Assessment of the optimal temporal window for intravenous CT cholangiography

The optimal temporal window of intravenous (IV) computed tomography (CT) cholangiography was prospectively determined. Fifteen volunteers (eight women, seven men; mean age, 38 years) underwent dynamic CT cholangiography. Two unenhanced images were acquired at the porta hepatis. Starting 5 min after initiation of IV contrast infusion (20 ml iodipamide meglumine 52%), 15 pairs of images at 5-min intervals were obtained. Attenuation of the extrahepatic bile duct (EBD) and the liver parenchyma was measured. Two readers graded visualization of the higher-order biliary branches. The first biliary opacification in the EBD occurred between 15 and 25 min (mean, 22.3 min +/- 3.2) after initiation of the contrast agent. Biliary attenuation plateaued between the 35- and the 75-min time points. Maximum hepatic parenchymal enhancement was 18.5 HU +/- 2.7. Twelve subjects demonstrated poor or non-visualization of higher-order biliary branches; three showed good or excellent visualization. Body weight and both biliary attenuation and visualization of the higher-order biliary branches correlated significantly (P<0.05). For peak enhancement of the biliary tree, CT cholangiography should be performed no earlier than 35 min after initiation of IV infusion. For a fixed contrast dose, superior visualization of the biliary system is achieved in subjects with lower body weight.

Multi-detector row CT of the small bowel: peak enhancement temporal window--initial experience

PURPOSE: To prospectively determine quantitatively and qualitatively the timing of maximal enhancement of the normal small-bowel wall by using contrast material-enhanced multi-detector row computed tomography (CT). MATERIALS AND METHODS: This HIPAA-compliant study was approved by the institutional review board. After information on radiation risk was given, written informed consent was obtained from 25 participants with no history of small-bowel disease (mean age, 58 years; 19 men) who had undergone single-level dynamic CT. Thirty seconds after the intravenous administration of contrast material, a serial dynamic acquisition, consisting of 10 images obtained 5 seconds apart, was performed. Enhancement measurements were obtained over time from the small-bowel wall and the aorta. Three independent readers qualitatively assessed small-bowel conspicuity. Quantitative and qualitative data were analyzed during the arterial phase, the enteric phase (which represented peak small-bowel mural enhancement), and the venous phase. Statistical analysis included paired Student t test and Wilcoxon signed rank test with Bonferroni correction. A P value less than .05 was used to indicate a significant difference. RESULTS: The mean time to peak enhancement of the small-bowel wall was 49.3 seconds +/- 7.7 (standard deviation) and 13.5 seconds +/- 7.6 after peak aortic enhancement. Enhancement values were highest during the enteric phase (P < .05). Regarding small-bowel conspicuity, images obtained during the enteric phase were most preferred qualitatively; there was a significant difference between the enteric and arterial phases (P < .001) but not between the enteric and venous phases (P = .18). CONCLUSION: At multi-detector row CT, peak mural enhancement of the normal small bowel occurs on average about 50 seconds after intravenous administration of contrast material or 14 seconds after peak aortic enhancement.

Four-window technique for measuring optical-phase-space-time-frequency tomography

A new approach, the four-window technique, was developed to measure optical phase-space-time-frequency tomography (OPSTFT). The four-window technique is based on balanced heterodyne detection with two local oscillator (LO) fields. This technique can provide independent control of position, momentum, time and frequency resolution. The OPSTFT is a Wigner distribution function of two independent Fourier transform pairs, phase-space and time-frequency. The OPSTFT can be applied for early disease detection.

The cranial bone window model: studying angiogenesis of primary and secondary bone tumors by intravital microscopy

The successful treatment of primary and secondary bone tumors in a huge number of cases remains one of the major unsolved challenges in modern medicine. Malignant primary bone tumor growth predominantly occurs in younger people, whereas older people predominantly suffer from secondary bone tumors since up to 85% of the most frequently occurring malignant solid tumors, such as lung, mammary, and prostate carcinomas, metastasize into the bone. It is well known that a tumor's course may be altered by its surrounding tissue. For this reason, reported here is the protocol for the surgical preparation of a cranial bone window in mice as well as the method to implant tumors in this bone window for further investigations of angiogenesis and other microcirculatory parameters in orthotopically growing primary or secondary bone tumors using intravital microscopy. Intravital microscopy represents an internationally accepted and sophisticated experimental method to study angiogenesis, microcirculation, and many other parameters in a wide variety of neoplastic and nonneoplastic tissues. Since most physiologic and pathophysiologic processes are active and dynamic events, one of the major strengths of chronic animal models using intravital microscopy is the possibility of monitoring the regions of interest in vivo continuously up to several weeks with high spatial and temporal resolution. In addition, after the termination of experiments, tissue samples can be excised easily and further examined by various in vitro methods such as histology, immunohistochemistry, and molecular biology.

Autumn Leaves: Curing the Window Plague in IDEs

Navigating large software systems is difficult as the various artifacts are distributed in a huge space, while the relationships between different artifacts often remain hidden and obscure. As a consequence, developers using a modern interactive development environment (IDE) are forced to open views on numerous source artifacts to reveal these hidden relationships, leading to a crowded workspace with many opened windows or tabs. Developers often lose the overview in such a cluttered workspace as IDEs provide little support to get rid of unused windows. AutumnLeaves automatically selects windows unlikely for future use to be closed or grayed out while important ones are displayed more prominently. This reduces the number of windows opened at a time and adds structure to the developer's workspace. We validate AutumnLeaves with a benchmark evaluation using recorded navigation data of various developers to determine the prediction quality of the employed algorithms.

Evaluation of texture features in hepatic tissue characterization from non-enhanced CT images

Aim of this paper is to evaluate the diagnostic contribution of various types of texture features in discrimination of hepatic tissue in abdominal non-enhanced Computed Tomography (CT) images. Regions of Interest (ROIs) corresponding to the classes: normal liver, cyst, hemangioma, and hepatocellular carcinoma were drawn by an experienced radiologist. For each ROI, five distinct sets of texture features are extracted using First Order Statistics (FOS), Spatial Gray Level Dependence Matrix (SGLDM), Gray Level Difference Method (GLDM), Laws' Texture Energy Measures (TEM), and Fractal Dimension Measurements (FDM). In order to evaluate the ability of the texture features to discriminate the various types of hepatic tissue, each set of texture features, or its reduced version after genetic algorithm based feature selection, was fed to a feed-forward Neural Network (NN) classifier. For each NN, the area under Receiver Operating Characteristic (ROC) curves (Az) was calculated for all one-vs-all discriminations of hepatic tissue. Additionally, the total Az for the multi-class discrimination task was estimated. The results show that features derived from FOS perform better than other texture features (total Az: 0.802+/-0.083) in the discrimination of hepatic tissue.

Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers

The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed.

Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks

Quantitative characterisation of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The computer-aided diagnosis (CAD) system described in this paper can analyse ultrasound (US) images of carotid artery and classify them into symptomatic or asymptomatic based on their echogenicity characteristics. The CAD system consists of three modules: a) the feature extraction module, where first-order statistical (FOS) features and Laws' texture energy can be estimated, b) the dimensionality reduction module, where the number of features can be reduced using analysis of variance (ANOVA), and c) the classifier module consisting of a neural network (NN) trained by a novel hybrid method based on genetic algorithms (GAs) along with the back propagation algorithm. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance. The performance is measured by the accuracy, sensitivity, specificity and the area under the receiver-operating characteristic (ROC) curve. The CAD design and development is based on images from 54 symptomatic and 54 asymptomatic plaques. This study demonstrates the ability of a CAD system based on US image analysis and a hybrid trained NN to identify atheromatous plaques at high risk of stroke.