Live Ultrasound Color-Encoded Speckle Imaging Platform for Real-Time Complex Flow Visualization In Vivo

Characterization of internal jugular vein region-specific distension and flow patterns during progressive volume shifting

Blood volume shifts during postural adjustment lead to irregular distension of the internal jugular vein (IJV). In microgravity, distension may contribute to flow stasis and thromboembolism, though the regional implications and associated risk remain unexplored. We characterized regional differences in IJV volume distension and flow complexity during progressive head-down tilt (HDT) (0°, -6°, -15°, -30°) using conventional ultrasound and vector flow imaging. We also evaluated low-pressure thigh cuffs (40 mmHg) as a fluid shifting countermeasure during -6° HDT. Total IJV volume expanded 139 ± 95% from supine position (4.6 ± 2.7 mL) to -30° HDT (10.3 ± 5.0 mL). Blood flow profiles had greater vector uniformity at the cranial IJV region (P < 0.01) and became more dispersed with increasing tilt (P < 0.01). Qualitatively, flow was more uniform throughout the IJV during its early flow cycle phase and more disorganized during late flow phase. This disorganized flow was accentuated closer to the vessel wall, near the caudal region, and during greater HDT. Low-pressure thigh cuffs during -6° HDT decreased IJV volume at the cranial region (-12 ± 15%; P < 0.01) but not the caudal region (P = 0.20), although flow uniformity was unchanged (both regions, P > 0.25). We describe a distensible IJV accommodating large volume shifts along its length. Prominent flow dispersion was primarily found at the caudal region, suggesting multidirectional blood flow. Thigh cuffs appear effective for decreasing IJV volume but effects on flow complexity are minor. Flow complexity along the vessel length is likely related to IJV distension during chronic volume shifting and may be a precipitating factor for flow stasis and future thromboembolism risk.NEW & NOTEWORTHY The internal jugular vein (IJV) facilitates cerebral outflow and is sensitive to volume shifts. Concerns about IJV expansion and fluid flow behavior in astronauts have surfaced following thromboembolism reports. Our study explored regional volume distension and blood flow complexity in the IJV during progressive volume shifting. We observed stepwise volume distension and increasing flow dispersion with head-down tilting across all regions. Flow dispersion may pose a risk of future thromboembolism during prolonged volume shifts.

A GPU-Based, Real-Time Dealiasing Framework for High-Frame-Rate Vector Doppler Imaging

Vector Doppler is well regarded as a potential way of deriving flow vectors to intuitively visualize complex flow profiles, especially when it is implemented at high frame rates. However, this technique's performance is known to suffer from aliasing artifacts. There is a dire need to devise real-time dealiasing solutions for vector Doppler. In this article, we present a new methodological framework for achieving aliasing-resistant flow vector estimation at real-time throughput from precalculated Doppler frequencies. Our framework comprises a series of compute kernels that have synergized: 1) an extended least squares vector Doppler (ELS-VD) algorithm; 2) single-instruction, multiple-thread (SIMT) processing principles; and 3) implementation on a graphical processing unit (GPU). Results show that this new framework, when executed on an RTX-2080 GPU, can effectively generate aliasing-free flow vector maps using high-frame-rate imaging datasets acquired from multiple transmit-receive angle pairs in a carotid phantom imaging scenario. Over the entire cardiac cycle, the frame processing time for aliasing-resistant vector estimation was measured to be less than 16 ms, which corresponds to a minimum processing throughput of 62.5 frames/s. In a human femoral bifurcation imaging trial with fast flow (150 cm/s), our framework was found to be effective in resolving two-cycle aliasing artifacts at a minimum throughput of 53 frames/s. The framework's processing throughput was generally in the real-time range for practical combinations of ELS-VD algorithmic parameters. Overall, this work represents the first demonstration of real-time, GPU-based aliasing-resistant vector flow imaging using vector Doppler estimation principles.

High-Level Synthesis Design of Scalable Ultrafast Ultrasound Beamformer With Single FPGA

Ultrafast ultrasound imaging is essential for advanced ultrasound imaging techniques such as ultrasound localization microscopy (ULM) and functional ultrasound (fUS). Current ultrafast ultrasound imaging is challenged by the ultrahigh data bandwidth associated with the radio frequency (RF) signal, and by the latency of the computationally expensive beamforming process. As such, continuous ultrafast data acquisition and beamforming remain elusive with existing software beamformers based on CPUs or GPUs. To address these challenges, the proposed work introduces a novel method of implementing an ultrafast ultrasound beamformer specifically for ultrafast plane wave imaging (PWI) on a field programmable gate array (FPGA) by using high-level synthesis. A parallelized implementation of the beamformer on a single FPGA was proposed by 1) utilizing a delay compression technique to reduce the delay profile size, which enables both run-time pre-calculated delay profile loading from external memory and delay reuse, 2) vectorizing channel data fetching which is enabled by delay reuse, and 3) using fixed summing networks to reduce consumption of logic resources. Our proposed method presents two unique advantages over current FPGA beamformers: 1) high scalability that allows fast adaptation to different FPGA resources and beamforming speed demands by using Xilinx High-Level Synthesis as the development tool, and 2) allow a compact form factor design by using a single FPGA to complete the beamforming instead of multiple FPGAs. Current Xilinx FPGAs provide the capabilities of connecting up to 1024 ultrasound channels with a single FPGA and the newest JESD204B interface analog front end (AFE). This channel count is much more than the channel count needed by current linear arrays, which normally have 128 or 256 channels. With the proposed method, a sustainable average beamforming rate of 4.83 G samples/second in terms of input raw RF sample was achieved. The resulting image quality of the proposed beamformer was compared with the software beamformer on the Verasonics Vantage system for both phantom imaging and in vivo imaging of a mouse brain. Multiple imaging schemes including B-mode, power Doppler and ULM were assessed to verify that the image quality was not compromised for speed.

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.

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.

Real-Time High Frame Rate Color Flow Mapping System

Plane wave (PW) transmission (TX) can be profitably used to improve the performance of color flow mapping (CFM) systems by increasing the autocorrelation ensemble length (EL) and/or the frame rate (FR). Although high-end scanners tend to include imaging schemes using PW TX and parallel receive beams, high frame rate (HFR) CFM has been so far experimentally implemented mostly through research platforms that transmit PWs and beamform/process the received channel data off-line. In this article, full real-time implementation of PW CFM with continuous-time clutter filtering and extended FR/EL is reported. The field-programmable gate arrays (FPGAs) and digital signal processors (DSPs) onboard the ULA-OP 256 research scanner were programmed to perform high-speed parallel beamforming and autocorrelation-based CFM processing, respectively. Different strategies were tested, in which the TX of PWs for CFM is either continuous or interleaved with the TX of packets of B-mode pulses. A fourth-order Chebyshev continuous-time high-pass filter with programmable cutoff frequency was implemented and its clutter rejection performance was positively compared with that obtained when operating on packet data. CFM FRs up to 575 were obtained. The possibility of programming the autocorrelation EL up to 64 permitted to detect flow with high sensitivity and accuracy (average relative errors down to 0.4% ± 8.4%). In vivo HFR movies are presented, showing the dynamics of flow in the common carotid artery, which highlight the presence of secondary flow components.

Architecture for an Ultrasound Advanced Open Platform With an Arbitrary Number of Independent Channels

Ultrasound open platforms are programmable and flexible tools for the development and test of novel methods. In most cases, they embed the electronics for the independent control of (maximum) 256 probe elements. However, a higher number of channels is needed for the control of 2-D array probes. This paper presents a system architecture that, through the hardware and software synchronization of multiple ULA-OP 256 scanners, may implement advanced open platforms with an arbitrary number of channels. The proposed solution needs a single personal computer, maintains real-time features, and preserves portability. A prototype demonstrator, composed of two ULA-OP 256 scanners connected to 512 elements of a matrix array, was implemented and tested according to different channel configurations. Experiments performed under MATLAB control confirmed that by doubling the number of elements (from 256 to 512) the signal-to-noise and contrast ratios improve by 9 dB and 3 dB, respectively. Furthermore, as a full 512-channel scanner, the demonstrator can produce real-time B-mode images at 18 Hz, high enough for probe positioning during acquisitions. Also, the demonstrator permitted the implementation of a new high frame rate, bi-plane, triplex modality. All probe elements are excited to simultaneously produce two planar, perpendicular diverging waves. Each scanner independently processes the echoes received by the 256 connected elements to beamform 1300 frames per second. For each insonified plane, good quality morphological (B-mode), qualitative (color flow-), and quantitative (spectral-) Doppler images are finally shown in real-time by a dedicated interface.

Virtual Real-Time for High PRF Multiline Vector Doppler on ULA-OP 256

The recent development of high-frame-rate (HFR) imaging/Doppler methods based on the transmission of plane or diverging waves has proposed new challenges to echographic data management and display. Due to the huge amount of data that need to be processed at very high speed, the pulse repetition frequency (PRF) is typically limited to hundreds hertz or few kilohertz. In Doppler applications, a PRF limitation may result unacceptable since it inherently translates to a corresponding limitation in the maximum detectable velocity. In this article, the ULA-OP 256 implementation of a novel ultrasound modality, called virtual real-time (VRT), is described. First, for a given HFR RT modality, the scanner displays the processed results while saving channel data into an internal buffer. Then, ULA-OP 256 switches to VRT mode, according to which the raw data stored in the buffer are immediately reprocessed by the same hardware used in RT. In the two phases, the ULA-OP 256 calculation power can be differently distributed to increase the acquisition frame rate or the quality of processing results. VRT was here used to extend the PRF limit in a multiline vector Doppler (MLVD) application. In RT, the PRF was maximized at the expense of the display quality; in VRT, data were reprocessed at a lower rate in a high-quality display format, which provides more detailed flow information. Experiments are reported in which the MLVD technique is shown capable of working at 16-kHz PRF, so that flow jet velocities higher up to 3 m/s can be detected.

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.

Case Studies in Physiology: Visualization of blood recirculation in a femoral artery "trifurcation" using ultrasound vector flow imaging

The femoral bifurcation is typically composed of a common femoral artery that bifurcates into the superficial (SFA) and deep (DFA) femoral arteries, with the lateral circumflex femoral artery (LCFA) branching distal to the origin of the DFA. We report a unique case of a 22-yr-old woman with a femoral "trifurcation," where the origin of the LCFA coincides with the origin of the DFA, resulting in a true three-way branching of the common femoral artery. We characterized the complex hemodynamics of the trifurcation region with ultrasound vector flow imaging at rest, and during 80 mmHg cuff compression of the calf to induce greater oscillatory blood flow. At rest, a clear trifurcation is observed with color Doppler imaging, while vector flow imaging further revealed a large area of flow circulation proximal to the LCFA and DFA. Cuff compression reduced SFA blood flow to 0 cm³/min, characterized by almost constant retrograde blood flow throughout diastole. When visualized with vector flow imaging, diastolic retrograde blood flow from the SFA appeared to reperfuse the DFA and LCFA during late systole, eliminating the retrograde flow component and providing a secondary source of anterograde blood flow to the thigh. In a rare case of a femoral trifurcation, we demonstrate blood recirculation patterns at rest, as well as collateral retrograde blood flow redistribution during lower limb compression. While it is unknown whether these trifurcation findings extend to typical bifurcations, it is evident that advanced methods of blood flow characterization are necessary to visualize and study complex vascular regions.NEW & NOTEWORTHY A femoral "trifurcation" is observed when the lateral circumflex femoral artery has an atypical proximal origin, branching at the same level as the superficial and deep femoral arteries. Ultrasound vector flow imaging at 750 fps was able to reveal substantial blood recirculation within the trifurcation at rest, as well as unique redistribution of blood flow between downstream branches during external cuff manipulation of retrograde flow, indicating novel ways in which diastolic blood flow is controlled.

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.