Homotopic non-local regularized reconstruction from sparse positron emission tomography measurements

BMC Medical Imaging

Research Article

Chenyi Liu¹  Hongxia Bie¹  Xiao Yu Wang²  Alexander Wong²  Paul Fieguth² 

[1] Department of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China;Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada;

关键词: Positron Emission Tomography;    Filter Back Projection;    Sampling Fraction;    Positron Emission Tomography Data;    Projection Angle;   

DOI  :  10.1186/s12880-015-0052-5

 received in 2014-05-28, accepted in 2015-02-25,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundPositron emission tomography scanners collect measurements of a patient’s in vivo radiotracer distribution. The system detects pairs of gamma rays emitted indirectly by a positron-emitting radionuclide (tracer), which is introduced into the body on a biologically active molecule, and the tomograms must be reconstructed from projections. The reconstruction of tomograms from the acquired PET data is an inverse problem that requires regularization. The use of tightly packed discrete detector rings, although improves signal-to-noise ratio, are often associated with high costs of positron emission tomography systems. Thus a sparse reconstruction, which would be capable of overcoming the noise effect while allowing for a reduced number of detectors, would have a great deal to offer.MethodsIn this study, we introduce and investigate the potential of a homotopic non-local regularization reconstruction framework for effectively reconstructing positron emission tomograms from such sparse measurements.ResultsResults obtained using the proposed approach are compared with traditional filtered back-projection as well as expectation maximization reconstruction with total variation regularization.ConclusionsA new reconstruction method was developed for the purpose of improving the quality of positron emission tomography reconstruction from sparse measurements. We illustrate that promising reconstruction performance can be achieved for the proposed approach even at low sampling fractions, which allows for the use of significantly fewer detectors and have the potential to reduce scanner costs.

【 授权许可】

CC BY   
© Wong et al.; licensee BioMed Central. 2015

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