Legendre Polynomials: A Fully Automatic Method for Noise Reduction in Tc-Mercaptoacetyltriglycine Renogram Analysis.
(2020) Journal of nuclear medicine technology — Vol. 48, n° 4, p. 346-353 (2020)
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- Our purpose was to develop a fully automatic method to deal with the presence of high levels of noise interfering with quantitative analysis of fast, dynamic mercaptoacetyltriglycine renogram images. A method based on Legendre polynomials to fit and filter time-activity curves was proposed. The method was applied to a renal database that contains Monte Carlo (MC)-simulated studies and real adult patient data. Clinically relevant parameters such as relative function, time to maximum uptake (Tmax), and half-emptying time (T1/2) were obtained with the proposed method, the 1-2-1 filter (F121) method recommended in the 2018 guidelines of the European Association of Nuclear Medicine, and a state-of-the-art commercial software program (Hermes) currently used in routine nuclear medicine. The root mean squared error between reference time-activity curves and the same curves with Poisson noise added was about 2 times lower for the Legendre method than for F121. The left relative function for MC and patient data was statistically equivalent for Hermes, Legendre, and F121 ( < 0.001). For MC studies, the Legendre technique performed better that the Hermes method regarding the known values of Tmax ( < 0.05), and the T1/2 determination was significantly improved ( < 0.05). For patient data, the Legendre and F121 methods were less influenced by noise in the data than the Hermes method, particularly for T1/2. In dynamic nuclear medicine imaging, Legendre polynomials appear to be a promising, fully automatic noise-removal tool that is routinely applicable, accurate, and robust.
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DESTINÉ, M., & Seret, A. (2020). Legendre Polynomials: A Fully Automatic Method for Noise Reduction in Tc-Mercaptoacetyltriglycine Renogram Analysis. Journal of nuclear medicine technology, 48(4), 346-353. https://doi.org/10.2967/jnmt.120.244574 (Original work published 2020)