Validation of automated whole-body analysis of metabolic and morphological parameters from an integrated FDG-PET/MRI acquisition

• Published in: Scientific reports Vol. 10; no. 1; p. 5331
• Main Authors: Guglielmo, P., Ekström, S., Strand, R., Visvanathar, R., Malmberg, F., Johansson, E., Pereira, M. J., Skrtic, S., Carlsson, B. C. L., Eriksson, J. W., Ahlström, H., Kullberg, J.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 24.03.2020; Nature Publishing Group
• Subjects: 692/163/2743/137; 692/4017; Aged; Algorithms; Automation; Biochemical Phenomena; Brain - physiology; Female; Fluorodeoxyglucose F18; Humanities and Social Sciences; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Image Processing, Computer-Assisted - methods; Liver - physiology; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Middle Aged; multidisciplinary; Multimodal Imaging - methods; Muscles; Pancreas; Positron emission tomography; Positron-Emission Tomography - methods; Reproducibility of Results; Science; Science (multidisciplinary); Segmentation; Tissue analysis; Tomography, X-Ray Computed - methods; Whole Body Imaging - methods
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-62353-9

Automated quantification of tissue morphology and tracer uptake in PET/MR images could streamline the analysis compared to traditional manual methods. To validate a single atlas image segmentation approach for automated assessment of tissue volume, fat content (FF) and glucose uptake (GU) from whole-body [ 18 F]FDG-PET/MR images. Twelve subjects underwent whole-body [ 18 F]FDG-PET/MRI during hyperinsulinemic-euglycemic clamp. Automated analysis of tissue volumes, FF and GU were achieved using image registration to a single atlas image with reference segmentations of 18 volume of interests (VOIs). Manual segmentations by an experienced radiologist were used as reference. Quantification accuracy was assessed with Dice scores, group comparisons and correlations. VOI Dice scores ranged from 0.93 to 0.32. Muscles, brain, VAT and liver showed the highest scores. Pancreas, large and small intestines demonstrated lower segmentation accuracy and poor correlations. Estimated tissue volumes differed significantly in 8 cases. Tissue FFs were often slightly but significantly overestimated. Satisfactory agreements were observed in most tissue GUs. Automated tissue identification and characterization using a single atlas segmentation performs well compared to manual segmentation in most tissues and will be valuable in future studies. In certain tissues, alternative quantification methods or improvements to the current approach is needed.