CERMEP-IDB-MRXFDG: a database of 37 normal adult human brain [18F]FDG PET, T1 and FLAIR MRI, and CT images available for research

Inés Mérida,Caroline Bouillot,Franck Lavenne,Julien Jung,Sandrine Bouvard,Alexander Hammers,Didier Le Bars,Sophie Lancelot,Nicolas Costes,Jérôme Redouté

doi:10.1186/s13550-021-00830-6

Abstract

We present a database of cerebral PET FDG and anatomical MRI for 37 normal adult human subjects (CERMEP-IDB-MRXFDG). Thirty-nine participants underwent static [18F]FDG PET/CT and MRI, resulting in [18F]FDG PET, T1 MPRAGE MRI, FLAIR MRI, and CT images. Two participants were excluded after visual quality control. We describe the acquisition parameters, the image processing pipeline and provide participants’ individual demographics (mean age 38 ± 11.5 years, range 23–65, 20 women). Volumetric analysis of the 37 T1 MRIs showed results in line with the literature. A leave-one-out assessment of the 37 FDG images using Statistical Parametric Mapping (SPM) yielded a low number of false positives after exclusion of artefacts. The database is stored in three different formats, following the BIDS common specification: (1) DICOM (data not processed), (2) NIFTI (multimodal images coregistered to PET subject space), (3) NIFTI normalized (images normalized to MNI space). Bona fide researchers can request access to the database via a short form.

Highlights

Imaging databases are very useful to re-analyse data in a different context, to increase the number of subjects of a study, and to develop new methods
We are aware of very few datasets for [18F] fluorodeoxyglucose ([18F]FDG) PET imaging that have been published [10]) or are available on request ([11]; [12]; Alzheimer’s Disease Neuroimaging Initiative, ADNI http://adni.loni.usc.edu)
In this work we introduce a multi-modal database of 37 healthy subjects constructed with MRI, CT and [18F]FDG PET images to BIDS standard

Summary

Introduction

Imaging databases are very useful to re-analyse data in a different context, to increase the number of subjects of a study, and to develop new methods. Imaging databases play a crucial role in numerous analysis methods that rely in the comparison between the data of a group or of an individual and a group of reference This includes studies using a normative database for analysis and quantification purposes (such as partial volume correction), machine learning approaches, multi-atlas techniques, In the last years, an increasing number of neuroimaging databases has been made available. These databases generally consist of MR images (such as ADNI http://adni.loni.usc.edu, OASIS https://www.oasis-brains.org; [5, 6], for a review see [7, 8]). We are aware of very few datasets for [18F] fluorodeoxyglucose ([18F]FDG) PET imaging that have been published [10]) or are available on request ([11]; [12]; Alzheimer’s Disease Neuroimaging Initiative, ADNI http://adni.loni.usc.edu)

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Results

Conclusion