Enhanced parameter estimation methods for noisy SPECT data.
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.
Wen
Lingfeng
L
Biomedical and Multimedia Information Technology Group, School of Information Technologies, The University of Sydney, Sydney, Australia. wenlf@it.usyd.edu.au
Eberl
Stefan
S
Choi
Hon-Chit
HC
Feng
David Dagan
DD
Fulham
Michael
M
D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D007090:Image Interpretation, Computer-Assisted / Q000592:standards; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D049268:Positron-Emission Tomography; D012680:Sensitivity and Specificity; D015899:Tomography, Emission-Computed, Single-Photon / Q000592:standards / Q000706:statistics & numerical data