Deconvolution of tracer and dilution data using the Wiener filter

The application of the Wiener filter to the deconvolution of tracer-type signals, i.e., any smooth concentration signals of biological origin, was investigated. This led to a more fundamental investigation of the Wiener filter itself, because tracer-type signals are invariably well described by parametric curves consisting of polynomials or sums of exponential functions. It is shown that better results are achieved with the Wiener filter if the model of the signal is not particularly accurate, whereas with a very accurate model it is better to deconvolve the model itself. The point at which the two deconvolution approaches perform comparably occurs when the error in the model is of a similar magnitude to the noise.