Although inulin clearance measured during constant infusion is still considered the reference method, single-injection of 51Cr-EDTA with subsequent plasma sampling has become the most popular technique for the routine assessment of glomerular filtration rate. Despite the fact that the technique has been in use for 30 years, there are only a few reports of normal values calculated directly from 51Cr-EDTA data and normal ranges have generally been produced by conversions of inulin data. The aim of this study was to measure the variation in total plasma clearance, calculated directly from 51Cr-EDTA measurements, in normal males and females, of Saudi Arabian origin, over a wide range. Altogether, 201 potential kidney donors aged 16-60 years were studied. No statistically significant association of total plasma clearance with age or sex could be demonstrated; however, predictive equations suggesting a small decline in total plasma clearance with age were developed. The figures presented suggest that the reduction in total plasma clearance of 51Cr-EDTA with age is relatively shallow up to the age of at least 60 years and that normal ranges produced by conversion of inulin data may overestimate the decline with age.

Identification of hypoperfused areas in myocardial perfusion single-photon emission tomography studies can be aided by bull's-eye representation of raw counts, lesion extent and lesion severity, the latter two being produced by comparison of the raw bull's-eye data with a normal data base. An artificial intelligence technique which is presently becoming widely popular and which is particularly suitable for pattern recognition is that of artificial neural network. We have studied the ability of feed forward neural networks to extract patterns from bull's-eye data by assessing their capability to predict lesion presence without direct comparison with a normal data base. Studies were undertaken on both simulation data and on real stress-rest data obtained from 410 male patients undergoing routine thallium-201 myocardial perfusion scintigraphy. The ability of trained neural networks to predict lesion presence was quantified by calculating the areas under receiver operating characteristic curves. Figures as high as 0.96 for non-preclassified patient data were obtained, corresponding to an accuracy of 92%. The results demonstrate that neural networks can accurately classify patterns from bull's-eye myocardial perfusion images and detect the presence of hypoperfused areas without the need for comparison with a normal data base. Preliminary work suggests that this technique could be used to study perfusion patterns in the myocardium and their correlation with clinical parameters.

For 15 years, perfusion indices derived from scintigraphic studies have proved useful in the serial evaluation of renal transplants and they have recently been confirmed as being more sensitive than Doppler ultrasound as an indicator of vascular rejection and cyclosporin toxicity. In the calculation of these indices, correction for administered activity is often accomplished using activity measurements made over a convenient artery which, therefore, has a critical influence on the value of the index obtained. In this communication, a theoretical assessment is made of the error and variability introduced into the calculation of the perfusion index, because of inadequate spatial sampling of activity in these narrow arteries and the consequential inconsistencies in the measurement of the arterial tracer activity. Using numerical simulation, it is shown that the errors in repeat studies on the same patient may be as high as 39% and between patients as high as 53%. These figures can be reduced to below 18% and 21%, respectively, by constructing a region of interest (ROI) to extend over as much of the arterial width as possible rather than relying only on the maximum pixel count. Further reduction to below 12% and 10% is possible by utilising a 128 x 128 acquisition matrix instead of 64 x 64 and drawing the ROI over the aorta instead of the iliac artery.

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