Physiological information about an organ may be assessed from the retention function as derived by deconvolution analysis. However, noise in data may cause distortion of the retention curve and potentially induce methodological errors. To take full advantage of all parts of the retention function, i.e. even the vascular part, we have developed a non-linear noise reduction algorithm. The algorithm is an adaptive polynomial fit (APF) in a sliding segment over the renogram to be deconvoluted. Each segment is modelled by the lowest polynomial resulting in a root of mean square error lower than a pre-set value. APF was tested in comparison with conventional repetitive 1:2:1 smoothing and rectangular window smoothing, using a set of simulated retention functions and corresponding renograms with superimposed artificial noise. The outcome was evaluated by comparing the generated retention functions with the simulated ones. The conventional smoothing algorithms, as well as APF, induced some distortion of the retention function, but the deviation from the true retention function is essentially lower in the case of APF. In addition, APF seems to be more robust in cases of essentially reduced renal function and thereby relative high noise levels. APF reduces noise in data, leading to retention functions with reliable information in terms of a tracer's first passage, uptake and outflow.