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3DV1EWNIX is a data-, machine., and applicationindependent software system, developed and maintained on an ongoing basis by the Medical Image Processing Group. It is aimed at serving the needs of biomedical visualization researchers as well as biomedical end users. 3DVIEWNIX is not designed around a fixed methodology or a set of methods packaged in a fixed fashion for a fixed application. Instead, we have identified and incorporated in 3DVIEWNIX a set of basic imaging transforms that are required in most visualization, manipulation, and analysis methods. The result is a powerful exploratory environment that provides not only the commonly used standard tools but also an immense variety of others. In addition to visualization, it incorporates a variety of multidimensional structure manipulation and analysis methods. We have tried to make its design as much as possible image.dimensionality-independent to make itjust as convenient to process 2D and 3D data as it is to process 4D data. It is based on UNIX, C, XWindow and our own mulddimensional generalization of the 2D ACR-NEMA standards for image data representation. It is distributed with source code in an open fashion. A single source code version is installed on a variety of computing platforms. It is currently in use worldwide.

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The relative performance of five fully 3D PET reconstruction algorithms is evaluated. The algorithms are a filtered backprojection (FBP) method and two variants each of the EM-ML and ART iterative methods. For each of the iterative methods, one variant makes use of voxels and the other makes use of 'blobs' (spherically symmetric functions smoothly decaying to zero at their boundaries) as basis functions in its discrete reconstruction model. The methods are evaluated from the point of view of the efficacy of the reconstructions produced by them for three typical medical tasks--estimation of the average activity inside specific regions of interest, detection of hot spots, and detection of cold spots. A free parameter is allowed in the description of each of the five algorithms; the parameters are determined by a training process during which a value of the free parameter is selected which (nearly) maximizes a technical figure of merit. Such training and the actual comparative evaluation is done by making use of randomly generated phantoms and their projection data. The methodology allows assignation of levels of statistical significance to claims of the relative superiority of one algorithm over another for a particular task. We find that using blobs as basis functions in the iterative algorithms is definitely advantageous over using voxels. This result has high statistical significance. (We also include a visual illustration of it.) Comparing FBP, EM-ML using blobs, and ART using blobs, we do not find a clear difference in the overall performance of the investigated variants of the methods. If anything, our results suggest that ART using blobs may be the most efficacious of the three.