Automatic algorithm for monitoring systolic pressure variation and difference in pulse pressure

BACKGROUND: Difference in pulse pressure (dPP) reliably predicts fluid responsiveness in patients. We have developed a respiratory variation (RV) monitoring device (RV monitor), which continuously records both airway pressure and arterial blood pressure (ABP). We compared the RV monitor measurements with manual dPP measurements. METHODS: ABP and airway pressure (PAW) from 24 patients were recorded. Data were fed to the RV monitor to calculate dPP and systolic pressure variation in two different ways: (a) considering both ABP and PAW (RV algorithm) and (b) ABP only (RV(slim) algorithm). Additionally, ABP and PAW were recorded intraoperatively in 10-min intervals for later calculation of dPP by manual assessment. Interobserver variability was determined. Manual dPP assessments were used for comparison with automated measurements. To estimate the importance of the PAW signal, RV(slim) measurements were compared with RV measurements. RESULTS: For the 24 patients, 174 measurements (6-10 per patient) were recorded. Six observers assessed dPP manually in the first 8 patients (10-min interval, 53 measurements); no interobserver variability occurred using a computer-assisted method. Bland-Altman analysis showed acceptable bias and limits of agreement of the 2 automated methods compared with the manual method (RV: -0.33% +/- 8.72% and RV(slim): -1.74% +/- 7.97%). The difference between RV measurements and RV(slim) measurements is small (bias -1.05%, limits of agreement 5.67%). CONCLUSIONS: Measurements of the automated device are comparable with measurements obtained by human observers, who use a computer-assisted method. The importance of the PAW signal is questionable.

A comparative study on manual and automatic slice-to-volume registration of CT images

In order to assess the clinical relevance of a slice-to-volume registration algorithm, this technique was compared to manual registration. Reformatted images obtained from a diagnostic CT examination of the lower abdomen were reviewed and manually registered by 41 individuals. The results were refined by the algorithm. Furthermore, a fully automatic registration of the single slices to the whole CT examination, without manual initialization, was also performed. The manual registration error for rotation and translation was found to be 2.7+/-2.8 degrees and 4.0+/-2.5 mm. The automated registration algorithm significantly reduced the registration error to 1.6+/-2.6 degrees and 1.3+/-1.6 mm (p = 0.01). In 3 of 41 (7.3%) registration cases, the automated registration algorithm failed completely. On average, the time required for manual registration was 213+/-197 s; automatic registration took 82+/-15 s. Registration was also performed without any human interaction. The resulting registration error of the algorithm without manual pre-registration was found to be 2.9+/-2.9 degrees and 1.1+/-0.2 mm. Here, a registration took 91+/-6 s, on average. Overall, the automated registration algorithm improved the accuracy of manual registration by 59% in rotation and 325% in translation. The absolute values are well within a clinically relevant range.

Effect of automatic tube current modulation on radiation dose and image quality for low tube voltage multidetector row CT angiography: phantom study

RATIONALE AND OBJECTIVES: To evaluate the effect of automatic tube current modulation on radiation dose and image quality for low tube voltage computed tomography (CT) angiography. MATERIALS AND METHODS: An anthropomorphic phantom was scanned with a 64-section CT scanner using following tube voltages: 140 kVp (Protocol A), 120 kVp (Protocol B), 100 kVp (Protocol C), and 80 kVp (Protocol D). To achieve similar noise, combined z-axis and xy-axes automatic tube current modulation was applied. Effective dose (ED) for the four tube voltages was assessed. Three plastic vials filled with different concentrations of iodinated solution were placed on the phantom's abdomen to obtain attenuation measurements. The signal-to-noise ratio (SNR) was calculated and a figure of merit (FOM) for each iodinated solution was computed as SNR(2)/ED. RESULTS: The ED was kept similar for the four different tube voltages: (A) 5.4 mSv +/- 0.3, (B) 4.1 mSv +/- 0.6, (C) 3.9 mSv +/- 0.5, and (D) 4.2 mSv +/- 0.3 (P > .05). As the tube voltage decreased from 140 to 80 kVp, image noise was maintained (range, 13.8-14.9 HU) (P > .05). SNR increased as the tube voltage decreased, with an overall gain of 119% for the 80-kVp compared to the 140-kVp protocol (P < .05). The FOM results indicated that with a reduction of the tube voltage from 140 to 120, 100, and 80 kVp, at constant SNR, ED was reduced by a factor of 2.1, 3.3, and 5.1, respectively, (P < .001). CONCLUSIONS: As tube voltage decreases, automatic tube current modulation for CT angiography yields either a significant increase in image quality at constant radiation dose or a significant decrease in radiation dose at a constant image quality.

Automatic Labeling of Software Components and their Evolution using Log-Likelihood Ratio of Word Frequencies in Source Code

As more and more open-source software components become available on the internet we need automatic ways to label and compare them. For example, a developer who searches for reusable software must be able to quickly gain an understanding of retrieved components. This understanding cannot be gained at the level of source code due to the semantic gap between source code and the domain model. In this paper we present a lexical approach that uses the log-likelihood ratios of word frequencies to automatically provide labels for software components. We present a prototype implementation of our labeling/comparison algorithm and provide examples of its application. In particular, we apply the approach to detect trends in the evolution of a software system.

A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier

In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease." The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.

Automatic Data Normalization and Parameterization for Optical Motion Tracking

Methods for optical motion capture often require timeconsuming manual processing before the data can be used for subsequent tasks such as retargeting or character animation. These processing steps restrict the applicability of motion capturing especially for dynamic VR-environments with real time requirements. To solve these problems, we present two additional, fast and automatic processing stages based on our motion capture pipeline presented in [HSK05]. A normalization step aligns the recorded coordinate systems with the skeleton structure to yield a common and intuitive data basis across different recording sessions. A second step computes a parameterization based on automatically extracted main movement axes to generate a compact motion description. Our method does not restrict the placement of marker bodies nor the recording setup, and only requires a short calibration phase.

Algorithms For Automatic And Robust Registration Of 3D Head Scans

wo methods for registering laser-scans of human heads and transforming them to a new semantically consistent topology defined by a user-provided template mesh are described. Both algorithms are stated within the Iterative Closest Point framework. The first method is based on finding landmark correspondences by iteratively registering the vicinity of a landmark with a re-weighted error function. Thin-plate spline interpolation is then used to deform the template mesh and finally the scan is resampled in the topology of the deformed template. The second algorithm employs a morphable shape model, which can be computed from a database of laser-scans using the first algorithm. It directly optimizes pose and shape of the morphable model. The use of the algorithm with PCA mixture models, where the shape is split up into regions each described by an individual subspace, is addressed. Mixture models require either blending or regularization strategies, both of which are described in detail. For both algorithms, strategies for filling in missing geometry for incomplete laser-scans are described. While an interpolation-based approach can be used to fill in small or smooth regions, the model-driven algorithm is capable of fitting a plausible complete head mesh to arbitrarily small geometry, which is known as "shape completion". The importance of regularization in the case of extreme shape completion is shown.

Development of a process model for the automatic miniload warehouse expansion

This paper provides an insight to the development of a process model for the essential expansion of the automatic miniload warehouse. The model is based on the literature research and covers four phases of a warehouse expansion: the preparatory phase, the current state analysis, the design phase and the decision making phase. In addition to the literature research, the presented model is based on a reliable data set and can be applicable with a reasonable effort to ensure the informed decision on the warehouse layout. The model is addressed to users who are usually employees of logistics department, and is oriented on the improvement of the daily business organization combined with the warehouse expansion planning.