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.

3DVJEWNIX is a data-, machine-, and application-independent 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 it just as convenient to process 2D and 3D data as it is to process 4D data. It is based on UNIX, C, X-Window and our own multidimensional generalization of the 2D ACR-NEMA standards for image data representation.

Because administration for 1 week of the GnRH antagonist Nal-Glu GnRH had been shown to decrease FSH secretion from supranormal to normal in men with gonadotroph adenomas, we investigated the effect of prolonged administration of Nal-Glu on the size of gonadotroph adenomas. To quantitate the effect of Nal-Glu GnRH on gonadotroph adenoma size, we first developed a technique for calculating adenoma volume. The technique involved collecting magnetic resonance (MR) imaging data from each adenoma at 1-mm slice intervals in the coronal, axial, and sagittal views and using the Softvu computer program to calculate adenoma volume from the MR data. The precision of this technique, as judged by the coefficients of variation of the calculations of the same view of the same study three times, was 1.7%, 1.0%, and 1.0% for each of three studies. When Nal-Glu GnRH (5 mg, sc, every 12 h) was self-administered for 3-12 months to five men with gonadotroph adenomas and supra-normal serum FSH concentrations, the serum FSH concentrations decreased to normal or below normal for the entire treatment period. Adenoma size, however, did not change during treatment in any of the five men. We conclude that calculating pituitary adenoma volume from MR data using the Softvu computer program is a highly reproducible technique, but that Nal-Glu GnRH is not an effective treatment for reducing gonadotroph adenoma size. The failure of Nal-Glu to reduce adenoma size despite its success in reducing FSH secretion suggests that FSH secretion from gonadotroph adenomas is dependent on endogenous GnRH, but growth of gonadotroph adenomas is not.

Three-dimensional (3D) visualization in biomedical and other imaging areas is a rapidly emerging discipline. The major developments in this field are described in a unified and concise way. To this end, we introduce an operator notation to describe the basic imaging transforms commonly used in 3D visualization and to identify a comprehensive set of basic transforms. We also introduce several new basic transforms for filtering and interpolating scenes and structures and for rendering surfaces and volumes. We demonstrate not only how the existing visualization methodologies can be described concisely, but we also show how a great variety of new methodologies can be generated using both the existing imaging transforms and the new transforms introduced in this paper. A comprehensive evaluation method to compare objectively rendering methods used in visualization based on task-specific mathematical phantoms is described. We examine in detail separate transform sequences that are best suited for rendering robust and frail structures (ie, structures with well- and poorly defined boundaries).

Multidimensional image data are becoming increasingly common in biomedical imaging. Three-dimensional visualization and analysis techniques based on three-dimensional image data have become an established discipline in biomedicine. Some imaging problems generate image data of even higher dimensions. It often becomes necessary, rather than just convenient, to consider the higher-dimensional data as a whole to adequately answer the underlying imaging questions. Despite this established need for convenient exchange of ima9e and image-derived information, no exchange protocols are available that adequately meet the needs of multidimensional imaging systems. This paper describes an exchange protocol that has been desi9ned after careful consideration of the common requirements of methodologies for visualization and analysis of multidimensional data. Ir is based on and is a generalization of the widely accepted American College of Radiology-National Electrical Manufacturers Association (ACR-NEMA) standards specified for two-dimensional images. It is implemented and actively being used in a data-, application-, and machine-independent software environment, being developed in the authors' department, for the visualization and analysis of muItidimensional images.