Paper Info
Title
Postreconstruction nonlocal means filtering of whole-body PET with an anatomical prior.
Abstract
Positron emission tomography (PET) images usually suffer from poor signal-to-noise ratio (SNR) due to the high level of noise and low spatial resolution, which adversely affect its performance for lesion detection and quantification. The complementary information present in high-resolution anatomical images from multi-modality imaging systems could potentially be used to improve the ability to detect and/or quantify lesions. However, previous methods that use anatomical priors usually require matched organ/lesion boundaries. In this study, we investigated the use of anatomical information to suppress noise in PET images while preserving both quantitative accuracy and the amplitude of prominent signals that do not have corresponding boundaries on computerized tomography (CT). The proposed approach was realized through a postreconstruction filter based on the nonlocal means (NLM) filter, which reduces noise by computing the weighted average of voxels based on the similarity measurement between patches of voxels within the image. Anatomical knowledge obtained from CT was incorporated to constrain the similarity measurement within a subset of voxels. In contrast to other methods that use anatomical priors, the actual number of neighboring voxels and weights used for smoothing were determined from a robust measurement on PET images within the subset. Thus, the proposed approach can be robust to signal mismatches between PET and CT. A 3-D search scheme was also investigated for the volumetric PET/CT data. The proposed anatomically guided median nonlocal means filter (AMNLM) was first evaluated using a computer phantom and a physical phantom to simulate realistic but challenging situations where small lesions are located in homogeneous regions, which can be detected on PET but not on CT. The proposed method was further assessed with a clinical study of a patient with lung lesions. The performance of the proposed method was compared to Gaussian, edge-preserving bilateral and NLM filters, as well as median nonlocal means (MNLM) filtering without an anatomical prior. The proposed AMNLM method yielded improved lesion contrast and SNR compared with other methods even with imperfect anatomical knowledge, such as missing lesion boundaries and mismatched organ boundaries.
Year
DOI
Venue
2014
10.1109/TMI.2013.2292881
IEEE Trans. Med. Imaging
Keywords
Field
DocType
postreconstruction nonlocal means filtering,median filters,positron emission tomography/computerized tomography (pet/ct),mismatched organ boundaries,physical phantom,high noise level,lesion quantification,image matching,low spatial resolution,whole-body pet,postreconstruction filter,anatomically guided median nonlocal means filter,edge-preserving bilateral filters,image resolution,matched organ-lesion boundaries,computer phantom,image denoising,anatomical knowledge,complementary information,positron emission tomography images,positron emission tomography,prominent signal amplitude,similarity measurement,nonlocal means,high-resolution anatomical images,signal mismatches,filtering theory,computerized tomography,lesion detection,emission tomography,biological organs,phantoms,medical image processing,signal-to-noise ratio,multimodality imaging systems,nonlocal means filter,anatomical prior,computed tomography,noise measurement,signal to noise ratio,noise
Voxel,Computer vision,Imaging phantom,Filter (signal processing),Tomography,Smoothing,Positron emission tomography,Artificial intelligence,Prior probability,Image resolution,Mathematics
Journal
Volume
Issue
ISSN
33
3
1558-254X
Citations 
PageRank 
References 
9
0.49
21
Authors
5
Name
Order
Citations
PageRank
Chung Chan190.49
R. R. Fulton2101.79
Robert Barnett390.49
David Dagan Feng43329413.76
Steven R. Meikle5235.20