Constrained least-squares restoration and renogram deconvolution: a comparison by simulation

Before deconvolution can be used in renography, it is necessary to decide whether the renal function is sufficiently good to allow it. To see if this decision can be circumvented, an iterative constrained least-squares restoration (CLSR) method was implemented in which the point of termination of the iteration occurs when a residual vector has a value less than an estimate of the noise in the original renogram curve. The technique was compared with the matrix algorithm and with direct FFT division. The comparison was achieved by deconvolving simulated renogram data with differing transit time spectra and statistics. As expected, the FFT technique produced results of little value whereas the CLSR and matrix methods produced values of mean transit time (MTT) that differed slightly from the expected results. Analysis indicated that the matrix approach was superior when the percentage noise component was less than 6% and vice versa. No technique produced useful transit time spectra. As the CLSR technique produced better results than the matrix method in simulations with relatively long MTTs and high noise, it seems reasonable to suggest that it might be used for renogram deconvolution without the need for previous inspection of the curves.