Effect of geometrical distortion correction in MR on image registration accuracy.

In this article we investigate the effect of geometrical distortion correction in MR images on the accuracy of the registration of X-ray CT and MR head images for both a fiducial marker (extrinsic point) method and a surface-matching technique. We use CT and T2-weighted MR image volumes acquired from seven patients who underwent craniotomies in a stereotactic neurosurgical clinical trial. Each patient had four external markers attached to transcutaneous posts screwed into the outer table of the skull.

Registration of 3-D images using weighted geometrical features.

The authors present a weighted geometrical feature (WGF) registration algorithm. Its efficacy is demonstrated by combining points and a surface. The technique is an extension of Besl and McKay's (1992) iterative closest point (ICP) algorithm.

The importance of ray pathlengths when measuring objects in maximum intensity projection images.

It is important to understand any process that affects medical data. Once the data have changed from the original form, one must consider the possibility that the information contained in the data has also changed. In general, false negative and false positive diagnoses caused by this post-processing must be minimized.

Accurate localization of cortical convolutions in MR brain images.

Analysis of brain images often requires accurate localization of cortical convolutions. Although magnetic resonance (MR) brain images offer sufficient resolution for identifying convolutions in theory, the nature of tomographic imaging prevents clear definition of convolutions in individual slices. Existing methods for solving this problem rely on heuristic adaptation of brain atlases created from a small number of individuals.

Adaptive signal analysis and interpretation for real-time intelligent patient monitoring.

On-line intelligent monitoring, diagnosis, and control of dynamic systems such as patients in intensive care units necessitates the context-dependent acquisition, processing, analysis, and interpretation of large amounts of possibly noisy and incomplete data. The dynamic nature of the process also requires a continuous evaluation and adaptation of the monitoring strategy to respond to changes both in the monitored patient and in the monitoring equipment. Moreover, real-time constraints may imply data losses, the importance of which has to be minimized.

Morphometric analysis of white matter lesions in MR images: method and validation.

The analysis of MR images is evolving from qualitative to quantitative. More and more, the question asked by clinicians is how much and where, rather than a simple statement on the presence or absence of abnormalities. The authors present a study in which the results obtained with a semiautomatic, multispectral segmentation technique are quantitatively compared to manually delineated regions.

Brain segmentation and white matter lesion detection in MR images.

This paper presents a review of methods and techniques that have been proposed for the segmentation of magnetic resonance (MR) images of the brain, with a special emphasis on the segmentation of white matter lesions. First, artifacts affecting MR images (noise, partial volume effect, and shading artifact) are reviewed and methods that have been proposed to correct for these artifacts are discussed. Next, a taxonomy of generic segmentation algorithms is presented, categorized as region-based, edge-based, and classification algorithms.

Automatic detection of intracranial contours in MR images.

Segmentation of the intracranial cavity in medical images is valuable in several research areas such as the quantitative analysis of normal and abnormal brain tissues, the registration of different imaging modalities (MRI, PET, CT) based on surface models of the brain, and the rendering of volume data. Because the manual delineation of the brain contour in the images can be demanding and error prone, an automatic procedure to perform this task is desirable. We have developed and tested a robust method that permits the automatic detection of the intracranial contour in transverse MR images.

Model-based diagnosis in intensive care monitoring: the YAQ approach.

YAQ is an ontology for model-based reasoning in physiologic domains. YAQ is based on a hybrid algebra of qualitative and numerical values, and is designed to benefit from the rich and ever-changing nature of information available in a critical care monitoring environment. The focus of the project is on diagnosis of clinical conditions, prediction of the effects of therapy, and therapy management assistance.

Correction of intensity variations in MR images for computer-aided tissue classification.

A number of supervised and unsupervised pattern recognition techniques have been proposed in recent years for the segmentation and the quantitative analysis of MR images. However, the efficacy of these techniques is affected by acquisition artifacts such as inter-slice, intra-slice, and inter-patient intensity variations. Here a new approach to the correction of intra-slice intensity variations is presented.

Neural-network-based segmentation of multi-modal medical images: a comparative and prospective study.

This work presents an investigation of the potential of artificial neural networks for classification of registered magnetic resonance and X-ray computer tomography images of the human brain. First, topological and learning parameters are established experimentally. Second, the learning and generalization properties of the neural networks are compared to those of a classical maximum likelihood classifier and the superiority of the neural network approach is demonstrated when small training sets are utilized.

Knowledge-based approach to sleep EEG analysis--a feasibility study.

A knowledge-based approach to automated sleep EEG (electroencephalogram) analysis is described. In this system, an object-oriented approach is followed in which specific waveforms and sleep stages ("objects") are represented in terms of frames. The latter capture the morphological and spatio-temporal information for each object.

Distribution of vascular resistance in terminal arteriolar networks of cat sartorius muscle.

Morphometric information on the terminal arteriolar networks (n = 10) in cat sartorius muscle [Koller et al., Am. J.

Analysis of vascular pattern and dimensions in arteriolar networks of the retractor muscle in young hamsters.

A quantitative analysis of the distribution of microvascular blood flow and oxygen delivery requires a detailed description of the vascular network geometry. The distributions of lengths and diameters were determined in terminal arteriolar networks of the cheek pouch retractor muscle of young (34 +/- 2 days) hamsters. We compared the Strahler centripetal vessel ordering scheme, which assigns lowest order to the capillaries and proceeds upstream toward the larger vessels, with the centrifugal ordering scheme, which begins with the input arteriole and proceeds downstream toward the capillaries.

Quantitative analysis of arteriolar network architecture in cat sartorius muscle.

The geometry of the arteriolar network is one of the major determinants of blood flow distribution within a tissue. The purpose of this study was to describe the distribution of geometrical variables (lengths, diameters) as well as the pattern of branching in the nonarcading portion of the arteriolar network in skeletal muscle. The exteriorized cat sartorius muscle was used as the experimental model.

Effect of dispersion of vessel diameters and lengths in stochastic networks. II. Modeling of microvascular hematocrit distribution.

A microvascular network model described in the preceding paper (B. Dawant, M. Levin, and A.

Effect of dispersion of vessel diameters and lengths in stochastic networks. I. Modeling of microcirculatory flow.

A microvascular network model is proposed with random arrangement and random dimensions of vessels. In addition to stochasticity of the topological characteristics of the model networks, as previously introduced by Fenton and Zweifach (1981, Ann. Biomed.