Constrained least-squares restoration and renogram deconvolution: a comparison with other techniques

It has previously been shown that an iterative constrained least-squares (CLSR) technique using a noise-based constraint may be superior to other methods of renogram deconvolution analysis. To test this hypothesis on real data, renography was performed on 70 patients with established diagnoses of normal, insufficient or acutely obstructed kidneys. Standard renography parameters were obtained from the time activity curves which were then deconvolved using three techniques. One kidney per patient was chosen at random for analysis resulting in a total of 43 normal and 27 diseased kidneys. The ability of each of the analytical techniques to discriminate between normal and diseased kidneys was assessed using logistic regression. CLSR proved to be robust and provide the best discrimination of the deconvolution techniques. However, the best overall discrimination was provided by a model based on the renography parameters excretion ratio, rate of uptake and time to peak activity which correctly classified 86% of the kidneys. It is possible that the renogram parameters could be used to produce notional probabilities of renal dysfunction which the physician could use as an aid in the interpretation of gamma-camera renography.