Ultrasound of the Kidneys and Adrenal gland

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Characteristic Plain Radiographic and Intravenous Urographic Findings of Bladder Calculi Formed over a Hair Nidus: A Case Report

We present the characteristic plain radiographic and intravenous urographic (IVU) findings of calculus formed over a hair. A 66-year-old man who had been quadriplegic for 40 years because of vertebral injury was admitted for further evaluation of frequent urinary tract infection. Plain radiography showed a linear, serpiginous calcification in the lower abdomen, and IVU revealed a round filling defect with linear radiopacity in the bladder, suggesting calculus. The gross appearance of the stone after extraction demonstrated that calcification had formed over a hair

Ultrasound of the Urinary Bladder, Revisited

Urine-filled bladder can be evaluated easily with ultrasound, and bladder tumors are usually well shown at ultrasound. Although ultrasound is not a primary imaging modality for staging of bladder tumors, it can provide general information regarding depth of tumor invasion into the proper muscle or perivesical adipose tissue. Ultrasound is also useful in showing nonneoplastic lesions of the bladder, such as stone, cystitis, diverticulum and ureterocele. Color Doppler ultrasound can show vascularity of the tumor. It also shows urine flow from the ureteral orifice or through the diverticular neck. As compared with transabdominal ultrasound, transrectal ultrasound shows bladder lesions more markedly in the dorsal wall or neck of the bladder

Radiologic Findings of Renal Inflammatory Pseudotumor: A Case Report

Renal inflammatory pseudotumor is a very rare benign condition of unknown etiology characterized by proliferative myofibroblasts, fibroblasts, histiocytes, and plasma cells. In the case we report, the lesion appeared on contrast-enhanced power Doppler US images as a well-defined hypoechoic mass with intratumoral vascularity, and on CT as a low-attenuated mass. Differentiation from malignant renal neoplasms was not possible

ICEF2004 -889 NUMERICAL PREDICTION ON THE CHARACTERISTICS OF SPRAY-INDUCED MIXING AND THERMAL DECOMPOSITION OF UREA SOLUTION IN SCR SYSTEM

ABSTRACT The spray-induced mixing characteristics and thermal decomposition of aqueous urea solution into ammonia have been studied to design optimum sizes and geometries of the mixing chamber in SCR (Selective Catalytic Reduction) system. The cold flow tests about the urea-injection nozzle were performed to clarify the parameters of spray mixing characteristics such as mean diameter and velocity of drops and spray width determined from the interactions between incoming air and injected drops. Discrete particle model in Fluent code was adopted to simulate spray-induced mixing process and the experimental results on the spray characteristics were used as input data of numerical calculations. The simulation results on the spray-induced mixing were verified by comparing the spray width extracted from the digital images with the simulated particle tracks of injected drops. The single kinetic model was adopted to predict thermal decomposition of urea solution into ammonia and solved simultaneously along with the verified spray model. The hot air generator was designed to match the flow rate and temperature of the exhaust gas of the real engines. The measured ammonia productions in the hot air generator were compared with the numerical predictions and the comparison results showed good agreements. Finally, we concluded that the design capabilities for sizing optimum mixing chamber were established

MR Imaging Findings of Ovarian Cystadenofibroma and Cystadenocarcinofibroma: Clues for the Differential Diagnosis

OBJECTIVE: We wanted to assess the MR imaging findings of ovarian cystadenofibroma and cystadenocarcinofibroma, and we wanted to find clues for making the differential diagnosis between them. MATERIALS AND METHODS: The MR images of 12 pathologically proven cystadenofibromas and two cystadenocarcinofibromas were reviewed, with a focus on the internal architecture, signal intensity and enhancement. RESULTS: All the tumors appeared as multilocular cysts, except for a single unilocular cystic mass and a single solid mass. The previously reported characteristic MR findings of cystadenofibroma (a multilocular cystic mass with a T2-dark-signal-intensity solid component containing small cystic locules) were found in only 43% of the tumors (6/14). Diffuse or partial thickening of the cyst wall with T2-dark signal intensity without a definite solid component was as common as the previous reported findings (6/14). Two cystadenocarcinofibromas showed more prominent solid portions with higher T2-signal intensities and stronger enhancement than did the cystadenofibromas. CONCLUSION: Diffuse or partial thickening of the cyst wall with dark-signal-intensity in multilocular cystic masses may suggest ovarian cystadenofibroma, and this type of appearance may be as common as the previously reported characteristic appearance. A prominent solid component with a higher T2-signal intensity and strong enhancement are the typical findings of cystadenocarcinofibroma

Medinoid : computer-aided diagnosis and localization of glaucoma using deep learning

Glaucoma is a leading eye disease, causing vision loss by gradually affecting peripheral vision if left untreated. Current diagnosis of glaucoma is performed by ophthalmologists, human experts who typically need to analyze different types of medical images generated by different types of medical equipment: fundus, Retinal Nerve Fiber Layer (RNFL), Optical Coherence Tomography (OCT) disc, OCT macula, perimetry, and/or perimetry deviation. Capturing and analyzing these medical images is labor intensive and time consuming. In this paper, we present a novel approach for glaucoma diagnosis and localization, only relying on fundus images that are analyzed by making use of state-of-the-art deep learning techniques. Specifically, our approach towards glaucoma diagnosis and localization leverages Convolutional Neural Networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM), respectively. We built and evaluated different predictive models using a large set of fundus images, collected and labeled by ophthalmologists at Samsung Medical Center (SMC). Our experimental results demonstrate that our most effective predictive model is able to achieve a high diagnosis accuracy of 96%, as well as a high sensitivity of 96% and a high specificity of 100% for Dataset-Optic Disc (OD), a set of center-cropped fundus images highlighting the optic disc. Furthermore, we present Medinoid, a publicly-available prototype web application for computer-aided diagnosis and localization of glaucoma, integrating our most effective predictive model in its back-end