Parametric image reconstruction using spectral analysis of PET projection data

Spectral analysis is a general modelling approach that enables calculation of parametric images from reconstructed tracer kinetic data independent of an assumed compartmental structure. We investigated the validity of applying spectral analysis directly to projection data motivated by the advantages...

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Bibliographic Details
Published in:Physics in medicine & biology 1998-03, Vol.43 (3), p.651-666
Main Authors: Meikle, Steven R, Matthews, Julian C, Cunningham, Vincent J, Bailey, Dale L, Livieratos, Lefteris, Jones, Terry, Price, Pat
Format: Article
Language:English
Subjects:
Algorithms
Astrocytoma - diagnostic imaging
Biological and medical sciences
Biophysical Phenomena
Biophysics
Brain Neoplasms - diagnostic imaging
Carbon Radioisotopes
Colonic Neoplasms - diagnostic imaging
Female
Humans
Image Processing, Computer-Assisted - methods
Image Processing, Computer-Assisted - statistics & numerical data
Investigative techniques, diagnostic techniques (general aspects)
Least-Squares Analysis
Medical sciences
Middle Aged
Miscellaneous. Technology
Models, Theoretical
Phantoms, Imaging
Radionuclide investigations
Software
Thymidine - metabolism
Tomography, Emission-Computed - statistics & numerical data
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Summary:Spectral analysis is a general modelling approach that enables calculation of parametric images from reconstructed tracer kinetic data independent of an assumed compartmental structure. We investigated the validity of applying spectral analysis directly to projection data motivated by the advantages that: (i) the number of reconstructions is reduced by an order of magnitude and (ii) iterative reconstruction becomes practical which may improve signal-to-noise ratio (SNR). A dynamic software phantom with typical 2-[11C]thymidine kinetics was used to compare projection-based and image-based methods and to assess bias-variance trade-offs using iterative expectation maximization (EM) reconstruction. We found that the two approaches are not exactly equivalent due to properties of the non-negative least-squares algorithm. However, the differences are small (< 5%) and mainly affect parameters related to early and late time points on the impulse response function (K1 and, to a lesser extent, VD). The optimal number of EM iteration was 15-30 with up to a two-fold improvement in SNR over filtered back projection. We conclude that projection-based spectral analysis with EM reconstruction yields accurate parametric images with high SNR and has potential application to a wide range of positron emission tomography ligands.
ISSN:0031-9155
1361-6560
DOI:10.1088/0031-9155/43/3/016