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A new image analysis method, called contiguous volume analysis, has been developed to automatically extract 3D information from emission images. The method considers volumes of activity and display,s data about them in a format which allows quantitative image comparison. This method of numerical analysis enables us to show, for example, whether or not information has been gained, lost or changed through the use of different filters and different attenuation and scatter correction, and reconstruction algorithms. Since the analysis method is consistent with a visual inspection of the data, intuitive insights into the meaning of the data are possible, allowing a better understanding of the effects of the different image processing techniques on the images. The method can be used to find patterns of activity in sets of images, and may be used to quantify noise, allowing an objective determination of which volumes in an image are meaningful.

To segment brain tissues in magnetic resonance images of the brain, we have implemented a stochastic relaxation method which utilizes partial volume analysis for every brain voxel, and operates on fully three-dimensional (3-D) data. However, there are still problems with automatically or semi-automatically segmenting thick magnetic resonance (MR) slices, particularly when trying to segment the small lesions present in MR images of multiple sclerosis patients. To improve lesion segmentation we have extended our method of stochastic relaxation by both pre- and post-processing the MR images. The preprocessing step involves image enhancement using homomorphic filtering to correct for nonhomogeneities in the coil and magnet. Because approximately 95% of all multiple sclerosis lesions occur in the white matter of the brain, the post-processing step involves application of morphological processing and thresholding techniques to the intermediate segmentation in order to develop a mask image containing only white matter and Multiple Sclerosis (MS) lesion. This white/lesion masked image is then segmented by again applying our stochastic relaxation technique. The process has been applied to multispectral MRI scans of multiple sclerosis patients and the results compare favorably to manual segmentations of the same scans obtained independently by radiology health professionals.

We describe two methods to correct for motion artifacts in head images obtained by positron emission tomography (PET). The methods are based on six-dimensional motion data of the head that have to be acquired simultaneously during scanning. The data are supposed to represent the rotational and translational deviations of the head as a function of time, with respect to the initial head position. The first compensation method is a rebinning procedure by which the lines of response are geometrically transformed according to the current values of the motion data, assuming a cylindrical scanner geometry. An approximation of the rebinning transformations by use of large look-up tables, having the potential of on-line event processing, is presented. The second method comprises post-processing of the reconstructed images by unconstrained or constrained de-convolution of the image or image segments with kernels that are generated from the motion data. We use motion data that were acquired with a volunteer in supine position, immobilized by a thermoplastic head holder, to demonstrate the effects of the compensation methods. Preliminary results obtained with test data indicate that the methods have the potential to improve the resolution of Positron Emission Tomography images in cases where significant head motion has occurred, provided that the head position and orientation can be accurately measured.