Abstract
Image reconstruction for list-mode time-of-flight (TOF) positron emission tomography (PET) can be achieved by analytic algorithms. The backprojection filtering (BPF) algorithm is an efficient algorithm for this task. The conventional noise control method for analytic image reconstruction is the use of a stationary lowpass filter, which does not model the Poisson noise properly. This study proposes a nonstationary filter for Poisson noise control. The filter is implemented in the spatial domain in a form similar to convolution.
Highlights
Analytic image reconstruction methods for list-mode time-of-flight (TOF) positron emission tomography (PET) have been developed over the years [1,2,3,4]
The conventional noise control method is the application of a lowpass filter [5]
The goal of this study is to develop a nonstationary filter for Poisson noise control in analytic TOF PET image reconstruction
Summary
Analytic image reconstruction methods for list-mode time-of-flight (TOF) positron emission tomography (PET) have been developed over the years [1,2,3,4]. One of the advantages of using TOF technology is its ability to reduce the image noise. If an iterative algorithm is used to reconstruct the image, the Poisson noise model is readily implemented as a weighting function for the projections [2]. The conventional noise control method is the application of a lowpass filter [5]. A lowpass filter is normally shift-invariant and can be implemented as convolution in the spatial domain or as multiplication in the Fourier domain [6]. The conventional lowpass filters are unable to model the Poisson noise accurately, because Poisson noise in an image is not stationary
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