Objectives: The aim of this study is to develop a method to evaluate the fluid dynamics of urine flow in the lower urinary tract (LUT), especially that of vorticity. Materials and Methods:This investigation included three sub-studies to demonstrate urine flow in the entire LUT. First, we attempted to observe vorticity generation in the urinary bladder during spontaneous voiding using transabdominal color Doppler ultrasonography (CDUS). Second, we performed transrectal CDUS to evaluate the vorticity of urine flow in the prostatic urethra. Patients with prostate cancer were enrolled before robotic surgery and divided into the vorticity and nonvorticity groups based on CDUS findings for comparisons of longitudinal urethral diameter and prostatic urethral angle. Third, the vorticity of the voided urine stream was observed using a high-speed video-camera. Micturition was done in a standing position while synchronously monitored for urine flow using uroflowmetry.Results: Vorticity formation could be dynamically demonstrated in the urinary bladder and prostatic urethra using CDUS. The prostatic urethral angle of the vorticity group was more than that of the non-vorticity group. High-speed video recording could clearly capture vorticity and spiral shape generation in voided urine. The distance from the external urethral orifice to the first twist changed in accordance with urine flow rate. Conclusions: In a series of sub-studies, this investigation proved vorticity generation in the LUT and voided urine. Vorticity was detectable in the LUT and in voided urine using CDUS and a high-speed video-camera. Vorticity generation might be associated with urethral morphology. K E Y W O R D S fluid dynamics, lower urinary tract, urodynamics, vorticity Abbreviations: CDUS, color Doppler ultrasonography; LUD, longitudinal urethral diameter; LUT, lower urinary tract; PUA, prostatic urethral angle; Qmax, maximum urine flow rate; US, ultrasonography; VFM, vector flow mapping.

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A shape feature by itself is not sufficient for effective 3D model retrieval. Long-lasting semantics shared by a community as well as a short-lived intention of a user determines the similarity of 3D models. In this paper, we describe a method of shape-based 3D model retrieval that employs off-line, semi-supervised learning of multiple classes in the database to capture long-lasting, shared semantic knowledge. The method performs two learning based dimension reductions, first one to accommodate distribution of features in the feature space and the second one to accommodate the semantic knowledge embodied in a set of user-defined semantic labels. We evaluate the method by using the SHREC'08 3D Generic and CAD Models Track.

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