RESUMEN
Functional imaging with PET and SPECT is capable of visualizing subtle changes in physiological function in vivo, which aids in the early diagnosis of disease. Quantitative functional parameters are usually derived by curve fitting the dynamic data of a functional imaging study. However, the intrinsic high level of noise and low signal to noise ratio can lead to instability in the parameter estimation and give rise to non-physiological parameter estimates. Clustering techniques have been applied to improve signal to noise ratio and the reliability of parametric image generation, but these may enhance partial volume effects (PVE) and result in biased estimates for small structures. Therefore, a systematic study was performed using computer simulations of SPECT data and the generalized linear least square algorithm (GLLS) to evaluate the ability of three proposed enhanced methods and a clustering-aided method to improve the reliability of parametric image generation. The results demonstrate that clustering with sufficient cluster numbers ameliorated PVE and provided noise-insensitive parameter estimates. The enhanced GLLS method with a prior volume of distribution and bootstrap Monte Carlo resampling improved the reliability of the curve fitting, and is thus suitable for application to noisy SPECT data.
Asunto(s)
Algoritmos , Interpretación Estadística de Datos , Tomografía Computarizada de Emisión de Fotón Único , Simulación por Computador , Interpretación de Imagen Asistida por Computador/normas , Modelos Biológicos , Modelos Estadísticos , Método de Montecarlo , Tomografía de Emisión de Positrones , Sensibilidad y Especificidad , Tomografía Computarizada de Emisión de Fotón Único/normas , Tomografía Computarizada de Emisión de Fotón Único/estadística & datos numéricosRESUMEN
Medical parametric imaging with dynamic positron emission tomography (PET) plays an increasingly potential role in modern biomedical research and clinical diagnosis. The key issue in parametric imaging is to estimate parameters based on sampled data at the pixel-by-pixel level from certain dynamic processes described by valid mathematical models. Classic nonlinear least squares (NLS) algorithm requires a "good" initial guess and the computational time-complexity is high, which is impractical for image-wide parameter estimation. Although a variety of fast parametric imaging techniques have been developed, most of them focus on single input systems, which do not provide an optimal solution for dual-input biomedical system parameter estimation, which is the case of liver metabolism. In this study, a dual-input-generalized linear least squares (D-I-GLLS) algorithm was proposed to identify the model parameters including the parameter in the dual-input function. Monte Carlo simulation was conducted to examine this novel fast algorithm. The results of the quantitative analysis suggested that the proposed technique could provide comparable reliability of the parameter estimation with NLS fitting and accurately identify the parameter in the dual-input function. This method may be potentially applicable to other dual-input biomedical system parameter estimation as well.