Theodore Jackson

Volumetric Magnetic Resonance Imaging Evidence of Bilateral Hippocampal Atrophy in Mesial Temporal Lobe Epilepsy

Summary: Purpose: We measured absolute volumes and volume differences of hippocampi in patients with mesial temporal lobe epilepsy (MTLE) using volumetric magnetic resonance imaging (MRI) to determine the extent of bilateral atrophy in MTLE and to relate hippocampal volumes (HV) to outcome of temporal lobectomy.

Noninvasive quantitative evaluation of atherosclerosis using MRI and image analysis.

A new medical image analysis system to quantify atherosclerosis in the lower abdominal aorta using magnetic resonance imaging is described. This medical image analysis and display system permits the quantification of the three-dimensional (3D) properties of the vessel wall and lumen cross-sectional area and volumes. Preliminary results of employing this medical image analysis capability on magnetic resonance images demonstrated a twofold increase in wall volume per unit vessel length, corresponding to intimal thickening, before luminal narrowing was detected. This work demonstrated the feasibility and usefulness of quantitatively evaluating the 3D properties of the vessel lumen and wall by using a combination of magnetic resonance imaging and image analysis. The demonstration that intimal wall thickening is observed in images before observable occlusion of the lumen can be expected to provide an important early indicator of the future development of atherosclerosis. Such capability will permit detailed and quantitative studies to assess the effectiveness of therapies, such as drug, exercise, and dietary regimens.

Registration of functional. magnetic resonance imagery using mutual information

Accurate statistical correlation of brain activation in functional magnetic resonance image (MRI) studies depends on the reduction of artifacts induced by patient motion. We have addressed this problem in two ways. First, we have eliminated gross movement by the development of an immobilization mask. Second, we have implemented the image registration procedure, mutual information. This registration procedure is used to correct remaining misalignments due to patient motion. We have chosen maximization of mutual information because it is applicable to a broad range of image registration problems because it requires no segmentation, feature extraction, a priori information, or operator-assisted extractions. Initial results, as applied to fMRI data, are also presented. Our results indicate that we have reduced the motion artifacts present in our original data sets with sub-voxel accuracy.

A Multispectral Pattern Recognition System for the Noninvasive Evaluation of Atherosclerosis Utilizing MRI

This paper describes an image processing, pattern recognition and computer graphics system for the noninvasive identification and evaluation of atherosclerosis using multidimensional Magnetic Resonance Imaging (MRI). Particular emphasis has been placed on the problem of developing a pattern recognition system for noninvasively identifying the different plaque classes involved in atherosclerosis using minimal a priori information. This pattern recognition technique involves an extension of the ISODATA clustering algorithm to include an information theoretic criterion (Consistent Akaike Information Criterion) to provide a measure of the fit of the cluster composition at a particular iteration to the actual data. A rapid 3-D display system is also described for the simultaneous display of multiple data classes resulting from the tissue identification process. This work demonstrates the feasibility of developing a “high information content” display which will aid in the diagnosis and analysis of the atherosclerotic disease process. Such capability will permit detailed and quantitative studies to assess the effectiveness of therapies, such as drug, exercise and dietary regimens.

Diseased Tissue Identification And Quantification Utilizing MRI

This paper describes the development of a Physicians Workstation (PWS) for the evaluation of soft tissue types utilizing MR, which includes the analysis, quantification and 3-D visualization of the pertinent tissues. A major theme underlying our work is that it is possible to develop robust image processinglpattem recognition algorithms for the evaluation of diseased tissue by focusing on specific diseases involving localized regions of the body obtained with consistent and well developed imaging protocols. We have implemented two specific image processinglpattem recognition and display systems for (1) the evaluation of atherosclerosis; and (2) the differentiation of breast lesions.

Multispectral Tissue Identification In MR Images

Magnetic Resonance Imaging (MRI) is rapidly becoming accepted as a valuable diagnostic tool in modem day radiology. One of the most valuable aspects of MRI is that it provides multidimensional (i.e., multispectral) imagery which emphasizes different soft tissue characteristics. This paper describes our work regarding the development and exploration of techniques for the identification and characterization of tissues in such multispectral images. We have employed multiple approaches to this multispectral pattern problem which include the development of classifiers which utilize statistical and morphological information as well as artificial neural networks. The application of these because it permits the disease process to be followed over time in response to therapy, and provides quantitative information permitting the disease to be staged which is essential for surgical planning. In the case of MRI, the ability of the operator to control extrinsic pulse sequence parameters means that overlapping (multispectral) image planes can be obtained. Such MR images creates with different pulse sequences emphasize different tissue characteristics which provides a wealth of multispectral information which can be utilized for tissue identification and characterization. We have been interested in developing image processing and pattern recognition techniques for combining and quantifying information regarding different tissue types from MR multispectral imagery.

Segmentation Of The Abdominal Aorta From Transverse MR Images

An algorithm to automatically segment the aorta from transverse MR images has been developed as part of a system to non-invasively evaluate the progression of atherosclerosis. After MR images of the abdomen are acquired, the aorta is segmented from the image. The tissues in the aorta wall are classified and the extent of each stage of the disease is quantified. A threedimensional reconstruction of the classified aorta is used to visualized the tissues in the aorta wall.

Refinement Of Tissue Clusters In MR Images Using Spatial Information

Pixels in magnetic resonance (MR) imuges can be clustered into groups of tissues with similar MR characteristics using iterative methods. This paper presents an approach for refining cluster assignment in data sets where characteristics of various tissues overlap in feature space. Relaxation labeling has been used to incorporate spatial information into the clustering scheme such that the assignment of a pixel to a particular cluster depends not only on the pixels features but also on the features of its neighbors.