Reconstruction of kinetic parameter images directly from dynamic PET sinograms

Mustafa E. Kamasak; Charles A. Bouman; Evan D. Morris; Ken Sauer

doi:10.1117/12.595787

29 April 2005 Reconstruction of kinetic parameter images directly from dynamic PET sinograms

Mustafa E. Kamasak, Charles A. Bouman, Evan D. Morris, Ken Sauer

Proceedings Volume 5747, Medical Imaging 2005: Image Processing; (2005) https://doi.org/10.1117/12.595787
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

Recently, there has been interest in estimating kinetic model parameters for each voxel in a PET image. To do this, the activity images are first reconstructed from PET sinogram frames at each measurement time, and then the kinetic parameters are estimated by fitting a model to the reconstructed time-activity response of each voxel. However, this indirect approach to kinetic parameter estimation tends to reduce signal-to-noise ratio (SNR) because of the requirement that the sinogram data be divided into individual time frames. In 1985, Carson and Lange proposed, but did not implement, a method based on the EM algorithm for direct parametric reconstruction. More recently, researchers have developed semi-direct methods which use spline-based reconstruction, or direct methods for estimation of kinetic parameters from image regions. However, direct voxel-wise parametric reconstruction has remained a challenge due to the unsolved complexities of inversion and required spatial regularization. In this work, we demonstrate an efficient method for direct voxel-wise reconstruction of kinetic parameters (as a parametric image) from all frames of the PET data. The direct parametric image reconstruction is formulated in a Bayesian framework, and uses the parametric iterative coordinate descent (PICD) algorithm to solve the resulting optimization problem. This PICD algorithm is computationally efficient and allows the physiologically important kinetic parameters to be spatially regularized. Our experimental simulations demonstrate that direct parametric reconstruction can substantially reduce estimation error of kinetic parameters as compared to indirect methods.

Citation Download Citation

Mustafa E. Kamasak, Charles A. Bouman, Evan D. Morris, and Ken Sauer "Reconstruction of kinetic parameter images directly from dynamic PET sinograms", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.595787

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