Ivo Rausch1, Lucas Rischka2, Claes N Ladefoged3, Julia Furtner4, Matthias Fenchel5, Andreas Hahn2, Rupert Lanzenberger2, Marius E Mayerhoefer4, Tatjana Traub-Weidinger6, Thomas Beyer1. 1. Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria. 2. Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria. 3. Department of Clinical Physiology, Nuclear Medicine and PET, University of Copenhagen, Copenhagen, Denmark. 4. Division of Neuroradiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria. 5. Siemens Healthcare GmbH, Magnetic Resonance, Erlangen, Germany; and. 6. Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria tatjana.traub-weidinger@meduniwien.ac.at.
Abstract
The aim of this study was to compare attenuation-correction (AC) approaches for PET/MRI in clinical neurooncology. Methods: Forty-nine PET/MRI brain scans were included: brain tumor studies using 18F-fluoro-ethyl-tyrosine (18F-FET) (n = 31) and 68Ga-DOTANOC (n = 7) and studies of healthy subjects using 18F-FDG (n = 11). For each subject, MR-based AC maps (MR-AC) were acquired using the standard DIXON- and ultrashort echo time (UTE)-based approaches. A third MR-AC was calculated using a model-based, postprocessing approach to account for bone attenuation values (BD, noncommercial prototype software by Siemens Healthcare). As a reference, AC maps were derived from patient-specific CT images (CTref). PET data were reconstructed using standard settings after AC with all 4 AC methods. We report changes in diagnosis for all brain tumor patients and the following relative differences values (RDs [%]), with regards to AC-CTref: for 18F-FET (A)-SUVs as well as volumes of interest (VOIs) defined by a 70% threshold of all segmented lesions and lesion-to-background ratios; for 68Ga-DOTANOC (B)-SUVs as well as VOIs defined by a 50% threshold for all lesions and the pituitary gland; and for 18F-FDG (C)-RD of SUVs of the whole brain and 10 anatomic regions segmented on MR images. Results: For brain tumor imaging (A and B), the standard PET-based diagnosis was not affected by any of the 3 MR-AC methods. For A, the average RDs of SUVmean were -10%, -4%, and -3% and of the VOIs 1%, 2%, and 7% for DIXON, UTE, and BD, respectively. Lesion-to-background ratios for all MR-AC methods were similar to that of CTref. For B, average RDs of SUVmean were -11%, -11%, and -3% and of the VOIs 1%, -4%, and -3%, respectively. In the case of 18F-FDG PET/MRI (C), RDs for the whole brain were -11%, -8%, and -5% for DIXON, UTE, and BD, respectively. Conclusion: The diagnostic reading of PET/MR patients with brain tumors did not change with the chosen AC method. Quantitative accuracy of SUVs was clinically acceptable for UTE- and BD-AC for group A, whereas for group B BD was in accordance with CTref. Nevertheless, for the quantification of individual lesions large deviations to CTref can be observed independent of the MR-AC method used.
The aim of this study was to compare attenuation-correction (AC) approaches for PET/MRI in clinical neurooncology. Methods: Forty-nine PET/MRI brain scans were included: brain tumor studies using 18F-fluoro-ethyl-tyrosine (18F-FET) (n = 31) and 68Ga-DOTANOC (n = 7) and studies of healthy subjects using 18F-FDG (n = 11). For each subject, MR-based AC maps (MR-AC) were acquired using the standard DIXON- and ultrashort echo time (UTE)-based approaches. A third MR-AC was calculated using a model-based, postprocessing approach to account for bone attenuation values (BD, noncommercial prototype software by Siemens Healthcare). As a reference, AC maps were derived from patient-specific CT images (CTref). PET data were reconstructed using standard settings after AC with all 4 AC methods. We report changes in diagnosis for all brain tumorpatients and the following relative differences values (RDs [%]), with regards to AC-CTref: for 18F-FET (A)-SUVs as well as volumes of interest (VOIs) defined by a 70% threshold of all segmented lesions and lesion-to-background ratios; for 68Ga-DOTANOC (B)-SUVs as well as VOIs defined by a 50% threshold for all lesions and the pituitary gland; and for 18F-FDG (C)-RD of SUVs of the whole brain and 10 anatomic regions segmented on MR images. Results: For brain tumor imaging (A and B), the standard PET-based diagnosis was not affected by any of the 3 MR-AC methods. For A, the average RDs of SUVmean were -10%, -4%, and -3% and of the VOIs 1%, 2%, and 7% for DIXON, UTE, and BD, respectively. Lesion-to-background ratios for all MR-AC methods were similar to that of CTref. For B, average RDs of SUVmean were -11%, -11%, and -3% and of the VOIs 1%, -4%, and -3%, respectively. In the case of 18F-FDG PET/MRI (C), RDs for the whole brain were -11%, -8%, and -5% for DIXON, UTE, and BD, respectively. Conclusion: The diagnostic reading of PET/MRpatients with brain tumors did not change with the chosen AC method. Quantitative accuracy of SUVs was clinically acceptable for UTE- and BD-AC for group A, whereas for group B BD was in accordance with CTref. Nevertheless, for the quantification of individual lesions large deviations to CTref can be observed independent of the MR-AC method used.
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