Transrectal ultrasound vector projectile imaging for time-resolved visualization of flow dynamics in the male urethra: A clinical pilot study.

BACKGROUND: Quantitative and comprehensive visualization of urinary flow dynamics in the urethra is crucial for investigating patient-specific mechanisms of lower urinary tract symptoms (LUTS). Although some methods can evaluate the global properties of the urethra, it is critical to assess the local information, such as the location of the responsible lesion and its interactions with urinary flow in relation to LUTS. This approach is vital for enhancing personalized and focal treatments. However, there is a lack of such diagnostic tools that can directly observe how the urethral shape and motion impact urinary flow in the urethra. PURPOSE: This study aimed to develop a novel transrectal ultrasound imaging modality based on the contrast-enhanced urodynamic vector projectile imaging (CE-UroVPI) framework and validate its clinical applicability for visualizing time-resolved flow dynamics in the urethra. METHODS: A new CE-UroVPI system was developed using a research-purpose ultrasound platform and a custom transrectal linear probe, and an imaging protocol for acquiring urodynamic echo data in male patients was designed. Thirty-four male patients with LUTS participated in this study. CE-UroVPI was performed to acquire ultrasound echo signals from the participant's urethra and urinary flow at various voiding phases (initiation, maintenance, and terminal). The ultrasound datasets were processed with custom software to visualize urinary flow dynamics and urethra tissue deformation. RESULTS: The transrectal CE-UroVPI system successfully visualized the time-resolved multidirectional urinary flow dynamics in the prostatic urethra during the initiation, maintenance, and terminal phases of voiding in 17 patients at a frame rate of 1250 fps. The maximum flow speed measured in this study was 2.5 m/s. In addition, when the urethra had an obstruction or an irregular partial deformation, the devised imaging modality visualized complex flow patterns, such as vortices and flow jets around the lesion. CONCLUSIONS: Our study findings demonstrate that the transrectal CE-UroVPI system developed in this study can effectively image fluid-structural interactions in the urethra. This new diagnostic technology has the potential to facilitate quantitative and precise assessments of urethral voiding functions and aid in the improvement of focal and effective treatments for patients with LUTS.

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.

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.

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.

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.