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The generalized linear least square (GLLS) method can successfully construct unbiased parametric images from dynamic positron emission tomography data. Quantitative dynamic single photon emission computed tomography (SPECT) also has the potential to generate physiological parametric images. However, the high level of noise, intrinsic in SPECT, can give rise to unsuccessful voxelwise fitting using GLLS, resulting in physiologically meaningless estimates. In this paper, we systematically investigated the applicability of our recently proposed approaches to improve the reliability of GLLS to parametric image generation from noisy dynamic SPECT data. The proposed approaches include use of a prior estimate of distribution volume (V(d)), a bootstrap Monte Carlo (BMC) resampling technique, as well as a combination of both techniques. Full Monte Carlo simulations were performed to generate dynamic projection data, which were then reconstructed with and without resolution recovery, before generating parametric images with the proposed methods. Four experimental clinical datasets were also included in the analysis. The GLLS methods incorporating BMC resampling could successfully and reliably generate parametric images. For high signal-to-noise ratio (SNR) imaging data, the BMC-aided GLLS provided the best estimates of K(1) , while the BMC-V(d)-aided GLLS proved superior for estimating V(d). The improvement in reliability gained with BMC-aided GLLS in low SNR image data came at the expense of some overestimation of V(d) and increased computation time.

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.

Dynamic single photon emission computed tomography (SPECT) has demonstrated the potential to quantitatively estimate physiological parameters in the brain and the heart. The generalized linear least square (GLLS) method is a well-established method for solving linear compartment models with fast computational speed. However, the high level of noise intrinsic in the SPECT data leads to reliability and instability problems of GLLS for generating parametric images. An integrated method is proposed to restrict the noise in both the temporal and spatial domains to estimate multiple parametric images for dynamic SPECT. This method comprises three steps which are optimum image sampling schedule in the projection space, cluster analysis applied postreconstruction and parametric image generation with GLLS. The simulation and experimental studies for the neuronal nicotine acetylcholine receptor tracer of 5-[123I]-iodo-A-85380 were employed to evaluate the performance of the proposed method. The results of influx rate of K1 and volume of distribution of Vd demonstrated that the integrated method was successful in generating low noise parametric images for high noise SPECT data without enhancing the partial volume effect. Furthermore, the integrated method is computationally efficient for potential clinical applications.

Parametric images derived in functional imaging can visualize the spatial distribution of physiological parameters in vivo. However, the high level of noise intrinsic in single photon emission computed tomography (SPECT) may lead to physiologically meaningless parameter estimates such as negative kinetic rate constants using the generalized linear least squares (GLLS) method for compartmental model fitting. In this study, an enhanced GLLS method using fast regressive adjustment of parameters was investigated for improving the reliability of GLLS applied to dynamic SPECT data. Monte Carlo simulation data were used to systematically evaluate accuracy and reliability of derived parametric images. The simulation and experimental results demonstrate that the enhanced GLLS method can achieve more reliable parametric images, while largely preserving computational efficiency.

The generalized linear least squares (GLLS) method has been shown to successfully construct unbiased parametric images from dynamic positron emission tomography (PET). However, the high level of noise intrinsic in single photon emission computed tomography (SPECT) can give rise to unsuccessful voxel-wise fitting using GLLS, resulting in physiologically meaningless estimates, such as negative kinetic parameters for compartment models. In this study, three approaches were investigated to improve the reliability of GLLS applied to dynamic SPECT data. The simulation and experimental results showed that GLLS with the aid of Bootstrap Monte Carlo method proved successful in generating parametric images and preserving all of the major advantages of all the originally GLLS method, although at the expense of increased computation time.