ArticleMolecular imaging and radionuclide therapy2022
Deconvolution of Tc-99m-Mercaptoacetyltriglycine Renograms with the Concomitant Use of a Sparse Legendre Polynomial Representation and the Moore-Penrose Pseudo-inverse.
Article in Molecular imaging and radionuclide therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Quantitative assessment of kidney split function and mean transit time in healthy patients using dynamicEJNMMI reports · 2024Article
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5 authors.
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Abstract
objectivesThis study aimed to introduce an improved deconvolution technique for Tc-99m-mercaptoacetyltriglycine renograms based on the combination of a sparse Legendre polynomial representation and the Moore-Penrose inversion matrix (LG). This method reduces the effect of noise on the measurement of renal retention function transit time (TT).
methodsThe stability and accuracy of the proposed method were tested using a renal database containing Monte Carlo-simulated studies and real adult patient data. Two clinical parameters, namely, split function (SF) and mean TT (meanTT), obtained with LG were compared with values calculated with the established method that combines matrix deconvolution and a three-point linear smoothing (F121) as recommended by the 2008 International Scientific Committee of Radionuclides in Nephrourology consensus on renal TT measurements.
resultsFor simulated data, the root mean square error (RMSE) between the theoretical non-noisy renal retention curve (RRC) and the results of the deconvolution methods applied to the noisy RRC were up to two times lower with LG (p<0.001). The RMSE of the reconvoluted renogram and the theoretical one was also lower for LG (p<0.001) and showed better preservation of the original signal. The SF was neither improved nor degraded by the proposed method. For patient data, no statistically significant difference was found between the SF for the LG method compared with the database values, and the meanTT better agreed with the physician's diagnosis than the matrix or clinical software (Hermes) outputs. A visual improvement of the RRC was also observed.
conclusionBy combining the sparse Legendre representation of the renogram curves and the Moore-Penrose matrix inverse techniques, we obtained improved noise reduction in the deconvoluted data, leading to better elimination of non-physiological signals -as negative values- and the avoidance of the smear effect of conventional smoothing on the vascular peak, which both influenced the meanTT measurement.
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