Abstract-A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, Mutual Information or relative entropy, as a new matching criterion. The method presented in this paper applies Mutual Information to measure the statistical dependence or information redundancy between the image intensities of corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned. Maximization of Mutual Information is a very general and powerful criterion, because no assumptions are made regarding the nature of this dependence and no limiting constraints are imposed on the image content of the modalities involved. The accuracy of the mutual information criterion is validated for rigid body registration of CT, MR and PET images by comparison with the stereotactic registration solution, while robustness is evaluated with respect to implementation issues, such as interpolation and optimization, and image content, including partial overlap and image degradation. Our results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction or other pre-processing steps, which makes this method very well suited for clinical applications.

We describe a fully automated method for model-based tissue classification of magnetic resonance (MR) images of the brain. The method interleaves classification with estimation of the model parameters, improving the classification at each iteration. The algorithm is able to segment single- and multispectral MR images, corrects for MR signal inhomogeneities, and incorporates contextual information by means of Markov random Fields (MRF's). A digital brain atlas containing prior expectations about the spatial location of tissue classes is used to initialize the algorithm. This makes the method fully automated and therefore it provides objective and reproducible segmentations. We have validated the technique on simulated as well as on real MR images of the brain.

Our results indicate that retrospective techniques have the potential to produce satisfactory results much of the time, but that visual inspection is necessary to guard against large errors.

Abstract-We propose a model-based method for fully automated bias field correction of MR brain images. The MR signal is modeled as a realization of a random process with a parametric probability distribution that is corrupted by a smooth polynomial inhomogeneity or bias field. The method we propose applies an iterative expectation-maximization (EM) strategy that interleaves pixel classification with estimation of class distribution and bias field parameters, improving the likelihood of the model parameters at each iteration. The algorithm, which can handle multichannel data and slice-by-slice constant intensity offsets, is initialized with information from a digital brain atlas about the a priori expected location of tissue classes. This allows full automation of the method without need for user interaction, yielding more objective and reproducible results. We have validated the bias correction algorithm on simulated data and we illustrate its performance on various MR images with important field inhomogeneities. We also relate the proposed algorithm to other bias correction algorithms.Index Terms-Bias field, digital brain atlas, MRI, tissue classification.

The non-invasive quantification of regional myocardial function is an important goal in clinical cardiology. Myocardial thickening/thinning indices is one method of attempting to define regional myocardial function. A new ultrasonic method of quantifying regional deformation has been introduced based on the principles of 'strain' and 'strain rate' imaging. These new imaging modes introduce concepts derived from mechanical engineering which most echocardiographers are not familiar with. In order to maximally exploit these new techniques, an understanding of what they measure is indispensable. This paper will define each of these modalities in terms of physical principles and will give an introduction to the principles of data acquisition and processing required to implement ultrasonic strain and strain rate imaging. In addition, the current status of development of the technique and its limitations will be discussed, together with examples of potential clinical applications.