -5° to 5° span). The array was found to produce unfocused transmissions with a peak negative pressure of 93.9 kPa at 2 cm depth. All transmissions steered up to 5° were free of secondary lobes within 12 dB of the main beam peak intensity. All wavefront arrival time profiles were found to closely match the expected profiles with maximum root-mean-squared errors of [Formula: see text] for plane wave (PW) and [Formula: see text] for diverging wave. The B-mode images showed good spatial resolution with a penetration depth of 22 mm in PW imaging. Overall, these results demonstrate that the density-tapered spiral array can facilitate unfocused transmissions below regulatory limits (mechanical index: 0.034; spatial-peak, pulse-average intensity: 0.298 W/cm2) and with suppressed secondary lobes while maintaining smooth wavefronts.

Minimizing Image Quality Loss After Channel Count Reduction for Plane Wave Ultrasound via Deep Learning Inference.

High-frame-rate ultrasound imaging uses unfocused transmissions to insonify an entire imaging view for each transmit event, thereby enabling frame rates over 1000 frames per second (fps). At these high frame rates, it is naturally challenging to realize real-time transfer of channel-domain raw data from the transducer to the system back end. Our work seeks to halve the total data transfer rate by uniformly decimating the receive channel count by 50% and, in turn, doubling the array pitch. We show that despite the reduced channel count and the inevitable use of a sparse array aperture, the resulting beamformed image quality can be maintained by designing a custom convolutional encoder-decoder neural network to infer the radio frequency (RF) data of the nullified channels. This deep learning framework was trained with in vivo human carotid data (5-MHz plane wave imaging, 128 channels, 31 steering angles over a 30° span, and 62 799 frames in total). After training, the network was tested on an in vitro point target scenario that was dissimilar to the training data, in addition to in vivo carotid validation datasets. In the point target phantom image beamformed from inferred channel data, spatial aliasing artifacts attributed to array pitch doubling were found to be reduced by up to 10 dB. For carotid imaging, our proposed approach yielded a lumen-to-tissue contrast that was on average within 3 dB compared to the full-aperture image, whereas without channel data inferencing, the carotid lumen was obscured. When implemented on an RTX-2080 GPU, the inference time to apply the trained network was 4 ms, which favors real-time imaging. Overall, our technique shows that with the help of deep learning, channel data transfer rates can be effectively halved with limited impact on the resulting image quality.

Vascular Imaging in Small Animals Using Clinical Ultrasound Scanners.

Conventional ultrasound with frequency (2-15 MHz) has been a global diagnostic and therapeutic tool in clinical medicine, and high-frequency ultrasound (>30 MHz) has been a powerful investigative device for preclinical studies such as cardiovascular research. In this chapter, we describe the use of conventional ultrasound with a 2.5-10 MHz transducer as an investigative device for the measurement/detection of blood flow in rabbit model. The chapter will describe the procedures for the preparation of sonographer, imaging locations, and the details of the rabbits used as well as detailed imaging steps for the preoperative, immediately after operation, and postoperative follow-up ultrasound for vascular surgery, using a vascular graft implantation as an example. We also provide useful notes to avoid pitfalls for successful imaging. The overall goal of this chapter is to deliver the steps in using low-cost, non-invasive, and highly versatile clinical ultrasound imaging in preclinical small animal testing.

In Situ and Intraoperative Detection of the Ureter Injury Using a Highly Sensitive Piezoresistive Sensor with a Tunable Porous Structure.

Iatrogenic ureteral injury, as a commonly encountered problem in gynecologic, colorectal, and pelvic surgeries, is known to be difficult to detect in situ and in real-time. Consequently, this injury may be left untreated, thereby leading to serious complications such as infections, renal failure, or even death. Here, high-performance tubular porous pressure sensors were proposed to identify the ureter in situ intraoperatively. The electrical conductivity, mechanical compressibility, and sensor sensitivity can be tuned by changing the pore structure of porous conductive composites. A low percolation threshold of 0.33 vol % was achieved due to the segregated conductive network by pores. Pores also lead to a low effective Young's modulus and high compressibility of the composites and thus result in a high sensitivity of 448.2 kPa-1 of sensors, which is consistent with the results of COMSOL simulation. Self-mounted on the tip of forceps, the sensors can monitor tube pressures with different frequencies and amplitudes, as demonstrated using an artificial pump system. The sensors can also differentiate ureter pulses from aorta pulses of a Bama minipig in situ and in real-time. This work provides a facile, cost-effective, and nondestructive method to identify the ureter intraoperatively, which cannot be effectively achieved by traditional methods.

Helical toroid phantom for 3D flow imaging investigations.

The medical physics community has hitherto lacked an effective calibration phantom to holistically evaluate the performance of three-dimensional (3D) flow imaging techniques. Here, we present the design of a new omnidirectional, three-component (3-C) flow phantom whose lumen is consisted of a helical toroid structure (4 mm lumen diameter; helically winded for 5 revolutions over a torus with 10 mm radius; 5 mm helix radius). This phantom's intraluminal flow trajectory embraces all combinations of x, y, and z directional components, as confirmed using computational fluid dynamics (CFD) simulations. The phantom was physically fabricated via lost-core casting with polyvinyl alcohol cryogel (PVA) as the tissue mimic. 3D ultrasound confirmed that the phantom lumen expectedly resembled a helical toroid geometry. Pulsed Doppler measurements showed that the phantom, when operating under steady flow conditions (3 ml s-1 flow rate), yielded flow velocity magnitudes that agreed well with those derived from CFD at both the inner torus (-47.6 ± 5.7 versus -52.0 ± 2.2 cm s-1; mean ± 1 S.D.) and the outer torus (49.5 ± 4.2 versus 48.0 ± 1.7 cm s-1). Additionally, 3-C velocity vectors acquired from multi-angle pulsed Doppler showed good agreement with CFD-derived velocity vectors (<7% and 10° difference in magnitude and flow angle, respectively). Ultrasound color flow imaging further revealed that the phantom's axial flow pattern was aligned with the CFD-derived flow profile. Overall, the helical toroid phantom has strong potential as an investigative tool in 3D flow imaging innovation endeavors, such as the development of flow vector estimators and visualization algorithms.

A Deep Learning Approach to Resolve Aliasing Artifacts in Ultrasound Color Flow Imaging.

Despite being used clinically as a noninvasive flow visualization tool, color flow imaging (CFI) is known to be prone to aliasing artifacts that arise due to fast blood flow beyond the detectable limit. From a visualization standpoint, these aliasing artifacts obscure proper interpretation of flow patterns in the image view. Current solutions for resolving aliasing artifacts are typically not robust against issues such as double aliasing. In this article, we present a new dealiasing technique based on deep learning principles to resolve CFI aliasing artifacts that arise from single- and double-aliasing scenarios. It works by first using two convolutional neural networks (CNNs) to identify and segment CFI pixel positions with aliasing artifacts, and then it performs phase unwrapping at these aliased pixel positions. The CNN for aliasing identification was devised as a U-net architecture, and it was trained with in vivo CFI frames acquired from the femoral bifurcation that had known presence of single- and double-aliasing artifacts. Results show that the segmentation of aliased CFI pixels was achieved successfully with intersection over union approaching 90%. After resolving these artifacts, the dealiased CFI frames consistently rendered the femoral bifurcation's triphasic flow dynamics over a cardiac cycle. For dealiased CFI pixels, their root-mean-squared difference was 2.51% or less compared with manual dealiasing. Overall, the proposed dealiasing framework can extend the maximum flow detection limit by fivefold, thereby improving CFI's flow visualization performance.

Contrast-Enhanced Urodynamic Vector Projectile Imaging (CE-UroVPI) for Urethral Voiding Visualization: Principles and Phantom Studies.

OBJECTIVE: To devise a new urodynamic imaging framework that can provide time-resolved visualization of urinary flow and urethral deformation during the initiation phase of voiding. MATERIALS AND METHODS: Contrast-enhanced urodynamic vector projectile imaging (CE-UroVPI) was devised using the principles of high-frame rate ultrasound, microbubble contrast agents, and flow vector mapping. CE-UroVPI was implemented using a research-purpose ultrasound scanner (5 MHz frequency) and commercial contrast agents (USphere Prime). The performance of CE-UroVPI was evaluated using 2 custom-designed deformable urethra phantoms - a healthy model and a diseased model with benign prostatic hyperplasia (BPH) - that respectively simulate urodynamics in the urinary tract with and without mechanical obstruction. The corresponding spatiotemporal urodynamics were investigated and analyzed. RESULTS: Using a frame rate of 1,250 fps that corresponds to 0.8 ms time resolution, CE-UroVPI effectively depicted the transient urodynamic events during the initiation phase of voiding. Anomalous spatiotemporal characteristics were observed in the urodynamics of the BPH-obstructed urethra. Specifically, upstream from the obstruction site, a transient surge in flow speed was observed in the first 100 ms of voiding. Also, downstream from the obstruction site, complex urodnyamics had emerged in the forms of flow jet and vortices. These anomalies were not found in the healthy urethra. CONCLUSION: CE-UroVPI is the first imaging framework that can visualize complex urodynamics over an entire voiding episode including its initiation phase. This new tool may be used to potentially gain new insight into the causal relationships between urethral morphokinetic factors and lower urinary tract symptoms.

Ultrasound vector projectile imaging for detection of altered carotid bifurcation hemodynamics during reductions in cardiac output.

PURPOSE: Complex blood flow is commonly observed in the carotid bifurcation, although the factors that regulate these patterns beyond arterial geometry are unknown. The emergence of high-frame-rate ultrasound vector flow imaging allows for noninvasive, time-resolved analysis of complex hemodynamic behavior in humans, and it can potentially help researchers understand which physiological stressors can alter carotid bifurcation hemodynamics in vivo. Here, we seek to pursue the first use of vector projectile imaging (VPI), a dynamic form of vector flow imaging, to analyze the regulation of carotid bifurcation hemodynamics during experimental reductions in cardiac output induced via a physiological stressor called lower body negative pressure (LBNP). METHODS: Seven healthy adults (age: 27 ± 4 yr, 4 men) underwent LBNP at -45 mmHg to simulate a postural hemodynamic response in a controlled environment. Using a research-grade, high-frame-rate ultrasound platform, vector flow estimation in each subject's right carotid bifurcation was performed through a multi-angle plane wave imaging (two transmission angles of 10° and -10°) formulation, and VPI cineloops were generated at a frame rate of 750 fps. Vector concentration was quantified by the resultant blood velocity vector angles within a region of interest; lower concentration indicated greater flow dispersion. Discrete concentration values during peak and late systole were compared across different segments of the carotid artery bifurcation before, and during, LBNP. RESULTS: Vector projectile imaging revealed that external and internal carotid arteries exhibited regional hemodynamic changes during LBNP, which acted to reduce both the subject's cardiac output (Δ - 1.2 ± 0.5 L/min, -19%; P < 0.01) and peak carotid blood velocity (Δ - 6.30 ± 8.27 cm/s, -7%; P = 0.05). In these carotid artery branches, the vector concentration time trace before and during LBNP were observed to be different. The impact of LBNP on flow complexity in the two carotid artery branches showed variations between subjects. CONCLUSIONS: Using VPI, intuitive visualization of complex hemodynamic changes can be obtained in healthy humans subjected to LBNP. This imaging tool has potential for further applications in vascular physiology to identify and quantify complex hemodynamic features in humans during different physiological stressor tests that regulate hemodynamics.

Deformable phantoms of the prostatic urinary tract for urodynamic investigations.

PURPOSE: Assessment of urethral dynamics is clinically regarded to be important in analyzing the functional impact of pathological features like urethral obstruction, albeit it is difficult to perform directly in vivo. To facilitate such an assessment, urethra phantoms may serve well as investigative tools by reconstructing urethral dynamics based on anthropomorphic factors. Here, our aim is to design a new class of anatomically realistic, deformable urethra phantoms that can simulate the geometric, mechanical, and hydrodynamic characteristics of the male prostatic urethra. METHODS: A new lost-core tube casting protocol was devised. It first involved the drafting of urethra geometry in computer-aided design software. Next, 3D printing was used to fabricate the urethra geometry and an outer mold. These parts were then used to cast a urinary tract using a polyvinyl alcohol (PVA)-based material (with 26.6 ± 4.0 kPa Young's elastic modulus). After forming a surrounding tissue-mimicking slab using an agar-gelatin mixture (with 17.4 ± 3.4 kPa Young's modulus), the completed urethra phantom was connected to a flow circuit that simulates voiding. To assess the fabricated phantoms' morphology, ultrasound imaging was performed over different planes. Also, color Doppler imaging was performed to visualize the flow profile within the urinary tract. RESULTS: Deformable phantoms were devised for the normal urethra and a diseased urethra with obstruction due to benign prostatic hyperplasia (BPH). During voiding, the short-axis lumen diameter at the verumontanum of the BPH-featured phantom (0.91 ± 0.08 mm) was significantly smaller than that for the normal phantom (2.49 ± 0.20 mm). Also, the maximum flow velocity of the BPH-featured phantom (59.3 ± 5.8 cm/s; without Doppler angle correction) was found to be higher than that of the normal phantom (22.7 ± 9.0 cm/s). CONCLUSION: The fabricated phantoms were effective in simulating urethra deformation resulting from urine passage during voiding. They can be used for mechanistic studies of urethral dynamics and for the testing of urodynamic diagnostic techniques in urology.

High frame rate doppler ultrasound bandwidth imaging for flow instability mapping.

PURPOSE: Flow instability has been shown to contribute to the risk of future cardiovascular and cerebrovascular events. Nonetheless, it is challenging to noninvasively detect and identify flow instability in blood vessels. Here, we present a new framework called Doppler ultrasound bandwidth imaging (DUBI) that uses high-frame-rate ultrasound and Doppler bandwidth analysis principles to assess flow instability within an image view. METHODS: Doppler ultrasound bandwidth imaging seeks to estimate the instantaneous Doppler bandwidth based on autoregressive modeling at every pixel position of data frames acquired from high-frame-rate plane wave pulsing. This new framework is founded upon the principle that flow instability naturally gives rise to a wide range of flow velocities over a sample volume, and such velocity range in turn yields a larger Doppler bandwidth estimate. The ability for DUBI to map unstable flow was first tested over a range of fluid flow conditions (ranging from laminar to turbulent) with a nozzle-flow phantom. As a further demonstration, DUBI was applied to assess flow instability in healthy and stenosed carotid bifurcation phantoms. RESULTS: Nozzle-flow phantom results showed that DUBI can effectively detect and visualize the difference in Doppler bandwidth magnitude (increased from 2.1 to 5.2 kHz) at stable and unstable flow regions in an image view. Receiver operating characteristic analysis also showed that DUBI can achieve optimal sensitivity and specificity of 0.72 and 0.83, respectively. In the carotid phantom experiments, differences were observed in the spatiotemporal dynamics of Doppler bandwidth over a cardiac cycle. Specifically, as the degree of stenosis increased (from 50% to 75%), DUBI showed an increase in Doppler bandwidth magnitude from 1.4 kHz in the healthy bifurcation to 7.7 kHz at the jet tail located downstream from a 75% stenosis site, thereby indicating flow perturbation in the stenosed bifurcations. CONCLUSION: DUBI can detect unstable flow. This new technique can provide useful hemodynamic information that may aid clinical diagnosis of atherosclerosis.

Receiver-Operating Characteristic Analysis of Eigen-Based Clutter Filters for Ultrasound Color Flow Imaging.

The eigen-based filter has theoretically established itself as a potent solution in ultrasound color flow imaging (CFI) for combating against clutter arising from moving tissues. Yet, it remains poorly understood on how much gain in flow detection sensitivity and specificity can be delivered by this adaptive clutter filter. Here, we investigated the receiver operating characteristic (ROC) of the eigen-based clutter filter to statistically evaluate its efficacy. Our investigation was conducted using a new vascular phantom testbed that incorporated both intrinsic tissue motion (vessel pulsation: 7.58 cm/s peak velocity) and extrinsic tissue motion (vibration: 5-Hz frequency, 2.98 cm/s peak velocity), as well as pulsatile flow (pulse rate: 60 beats/min; systolic flow rate: 6.5 mL/s). The eigen-filter (single-ensemble formulation) was applied to CFI raw data sets obtained from the phantom's short-axis view (slow-time ensemble size: 12; pulse repetition frequency: 2 kHz; and ultrasound frequency: 5 MHz), and post-filter Doppler power was compared between flow and tissue regions. Results show that, in the presence of vessel pulsation and tissue vibration, the eigen-filter yielded a high true positive rate in depicting flow pixels in CFI frames (0.945 and 0.917, respectively, during peak systole and end diastole at 60° beam-flow angle), while maintaining a low false alarm rate (0.10) in rendering tissue pixels. Also, the eigen-filter posed ROC curves whose area under curve was higher than those for the polynomial regression filter (statistically significant; t-test p values were less than 0.05). These findings serve well to substantiate the merit of using eigen-filters to enhance the vascular visualization capability of CFI.

Arterial Phantoms with Regional Variations in Wall Stiffness and Thickness.

Regional wall stiffening and thickening are two common pathological features of arteries. To account for these two features, we developed a new arterial phantom design framework to facilitate the development of vessel models that contain a lesion segment whose wall stiffness and thickness differ from those of other segments. This new framework is based on multi-part injection molding principles that sequentially casted the lesion segment and the flank segments of the vessel model using molding parts devised with computer-aided design tools. The vessel-mimicking material is created from polyvinyl alcohol cryogel, and its acoustic properties are similar to those of arteries. As a case demonstration, we fabricated a stenosed three-segment phantom composed of a central lesion segment (5.1-mm diameter, 1.95-mm wall thickness, 212.6-kPa elastic modulus) and two flank segments (6.0-mm diameter, 1.5-mm wall thickness, 133.7-kPa elastic modulus). B-mode imaging confirmed the difference in thickness between the lesion segment and flank segments of the phantom. Also, Doppler-based vessel wall displacement analysis revealed that when pulsatile flow was fed through the phantom (carotid pulse; 27 mL/s peak flow rate), the lesion segment distended less compared with the flank segments. Specifically, the three-beat averaged peak wall displacement in the lesion segment was measured as 0.28 mm, and it was significantly smaller than that of the flank segments (0.60 mm). It is anticipated that this new multi-segment arterial phantom can serve as a performance testbed for the evaluation of local arterial stiffness estimation algorithms.

Wall-Less Flow Phantoms With Tortuous Vascular Geometries: Design Principles and a Patient-Specific Model Fabrication Example.

Flow phantoms with anatomically realistic geometry and high acoustic compatibility are valuable investigative tools in vascular ultrasound studies. Here, we present a new framework to fabricate ultrasound-compatible flow phantoms to replicate human vasculature that is tortuous, nonplanar, and branching in nature. This framework is based upon the integration of rapid prototyping and investment casting principles. A pedagogical walkthrough of our engineering protocol is presented in this paper using a patient-specific cerebral aneurysm model as an exemplar demonstration. The procedure for constructing the flow circuit component of the phantoms is also presented, including the design of a programmable flow pump system, the fabrication of blood mimicking fluid, and flow rate calibration. Using polyvinyl alcohol cryogel as the tissue mimicking material, phantoms developed with the presented protocol exhibited physiologically relevant acoustic properties [attenuation coefficient: 0.229±0.032 dB/( [Formula: see text]) and acoustic speed: 1535±2.4 m/s], and their pulsatile flow dynamics closely resembled the flow profile input. As a first application of our developed phantoms, the flow pattern of the patient-specific aneurysm model was visualized by performing high-frame-rate color-encoded speckle imaging over multiple time-synchronized scan planes. Persistent recirculation was observed, and the vortex center was found to shift in position over a cardiac cycle, indicating the 3-D nature of flow recirculation inside an aneurysm. These findings suggest that phantoms produced from our reported protocol can serve well as acoustically compatible test beds for vascular ultrasound studies, including 3-D flow imaging.

A GPU-Parallelized Eigen-Based Clutter Filter Framework for Ultrasound Color Flow Imaging.

Eigen-filters with attenuation response adapted to clutter statistics in color flow imaging (CFI) have shown improved flow detection sensitivity in the presence of tissue motion. Nevertheless, its practical adoption in clinical use is not straightforward due to the high computational cost for solving eigendecompositions. Here, we provide a pedagogical description of how a real-time computing framework for eigen-based clutter filtering can be developed through a single-instruction, multiple data (SIMD) computing approach that can be implemented on a graphical processing unit (GPU). Emphasis is placed on the single-ensemble-based eigen-filtering approach (Hankel singular value decomposition), since it is algorithmically compatible with GPU-based SIMD computing. The key algebraic principles and the corresponding SIMD algorithm are explained, and annotations on how such algorithm can be rationally implemented on the GPU are presented. Real-time efficacy of our framework was experimentally investigated on a single GPU device (GTX Titan X), and the computing throughput for varying scan depths and slow-time ensemble lengths was studied. Using our eigen-processing framework, real-time video-range throughput (24 frames/s) can be attained for CFI frames with full view in azimuth direction (128 scanlines), up to a scan depth of 5 cm ( λ pixel axial spacing) for slow-time ensemble length of 16 samples. The corresponding CFI image frames, with respect to the ones derived from non-adaptive polynomial regression clutter filtering, yielded enhanced flow detection sensitivity in vivo, as demonstrated in a carotid imaging case example. These findings indicate that the GPU-enabled eigen-based clutter filtering can improve CFI flow detection performance in real time.

Walled Carotid Bifurcation Phantoms for Imaging Investigations of Vessel Wall Motion and Blood Flow Dynamics.

As a major application domain of vascular ultrasound, the carotid artery has long been the subject of anthropomorphic phantom design. It is nevertheless not trivial to develop walled carotid phantoms that are compatible for use in integrative imaging of carotid wall motion and flow dynamics. In this paper, we present a novel phantom design protocol that can enable efficient fabrication of walled carotid bifurcation phantoms with: (i) high acoustic compatibility, (ii) artery-like vessel elasticity, and (iii) stenotic narrowing feature. Our protocol first involved direct fabrication of the vessel core and an outer mold using computer-aided design tools and 3-D printing technology; these built parts were then used to construct an elastic vessel tube through investment casting of a polyvinyl alcohol containing mixture, and an agar-gelatin tissue mimicking slab was formed around the vessel tube. For demonstration, we applied our protocol to develop a set of healthy and stenosed (25%, 50%, 75%) carotid bifurcation phantoms. Plane wave imaging experiments were performed on these phantoms using an ultrasound scanner with channel-level configurability. Results show that the wall motion dynamics of our phantoms agreed with pulse wave propagation in an elastic vessel (pulse wave velocity of 4.67±0.71 m/s measured at the common carotid artery), and their flow dynamics matched the expected ones in healthy and stenosed bifurcation (recirculation and flow jet formation observed). Integrative imaging of vessel wall motion and blood flow dynamics in our phantoms was also demonstrated, from which we observed fluid-structure interaction differences between healthy and diseased bifurcation phantoms. These findings show that the walled bifurcation phantoms developed with our new protocol are useful in vascular imaging studies that individually or jointly assess wall motion and flow dynamics.