Title
Homotopic non-local regularized reconstruction from sparse positron emission tomography measurements.
Abstract
Positron 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.In 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.Results obtained using the proposed approach are compared with traditional filtered back-projection as well as expectation maximization reconstruction with total variation regularization.A 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.
Year
DOI
Venue
2015
10.1186/s12880-015-0052-5
BMC Medical Imaging
Keywords
Field
DocType
biomedical research,bioinformatics
Tomography,Regularization (mathematics),Positron emission tomography,Inverse problem,Gamma ray,Detector,Medicine,Pathology
Journal
Volume
Issue
ISSN
15
1
1471-2342
Citations 
PageRank 
References 
1
0.35
15
Authors
5
Name
Order
Citations
PageRank
Alexander Wong135169.61
Chenyi Liu210.69
Xiao Wang310.35
Paul W. Fieguth461254.17
Hongxia Bie5117.67