Deep-learning-based attenuation correction in dynamic [15O]H2O studies using PET/MRI in healthy volunteers
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Quantitative [15O]H2O positron emission tomography (PET) is the accepted reference method for regional cerebral blood flow (rCBF) quantification. To perform reliable quantitative [15O]H2O-PET studies in PET/MRI scanners, MRI-based attenuation-correction (MRAC) is required. Our aim was to compare two MRAC methods (RESOLUTE and DeepUTE) based on ultrashort echo-time with computed tomography-based reference standard AC (CTAC) in dynamic and static [15O]H2O-PET. We compared rCBF from quantitative perfusion maps and activity concentration distribution from static images between AC methods in 25 resting [15O]H2O-PET scans from 14 healthy men at whole-brain, regions of interest and voxel-wise levels. Average whole-brain CBF was 39.9 ± 6.0, 39.0 ± 5.8 and 40.0 ± 5.6 ml/100 g/min for CTAC, RESOLUTE and DeepUTE corrected studies respectively. RESOLUTE underestimated whole-brain CBF by 2.1 ± 1.50% and rCBF in all regions of interest (range −2.4%– −1%) compared to CTAC. DeepUTE showed significant rCBF overestimation only in the occipital lobe (0.6 ± 1.1%). Both MRAC methods showed excellent correlation on rCBF and activity concentration with CTAC, with slopes of linear regression lines between 0.97 and 1.01 and R2 over 0.99. In conclusion, RESOLUTE and DeepUTE provide AC information comparable to CTAC in dynamic [15O]H2O-PET but RESOLUTE is associated with a small but systematic underestimation.
|Journal||Journal of Cerebral Blood Flow and Metabolism|
|Publication status||Published - 2021|
The authors would like to thank the hard work and dedication of the nuclear medicine technologists and radiographers Nadia Azizi, Marianne Federspiel, Jakup Poulsen and Karin Stahr; the staff of the Cyclotron and Radiochemistry Unit and Annette Ulrich, from the cardiothoracic anesthesiology department for the arterial cannulations. We would like to thank David E. Nyrnberg for his help on generating the CT-based ?-maps. We would also like to thank The John and Birthe Meyer Foundation, who generously donated the PET/MRI scanner and the cyclotron to Rigshospitalet, University of Copenhagen. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Oriol Puig was supported by a training grant from the Fundaci?n Alfonso Mart?n Escudero and Claes N. Ladefoged was supported by a post-doc grant from the Danish Council for Independent Research (reference number: 6110-00692A).
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Oriol Puig was supported by a training grant from the Fundación Alfonso Martín Escudero and Claes N. Ladefoged was supported by a post-doc grant from the Danish Council for Independent Research (reference number: 6110-00692A). Acknowledgments
© The Author(s) 2021.
- Attenuation correction, deep learning, PET/MRI, rCBF, [O]HO-PET