Sparse/Low Rank Constrained Reconstruction for Dynamic PET Imaging

In dynamic Positron Emission Tomography (PET), an estimate of the radio activity concentration is obtained from a series of frames of sinogram data taken at ranging in duration from 10 seconds to minutes under some criteria. So far, all the well-known reconstruction algorithms require known data sta...

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Bibliographic Details
Published in:PloS one 2015-11, Vol.10 (11), p.e0142019-e0142019
Main Authors: Yu, Xingjian, Chen, Shuhang, Hu, Zhenghui, Liu, Meng, Chen, Yunmei, Shi, Pengcheng, Liu, Huafeng
Format: Article
Language:English
Subjects:
Algorithms
Biology
Computational mathematics
Computer Simulation
Convex analysis
Data acquisition
Data models
Data processing
Emissions
Engineering
Frames
Humans
Laboratories
Mathematical models
Medical imaging
Medical research
Metabolism
Noise
Nuclear medicine
Numerical methods
Phantoms, Imaging
Physics
Positron emission
Positron emission tomography
Positron-Emission Tomography - methods
Principal components analysis
Quantitative analysis
Reconstruction
Science
Statistical methods
Tomography
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Summary:In dynamic Positron Emission Tomography (PET), an estimate of the radio activity concentration is obtained from a series of frames of sinogram data taken at ranging in duration from 10 seconds to minutes under some criteria. So far, all the well-known reconstruction algorithms require known data statistical properties. It limits the speed of data acquisition, besides, it is unable to afford the separated information about the structure and the variation of shape and rate of metabolism which play a major role in improving the visualization of contrast for some requirement of the diagnosing in application. This paper presents a novel low rank-based activity map reconstruction scheme from emission sinograms of dynamic PET, termed as SLCR representing Sparse/Low Rank Constrained Reconstruction for Dynamic PET Imaging. In this method, the stationary background is formulated as a low rank component while variations between successive frames are abstracted to the sparse. The resulting nuclear norm and l1 norm related minimization problem can also be efficiently solved by many recently developed numerical methods. In this paper, the linearized alternating direction method is applied. The effectiveness of the proposed scheme is illustrated on three data sets.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0142019