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R J Goncalves

Publications and source records attributed to R J Goncalves.

4 recordsLinked to original sources

New technique for quantitation of pituitary adenoma size: use in evaluating treatment of gonadotroph adenomas with a gonadotropin-releasing hormone antagonist.

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.

Adenoma↗

Medical image rendering.

Three-dimensional (3-D) visualization has recently become an established discipline in medicine. Although numerous visualization methods are currently available, a unified framework to describe and study them has been lacking. Often, the functionally independent operations in a method are integrated among themselves or with the method itself for computational efficiency. The two main aims of this article are (1) to review the methods in a unified way in a general setting so that it becomes possible to appreciate the interrelationship and interdependence of methods, and (2) to show how a variety of new methods emerge with potentially improved renditions in this unified treatment. To this end, we introduce an operator notation to describe concisely the basic 3-D imaging transforms commonly used in visualization and identify a comprehensive set of basic transforms. We describe several new basic transforms for filtering and interpolating structures and scenes, and for rendering surfaces and volumes. We show the power of the principle of treating 3-D imaging methodologies as comprising an appropriate combination of the basic operators. We show how such a treatment leads to a great variety of new rendering methods and how many such methods can lead to improved portrayal. We develop separate transform sequences to optimally render robust and frail structures (ie, structures represented in scenes with well-defined and ill-defined boundaries, respectively).

Data Display↗