Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22458
Title: A non-local post-filtering algorithm for PET incorporating anatomical knowledge
Authors: Chan, C
Meikle, S
Fulton, R
Tian, G
Cai, W
Feng, D
Keywords: Adaptive filters
Expectation-maximisation algorithm
Filtering theory
Image reconstruction
Image registration
Medical image processing
Positron emission tomography
Issue Date: 2009
Publisher: IEEE
Source: 2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC), October 24 2009-November 1 2009, Orlando, FL, p. 2728-2732 How to cite?
Journal: 2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC), October 24 2009-November 1 2009, Orlando, FL 
Abstract: The maximum likelihood expectation maximization (MLEM) reconstruction method is known to yield noisy images at high iteration numbers because emission tomographic reconstruction is an ill-posed problem. The noise can be suppressed by post-filtering the ML estimate or imposing a priori knowledge as a constraint within a Bayesian reconstruction framework. Most of these filters and priors are based on weighting the intensity differences between neighbouring pixels within a small local neighbourhood. Therefore, they have limited information to distinguish edges from noise. We investigated the use of a non-local means (NLM) filter for post-filtering MLEM reconstructed positron emission tomography (PET) images. We further investigated the effect of incorporating anatomical side information obtained from co-registered computed tomography (CT) images into the NLM, resulting in an adaptive non-local means (A-NLM) filter which takes into account the variance within each anatomical region on the PET image. In simulated and physical phantom experiments, the A-NLM filter demonstrated superior performance tradeoff between lesion contrast and noise than conventional Gaussian post-filtering and NLM without anatomical prior. We conclude that the A-NLM filter has potential for improved lesion detection over Gaussian post-filtered MLEM images.
URI: http://hdl.handle.net/10397/22458
ISBN: 978-1-4244-3961-4
978-1-4244-3962-1 (E-ISBN)
ISSN: 1095-7863
DOI: 10.1109/NSSMIC.2009.5401971
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