No abstract

This study quantified intraoperative brain distortion, determined the different behavior of tumors in four pathological groups, and identified preoperative predictors of shift with which the reliability of neuronavigation may be estimated.

We have developed a method for automatic contour propagation in cine cardiac magnetic resonance images. The method consists of a new active contour model that tries to maintain a constant contour environment by matching gray values in profiles perpendicular to the contour. Consequently, the contours should maintain a constant position with respect to neighboring anatomical structures, such that the resulting contours reflect the preferences of the user. This is particularly important in cine cardiac magnetic resonance images because local image features do not describe the desired contours near the papillary muscle. The accuracy of the propagation result is influenced by several parameters. Because the optimal setting of these parameters is application dependent, we describe how to use full factorial experiments to optimize the parameter setting. We have applied our method to cine cardiac magnetic resonance image sequences from the long axis two-chamber view, the long axis four-chamber view, and the short axis view. We performed our optimization procedure for each contour in each view. Next, we performed an extensive clinical validation of our method on 69 short axis data sets and 38 long axis data sets. In the optimal parameter setting, our propagation method proved to be fast, robust, and accurate. The resulting cardiac contours are positioned within the interobserver ranges of manual segmentation. Consequently, the resulting contours can be used to accurately determine physiological parameters such as stroke volume and ejection fraction.

A crucial step in volume rendering is the design of transfer functions that will highlight those aspects of the volume data that are of interest to the user. For many applications, boundaries carry most of the relevant information. Reliable detection of boundaries is often hampered by limitations of the imaging process, such as blurring and noise. We present a method to identify the materials that form the boundaries. These materials are then used in a new domain that facilitates interactive and semiautomatic design of appropriate transfer functions. We also show how the obtained boundary information can be used in region-growing-based segmentation.

Recently, we showed that it is possible to distinguish between three common interstitial lung diseases (ILD) with similarities in clinical presentation by using a number of selected variables derived from bronchoalveolar lavage fluid (BALF) analysis. The aim of this study was to develop a more general discriminant model, based on polychotomous logistic regression analysis. The 277 patients involved in the study belonged to diagnostic groups with sarcoidosis (n = 193), extrinsic allergic alveolitis (EAA; n = 39), and idiopathic pulmonary fibrosis (IPF; n = 45). The diagnosis had been established independently of the BALF-analysis results. The variables used to discriminate among these patient groups were the yield of recovered BALF, total cell count, and percentages of alveolar macrophages, lymphocytes, neutrophils, and eosinophils. In order to test the predictive power of the logistic model, we used 128 patients having sarcoidosis (n = 91), EAA (n = 5), or IPF (n = 32) from another hospital. In this test set the agreement of predicted with actual diagnostic-group membership was the same as in the learning set in which the logistic model was fitted: 94.5% of the cases were correctly classified. A validated computer program based on the polychotomous logistic regression model can be used to predict the diagnosis for an arbitrary patient with information provided by BALF analysis, and is thought to be of diagnostic value in patients suspected of having ILD.