A non-local post-filtering algorithm for PET incorporating anatomical knowledge

• Main Authors: Chung Chan, Meikle, S., Fulton, R., Guang-Jian Tian, Weidong Cai, Feng, D.D.
• Format: Conference Proceeding
• Language: English
• Subjects: Bayesian methods; Computed tomography; Filtering algorithms; Filters; Image reconstruction; Lesions; Maximum likelihood detection; Maximum likelihood estimation; Positron emission tomography; Reconstruction algorithms
• ISSN: 1082-3654; 2577-0829
• DOI: 10.1109/NSSMIC.2009.5401971

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.