PURPOSE: To obtain regional blood flow and tissue-blood partition coefficient with time-activity curves from H2(15)O PET, fitting of some parameters in the Kety model is conventionally accomplished by nonlinear least squares (NLS) analysis. However, NLS requires considerable compuation time then is impractical for pixel-by-pixel analysis to generate parametric images of these parameters. In this study, we investigated several fast parameter estimation methods for the parametric image generation and compared their statistical reliability and computational efficiency.
MATERIALS AND METHODS: These methods included linear least squres (LLS), linear weighted least squares (LWLS), linear generalized least squares (GLS), linear generalized weighted least squares (GWLS), weighted integration (WI), and model-based clustering method (CAKS). H2(15)O dynamic brain PET with Poisson noise component was simulated using numerical Zubal brain phantom. Error and bias in the estimation of rCBF and partition coefficient, and computation time in various noise environments was estimated and compared. In addition, parametric images from H2(15)O dynamic brain PET data performed on 16 healthy volunteers under various physiological conditions was compared to examine the utility of these methods for real human data.
RESULTS: These fast algorithms produced parametric images with similar image quality and statistical reliability. When CAKS and LLS methods were used combinedly, computation time was significantly reduced and less than 30 seconds for 128x128x46 images on Pentium III processor.
CONCLUSION: Parametric images of rCBF and partition coefficient with good statistical properties can be generated with short computation time which is acceptable in clinical situation.
|