Despite being used clinically as a non-invasive 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 paper, 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 is 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 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 rootmean-squared difference was 2.51% or less comparing to manual dealiasing. Overall, the proposed dealiasing framework can extend the maximum flow detection limit by fivefold, thereby improving CFI's flow visualization performance.

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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 AE 4.0 kPa Young's elastic modulus). After forming a surrounding tissue-mimicking slab using an agar-gelatin mixture (with 17.4 AE 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 AE 0.08 mm) was significantly smaller than that for the normal phantom (2.49 AE 0.20 mm). Also, the maximum flow velocity of the BPHfeatured phantom (59.3 AE 5.8 cm/s; without Doppler angle correction) was found to be higher than that of the normal phantom (22.7 AE 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.