Abstract
Aim
The aim of the study was (1) to evaluate the reconstruction strategies with dynamic [\(_{ }^{11}\mathrm C\)]-raclopride human positron emission tomography (PET) studies acquired from ECAT high-resolution research tomograph (HRRT) scanner and (2) to justify for the selected gap-filling method for analytical reconstruction with simulated phantom data.
Methods
A new transradial bicubic interpolation method has been implemented to enable faster analytical 3D-reprojection (3DRP) reconstructions for the ECAT HRRT PET scanner data. The transradial bicubic interpolation method was compared to the other gap-filling methods visually and quantitatively using the numerical Shepp–Logan phantom. The performance of the analytical 3DRP reconstruction method with this new gap-filling method was evaluated in comparison with the iterative statistical methods: ordinary Poisson ordered subsets expectation maximization (OPOSEM) and resolution modeled OPOSEM methods. The image reconstruction strategies were evaluated using human data at different count statistics and consequently at different noise levels. In the assessments, 14 [\(_{ }^{11}\mathrm C\)]-raclopride dynamic PET studies (test–retest studies of 7 healthy subjects) acquired from the HRRT PET scanner were used. Besides the visual comparisons of the methods, we performed regional quantitative evaluations over the cerebellum, caudate and putamen structures. We compared the regional time–activity curves (TACs), areas under the TACs and binding potential (BP\(_{\text {ND}}\)) values.
Results and conclusions
The results showed that the new gap-filling method preserves the linearity of the 3DRP method. Results with the 3DRP after gap-filling method exhibited hardly any dependency on the count statistics (noise levels) in the sinograms while we observed changes in the quantitative results with the EM-based methods for different noise contamination in the data. With this study, we showed that 3DRP with transradial bicubic gap-filling method is feasible for the reconstruction of high-resolution PET data with missing sinogram bins. The calculated intraclass correlation coefficient (ICC) values were similar for all tested methods and validated the test–retest study. The gap-filling and 3DRP method can be used for quantitative PET studies in which high temporal information is crucial and can serve as a reference method for comparison studies of the other reconstruction methods.
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Notes
See footnote 3.
The study protocol was approved by the Ethics Committee of the Hospital District of Southwestern Finland. The study subjects were given written information about all the relevant issues involved in this study. A written consent was obtained from each subject. This study was performed according to the ethical guidelines given by the Declaration of Helsinki.
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Acknowledgments
The authors thank Prof. Jarmo Hietala, Dr. Mika Teräs, Prof. Juha Rinne and MD. Kati Alakurtti (Turku PET Centre, Turku, Finland) for the test–retest ECAT HRRT data. Moreover, we would like to thank the head of the ECAT HRRT users community, Merence Sibomana for providing us the ECAT HRRT software packages. This work was supported by the Academy of Finland (application number: 129657, Finnish Programme for Centres of Excellence in Research 2006–2011) and by the Graduate School in Electronics, Telecommunication and Automation (GETA), Finland.
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Tuna, U., Johansson, J. & Ruotsalainen, U. Evaluation of analytical reconstruction with a new gap-filling method in comparison to iterative reconstruction in [\(_{ }^{11}\mathrm C\)]-raclopride PET studies. Ann Nucl Med 28, 417–429 (2014). https://doi.org/10.1007/s12149-014-0832-y
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DOI: https://doi.org/10.1007/s12149-014-0832-y
Keywords
- Area under the curve (AUC)
- Binding potential image ((BPND)-image)
- Brain
- Image reconstruction
- Intraclass correlation coefficient (ICC)
- Nuclear imaging
- Positron emission tomography (PET)
- Quantitative evaluation
- Raclopride
- Reconstruction methods comparison
- Region of interest (ROI)
- Time–activity curve (TAC)
- Transradial bicubic interpolation