Mr. Hon-Chit Choi Profile

Hon-Chit Choi

at Univ of Sydney

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Proceedings Article | 16 March 2006 Paper

Fuzzy c-mean clustering on kinetic parameter estimation with generalized linear least square algorithm in SPECT

Hon-Chit Choi, Lingfeng Wen, Stefan Eberl, Dagan Feng

Proceedings Volume 6144, 61443Z (2006) https://doi.org/10.1117/12.653017

KEYWORDS: Single photon emission computed tomography, Cerebellum, Thalamus, Monte Carlo methods, Fuzzy logic, Data modeling, Brain, Reliability, Image analysis, Biological research

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Dynamic Single Photon Emission Computed Tomography (SPECT) has the potential to quantitatively estimate physiological parameters by fitting compartment models to the tracer kinetics. The generalized linear least square method (GLLS) is an efficient method to estimate unbiased kinetic parameters and parametric images. However, due to the low sensitivity of SPECT, noisy data can cause voxel-wise parameter estimation by GLLS to fail. Fuzzy C-Mean (FCM) clustering and modified FCM, which also utilizes information from the immediate neighboring voxels, are proposed to improve the voxel-wise parameter estimation of GLLS. Monte Carlo simulations were performed to generate dynamic SPECT data with different noise levels and processed by general and modified FCM clustering. Parametric images were estimated by Logan and Yokoi graphical analysis and GLLS. The influx rate (K₁), volume of distribution (V_d) were estimated for the cerebellum, thalamus and frontal cortex. Our results show that (1) FCM reduces the bias and improves the reliability of parameter estimates for noisy data, (2) GLLS provides estimates of micro parameters (K₁-k₄) as well as macro parameters, such as volume of distribution (Vd) and binding potential (BP₁ & BP₂) and (3) FCM clustering incorporating neighboring voxel information does not improve the parameter estimates, but improves noise in the parametric images. These findings indicated that it is desirable for pre-segmentation with traditional FCM clustering to generate voxel-wise parametric images with GLLS from dynamic SPECT data.

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