Hsuan-Ming Huang1, Chih-Chieh Liu2, Chieh Lin3. 1. Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, No.1, Sec. 1, Jen Ai Rd., Taipei City, Zhongzheng Dist., 100, Taiwan. 2. Department of Biomedical Engineering, University of California, Davis, CA, 95616, USA. 3. Department of Nuclear Medicine, Chang Gung Memorial Hospital, No. 5 Fu-Shin Street, Kwei-Shan, Taoyuan County, Taiwan.
Abstract
PURPOSE: Parametric images obtained from kinetic modeling of dynamic positron emission tomography (PET) data provide a new way of visualizing quantitative parameters of the tracer kinetics. However, due to the high noise level in pixel-wise image-driven time-activity curves, parametric images often suffer from poor quality and accuracy. In this study, we propose an indirect parameter estimation framework which aims to improve the quality and quantitative accuracy of parametric images. METHODS: Three different approaches related to noise reduction and advanced curve fitting algorithm are used in the proposed framework. First, dynamic PET images are denoised using a kernel-based denoising method and the highly constrained backprojection technique. Second, gradient-free curve fitting algorithms are exploited to improve the accuracy and precision of parameter estimates. Third, a kernel-based post-filtering method is applied to parametric images to further improve the quality of parametric images. Computer simulations were performed to evaluate the performance of the proposed framework. RESULTS AND CONCLUSIONS: The simulation results showed that when compared to the Gaussian filtering, the proposed denoising method could provide better PET image quality, and consequentially improve the quality and quantitative accuracy of parametric images. In addition, gradient-free optimization algorithms (i.e., pattern search) can result in better parametric images than the gradient-based curve fitting algorithm (i.e., trust-region-reflective). Finally, our results showed that the proposed kernel-based post-filtering method could further improve the precision of parameter estimates while maintaining the accuracy of parameter estimates.
PURPOSE: Parametric images obtained from kinetic modeling of dynamic positron emission tomography (PET) data provide a new way of visualizing quantitative parameters of the tracer kinetics. However, due to the high noise level in pixel-wise image-driven time-activity curves, parametric images often suffer from poor quality and accuracy. In this study, we propose an indirect parameter estimation framework which aims to improve the quality and quantitative accuracy of parametric images. METHODS: Three different approaches related to noise reduction and advanced curve fitting algorithm are used in the proposed framework. First, dynamic PET images are denoised using a kernel-based denoising method and the highly constrained backprojection technique. Second, gradient-free curve fitting algorithms are exploited to improve the accuracy and precision of parameter estimates. Third, a kernel-based post-filtering method is applied to parametric images to further improve the quality of parametric images. Computer simulations were performed to evaluate the performance of the proposed framework. RESULTS AND CONCLUSIONS: The simulation results showed that when compared to the Gaussian filtering, the proposed denoising method could provide better PET image quality, and consequentially improve the quality and quantitative accuracy of parametric images. In addition, gradient-free optimization algorithms (i.e., pattern search) can result in better parametric images than the gradient-based curve fitting algorithm (i.e., trust-region-reflective). Finally, our results showed that the proposed kernel-based post-filtering method could further improve the precision of parameter estimates while maintaining the accuracy of parameter estimates.