Abstract
Image reconstruction from low-count positron emission tomography (PET) projection data is challenging because the inverse problem is ill-posed. Prior information can be used to improve image quality. Inspired by the kernel methods in machine learning, this paper proposes a kernel based method that models PET image intensity in each pixel as a function of a set of features obtained from prior information. The kernel-based image model is incorporated into the forward model of PET projection data and the coefficients can be readily estimated by the maximum likelihood (ML) or penalized likelihood image reconstruction. A kernelized expectation-maximization algorithm is presented to obtain the ML estimate. Computer simulations show that the proposed approach can achieve better bias versus variance trade-off and higher contrast recovery for dynamic PET image reconstruction than the conventional maximum likelihood method with and without post-reconstruction denoising. Compared with other regularization-based methods, the kernel method is easier to implement and provides better image quality for low-count data. Application of the proposed kernel method to a 4-D dynamic PET patient dataset showed promising results.
Original language | English (US) |
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Article number | 6868314 |
Pages (from-to) | 61-71 |
Number of pages | 11 |
Journal | IEEE Transactions on Medical Imaging |
Volume | 34 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2015 |
Keywords
- Expectation maximization (EM)
- image prior
- image reconstruction
- kernel method
- positron emission tomography (PET)
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Science Applications
- Radiological and Ultrasound Technology
- Software