Creation and Application of a Simulated Database of Dynamic [$^18$F]MPPF PET Acquisitions Incorporating Inter-Individual Anatomical and Biological Variability

During the process of validation of a new tracer, estimation of performance and validation of processing algorithms have to be investigated with data sets representative of the ground truth. Because this ground truth is hardly accessible in positron emission tomography (PET), validations of processing algorithms often rely on the use of simulated data sets. Considering that Monte Carlo simulators are very time consuming and are not very easy to use, the building of publicly available databases of simulated PET volumes are becoming highly desirable. We present here the methodology employed for the creation of a database of simulated dynamic [18F]MPPF-PET data, including inter-individual anatomical and biological variability which meets the criteria of a gold standard database as defined by Lehmann: reliance, equivalence, independence, relevance, significance. The assessment of the realism of the built database against actual MPPF PET data is also presented here. Whereas the database was specifically created for the investigations of quantification of activity and binding of ligand-receptor with the [18F]MPPF PET tracer, it may serve the community with countless purposes. The full strength of this database, does not only stem from the knowledge of important information such as the true activity map and underlying anatomical data, but also from the possibility to fully control the biological difference between sets of simulated PET data. Indeed, time activity curves included in the simulated data sets are controlled by a multicompartmental model of ligand-receptor exchanges. This latter feature is of a great interest in the context of the improvement of the detectability of biological variation in PET

[1]  G Brix,et al.  Performance evaluation of a whole-body PET scanner using the NEMA protocol. National Electrical Manufacturers Association. , 1997, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[2]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[3]  Roger N Gunn,et al.  Parametrically defined cerebral blood vessels as non-invasive blood input functions for brain PET studies. , 2004, Physics in medicine and biology.

[4]  Latifa Rbah,et al.  Displacement of the PET ligand 18F‐MPPF by the electrically evoked serotonin release in the rat hippocampus , 2003, Synapse.

[5]  J. Logan Graphical analysis of PET data applied to reversible and irreversible tracers. , 1999, Nuclear medicine and biology.

[6]  André Luxen,et al.  Modeling [18F]MPPF Positron Emission Tomography Kinetics for the Determination of 5-Hydroxytryptamine(1A) Receptor Concentration with Multiinjection , 2002, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[7]  I. Kanno,et al.  Error Analysis of a Quantitative Cerebral Blood Flow Measurement Using H215O Autoradiography and Positron Emission Tomography, with Respect to the Dispersion of the Input Function , 1986, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[8]  Thomas Martin Lehmann From plastic to gold: a unified classification scheme for reference standards in medical image processing , 2002, SPIE Medical Imaging.

[9]  François Mauguière,et al.  Statistical parametric mapping of 5-HT1A receptor binding in temporal lobe epilepsy with hippocampal ictal onset on intracranial EEG , 2004, NeuroImage.

[10]  Vincent Frouin,et al.  Correction of partial-volume effect for PET striatal imaging: fast implementation and study of robustness. , 2002, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[11]  D. Louis Collins,et al.  A new improved version of the realistic digital brain phantom , 2006, NeuroImage.

[12]  F. Fazio,et al.  A publicly accessible Monte Carlo database for validation purposes in emission tomography , 2005, European Journal of Nuclear Medicine and Molecular Imaging.

[13]  Alan C. Evans,et al.  PET-SORTEO: a Monte Carlo-based Simulator with high count rate capabilities , 2004, IEEE Transactions on Nuclear Science.

[14]  W. J. Lorenz,et al.  Performance evaluation of the whole-body PET scanner ECAT EXACT HR + , 1997 .

[15]  André Luxen,et al.  [18F]p-MPPF: A Radiolabeled Antagonist for the Study of 5-HT1A Receptors with PET , 2000 .

[16]  François Mauguière,et al.  5-HT1A receptor binding and intracerebral activity in temporal lobe epilepsy: an [18F]MPPF-PET study. , 2004, Brain : a journal of neurology.

[17]  D. Louis Collins,et al.  ANIMAL+INSECT: Improved Cortical Structure Segmentation , 1999, IPMI.

[18]  Karl J. Friston,et al.  Spatial registration and normalization of images , 1995 .

[19]  Vincent J. Cunningham,et al.  Parametric Imaging of Ligand-Receptor Binding in PET Using a Simplified Reference Region Model , 1997, NeuroImage.

[20]  A. Reilhac,et al.  PET-SORTEO: validation and development of database of Simulated PET volumes , 2005, IEEE Transactions on Nuclear Science.

[21]  Alan C. Evans,et al.  A fully automatic and robust brain MRI tissue classification method , 2003, Medical Image Anal..