Intracranial internal carotid artery calcifications: association with vascular risk factors and ischemic cerebrovascular disease.

BACKGROUND AND PURPOSE: Atherosclerotic calcifications are present not only in the extracranial carotid bifurcation but also in the intracranial part of the internal carotid artery. We assessed the association between intracranial internal carotid artery calcifications and cardiovascular risk factors in patients with ischemic cerebrovascular disease and the association between calcifications and the presence of this disease. MATERIALS AND METHODS: Patients undergoing multidetector CT (MDCT) angiography of the carotid arteries for assessment of stenosis degree were included in the study. A semiautomatic custom-made system to quantify calcifications was developed. The associations between the volume of calcifications and cardiovascular risk factors and the type of ischemic cerebrovascular symptoms were assessed with logistic regression. RESULTS: MDCT angiography was performed in 406 patients (age, 62 +/- 14 years; 242 men). Men had a significantly higher calcification volume (66 mm(3)) than women (33 mm(3)). Calcification volume was positively associated with age in both men and women. Smoking, hypercholesterolemia, and a history of cardiac disease were independently related to the presence of calcifications. A history of cardiac disease and ischemic cerebrovascular disease were independently related to the volume of calcifications. No association was found between calcifications and the presence or type of ischemic cerebrovascular disease in the vascular territory of the intracranial internal carotid artery. CONCLUSIONS: Calcifications were associated with higher age and male gender. The presence and volume of calcifications were independently associated with cardiovascular risk factors. Calcifications were not related to the presence or type of ischemic cerebrovascular disease.

Multiple object tracking in molecular bioimaging by Rao-Blackwellized marginal particle filtering.

Time-lapse fluorescence microscopy imaging has rapidly evolved in the past decade and has opened new avenues for studying intracellular processes in vivo. Such studies generate vast amounts of noisy image data that cannot be analyzed efficiently and reliably by means of manual processing. Many popular tracking techniques exist but often fail to yield satisfactory results in the case of high object densities, high noise levels, and complex motion patterns. Probabilistic tracking algorithms, based on Bayesian estimation, have recently been shown to offer several improvements over classical approaches, by better integration of spatial and temporal information, and the possibility to more effectively incorporate prior knowledge about object dynamics and image formation. In this paper, we extend our previous work in this area and propose an improved, fully automated particle filtering algorithm for the tracking of many subresolution objects in fluorescence microscopy image sequences. It involves a new track management procedure and allows the use of multiple dynamics models. The accuracy and reliability of the algorithm are further improved by applying marginalization concepts. Experiments on synthetic as well as real image data from three different biological applications clearly demonstrate the superiority of the algorithm compared to previous particle filtering solutions.

Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images.

BACKGROUND: For the investigation of the molecular mechanisms involved in neurite outgrowth and differentiation, accurate and reproducible segmentation and quantification of neuronal processes are a prerequisite. To facilitate this task, we developed a semiautomatic neurite tracing technique. This article describes the design and validation of the technique. METHODS: The technique was compared to fully manual delineation. Four observers repeatedly traced selected neurites in 20 fluorescence microscopy images of cells in culture, using both methods. Accuracy and reproducibility were determined by comparing the tracings to high-resolution reference tracings, using two error measures. Labor intensiveness was measured in numbers of mouse clicks required. The significance of the results was determined by a Student t-test and by analysis of variance. RESULTS: Both methods slightly underestimated the true neurite length, but the differences were not unanimously significant. The average deviation from the true neurite centerline was a factor 2.6 smaller with the developed technique compared to fully manual tracing. Intraobserver variability in the respective measures was reduced by a factor 6.0 and 23.2. Interobserver variability was reduced by a factor 2.4 and 8.8, respectively, and labor intensiveness by a factor 3.3. CONCLUSIONS: Providing similar accuracy in measuring neurite length, significantly improved accuracy in neurite centerline extraction, and significantly improved reproducibility and reduced labor intensiveness, the developed technique may replace fully manual tracing methods.

Quantitative evaluation of convolution-based methods for medical image interpolation.

Interpolation is required in a variety of medical image processing applications. Although many interpolation techniques are known from the literature, evaluations of these techniques for the specific task of applying geometrical transformations to medical images are still lacking. In this paper we present such an evaluation. We consider convolution-based interpolation methods and rigid transformations (rotations and translations). A large number of sinc-approximating kernels are evaluated, including piecewise polynomial kernels and a large number of windowed sinc kernels, with spatial supports ranging from two to ten grid intervals. In the evaluation we use images from a wide variety of medical image modalities. The results show that spline interpolation is to be preferred over all other methods, both for its accuracy and its relatively low computational cost.

Reduction of patient motion artifacts in digital subtraction angiography: evaluation of a fast and fully automatic technique.

The performance of an automatic technique for the reduction of patient motion artifacts in digital subtraction angiography was evaluated. Four observers assessed the quality of 104 cerebral digital subtraction angiographic images that were processed by means of both the automatic technique and manual pixel shifting. The automatic technique resulted in better image quality and was considerably less time-consuming.

Retrospective motion correction in digital subtraction angiography: a review.

Digital subtraction angiography (DSA) is a well-established modality for the visualization of blood vessels in the human body. A serious disadvantage of this technique, inherent to the subtraction operation, is its sensitivity to patient motion. The resulting artifacts frequently reduce the diagnostic value of the images. Over the past two decades, many solutions to this problem have been put forward. In this paper, we give an overview of the possible types of motion artifacts and the techniques that have been proposed to avoid them. The main purpose of this paper is to provide a detailed review and discussion of retrospective motion correction techniques that have been described in the literature, to summarize the conclusions that can be drawn from these studies, and to provide suggestions for future research.

Image reconstruction by convolution with symmetrical piecewise nth-order polynomial kernels.

The reconstruction of images is an important operation in many applications. From sampling theory, it is well known that the sine-function is the ideal interpolation kernel which, however, cannot be used in practice. In order to be able to obtain an acceptable reconstruction, both in terms of computational speed and mathematical precision, it is required to design a kernel that is of finite extent and resembles the sinc-function as much as possible. In this paper, the applicability of the sine-approximating symmetrical piecewise nth-order polynomial kernels is investigated in satisfying these requirements. After the presentation of the general concept, kernels of first, third, fifth and seventh order are derived. An objective, quantitative evaluation of the reconstruction capabilities of these kernels is obtained by analyzing the spatial and spectral behavior using different measures, and by using them to translate, rotate, and magnify a number of real-life test images. From the experiments, it is concluded that while the improvement of cubic convolution over linear interpolation is significant, the use of higher order polynomials only yields marginal improvement.