New Statistical Models for Randoms-Precorrected PET Scans
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
Dynamic Dual-Tracer PET Reconstruction
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Tracer kinetics guided dynamic PET reconstruction
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Efficient robust reconstruction of dynamic PET activity maps with radioisotope decay constraints
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Simultaneous estimation of PET attenuation and activity images with divided difference filters
Miar'06 Proceedings of the Third international conference on Medical Imaging and Augmented Reality
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Statistical iterative reconstruction algorithms have shown improved image quality over conventional nonstatistical methods in PET by using accurate system response models and measurement noise models. Strictly speaking, however, PET measurements, pre-corrected for accidental coincidences, are neither Poisson nor Gaussian distributed and thus do not meet basic assumptions of these algorithms. In addition, the difficulty in determining the proper system response model also greatly affects the quality of the reconstructed images. In this paper, we explore the usage of state space principles for the estimation of activity map in tomographic PET imaging. The proposed strategy formulates the organ activity distribution through tracer kinetics models, and the photon-counting measurements through observation equations, thus makes it possible to unify the dynamic reconstruction problem and static reconstruction problem into a general framework. Further, it coherently treats the uncertainties of the statistical model of the imaging system and the noisy nature of measurement data. Since H∞ filter seeks minimum-maximum-error estimates without any assumptions on the system and data noise statistics, it is particular suited for PET image reconstruction where the statistical properties of measurement data and the system model are very complicated. The performance of the proposed framework is evaluated using Shepp-Logan simulated phantom data and real phantom data with favorable results.