Simplified estimation of binding parameters based on image-derived reference tissue models for dopamine transporter bindings in non-human primates using [18F]FE-PE2I and PET.

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Simplified estimation of binding parameters based on image-derived reference tissue models for dopamine transporter bindings in non-human primates using [18F]FE-PE2I and PET.

Am J Nucl Med Mol Imaging. 2017;7(6):246-254

Authors: Odano I, Varrone A, Hosoya T, Sakaguchi K, Gulyás B, Padmanabhan P, Ghosh KK, Yang CT, Guenther I, Wang Z, Serrano R, Chimon NG, Halldin C

Abstract
The aim of this study on dopamine transporter binding by [18F]FE-PE2I and PET was to describe an image-derived approach using reference tissue models: the Logan DVR approach and simplified reference tissue model (SRTM), the features of which were simple to operate and precise in the measurements. Using the approach, the authors sought to obtain binding images and parameters. [18F]FE-PE2I and dynamic PET as well as an MRI was performed on three rhesus monkeys, and metabolite corrected arterial plasma inputs were obtained. After co-registering of PET to MR images, both image sets were resliced. The time-activity curve of the cerebellum was used as indirect input, and binding parametric images were computed voxel-by-voxel. Voxel-wise linear calculations were used for the Logan DVR approach, and nonlinear least squares fittings for the SRTM. To determine the best linear regression in the Logan DVR approach, the distribution volume ratio was obtained using the optimal starting frame analysis. The obtained binding parameters were compared with those obtained by the other independent ROI-based numerical approaches: two-tissue compartment model (2TCM), Logan DVR approach and SRTM using PMOD software. Binding potentials (BP) obtained by the present approach agreed well with those obtained by ROI-based numerical approaches, although reference tissue models tended to underestimate the BP value than 2TCM. Image-derived Logan approach provided a low-noise image, the computation time was short, and the error in the optimal starting frame analysis was small. The present approach provides a high-quality binding parametric image and reliable parameter value easily.

PMID: 29348979 [PubMed]

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