A whole-body FDG PET/MR atlas for multiparametric voxel-based analysis

• Published in: Scientific reports Vol. 9; no. 1; p. 6158
• Main Authors: Sjöholm, Therese, Ekström, Simon, Strand, Robin, Ahlström, Håkan, Lind, Lars, Malmberg, Filip, Kullberg, Joel
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 16.04.2019; Nature Publishing Group
• Subjects: 59/57; 59/78; 639/705/117; 692/700/1421/1628; 692/700/1421/2109; 692/700/1421/2160; Aged; Computerized Image Processing; Data processing; Datoriserad bildbehandling; Female; Fluorodeoxyglucose F18 - analysis; Humanities and Social Sciences; Humans; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Middle Aged; multidisciplinary; Multimodal Imaging - methods; Positron emission tomography; Positron-Emission Tomography - methods; Prospective Studies; Science; Science (multidisciplinary); Tomography; Whole Body Imaging - methods
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-019-42613-z

Quantitative multiparametric imaging is a potential key application for Positron Emission Tomography/Magnetic Resonance (PET/MR) hybrid imaging. To enable objective and automatic voxel-based multiparametric analysis in whole-body applications, the purpose of this study was to develop a multimodality whole-body atlas of functional 18F-fluorodeoxyglucose (FDG) PET and anatomical fat-water MR data of adults. Image registration was used to transform PET/MR images of healthy control subjects into male and female reference spaces, producing a fat-water MR, local tissue volume and FDG PET whole-body normal atlas consisting of 12 male (66.6 ± 6.3 years) and 15 female (69.5 ± 3.6 years) subjects. Manual segmentations of tissues and organs in the male and female reference spaces confirmed that the atlas contained adequate physiological and anatomical values. The atlas was applied in two anomaly detection tasks as proof of concept. The first task automatically detected anomalies in two subjects with suspected malignant disease using FDG data. The second task successfully detected abnormal liver fat infiltration in one subject using fat fraction data.