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

Abstract

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.

Optimal dose of Gd‐DTPA in dynamic MR studies

“…We obtained functions h r (t) separately for the cortical and medullary compartments by a deconvolution algorithm based on singular value decomposition (SVD). A problem central to the deconvolution is its poor numerical stability (13,14). The SVD technique for solving a linear system allows one to control the smoothness of the solution (2).…”

“…with similar equations applied to variables SЈ, TЈ, R. Since for small values of ⌬S: [14] provided other errors are zero, we refer to factors a S , a SЈ , a TЈ , and a R as error amplification factors. Similarly, we use symbols b S , b SЈ , b TЈ , and b R to denote the error amplification factors corresponding to independent variables S, SЈ, TЈ, R and the relative error in glomerular filtration rate G. The differentiation of Eq.…”

Noise reduction of renograms: a new algorithm applied to simulated renograms for evaluation of the renal retention function

“…has been addressed by Tawatchai et al (1994), who applied a large set of different Butterworth ®lters when analysing the vascular phase of the RRF. Sutton & Kempi, 1992, 1993 proposed instead a constrained least-squares restoration (CLSR) deconvolution algorithm based on the FT, and employed a Wiener ®lter to handle the noise. They reported results superior to the outcome of 3-point smoothing, although the 3-point smoothings were not investigated with respect to the number of smoothings.…”

Logistic Regression Analysis

“…Recently, it was shown that renogram parameters can be used to objectively separate kidneys with dysfunction from those with normal function with an overall accuracy of 86% (7). In that study, the kidneys were divided into two broad groups: normal and pathological.…”

“…This classification strategy allows an assessment to be made of the ability of standard renography and deconvo lution to discriminate between normal functioning kidneys and different types of kidney dysfunction. Moreover, in the interest of completeness, a number of curve parameters not considered in the previous study (7) have been in cluded in this analysis.…”