Chen K.T.Sch?rer M.Ouyang J.Koran M.E.I.Davidzon G.Mormino E.Tiepolt S.Hoffmann K.-T.Sabri O.Zaharchuk G.Barthel H.TZE-HSIANG CHEN2022-05-242022-05-24202016197070https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086446543&doi=10.1007%2fs00259-020-04897-6&partnerID=40&md5=859fad2d524db2331dad3e7e32695305https://scholars.lib.ntu.edu.tw/handle/123456789/611661Purpose: We aimed to evaluate the performance of deep learning-based generalization of ultra-low-count amyloid PET/MRI enhancement when applied to studies acquired with different scanning hardware and protocols. Methods: Eighty simultaneous [18F]florbetaben PET/MRI studies were acquired, split equally between two sites (site 1: Signa PET/MRI, GE Healthcare, 39 participants, 67 ± 8?years, 23 females; site 2: mMR, Siemens Healthineers, 64 ± 11?years, 23 females) with different MRI protocols. Twenty minutes of list-mode PET data (90–110?min post-injection) were reconstructed as ground-truth. Ultra-low-count data obtained from undersampling by a factor of 100 (site 1) or the first minute of PET acquisition (site 2) were reconstructed for ultra-low-dose/ultra-short-time (1% dose and 5% time, respectively) PET images. A deep convolution neural network was pre-trained with site 1 data and either (A) directly applied or (B) trained further on site 2 data using transfer learning. Networks were also trained from scratch based on (C) site 2 data or (D) all data. Certified physicians determined amyloid uptake (+/?) status for accuracy and scored the image quality. The peak signal-to-noise ratio, structural similarity, and root-mean-squared error were calculated between images and their ground-truth counterparts. Mean regional standardized uptake value ratios (SUVR, reference region: cerebellar cortex) from 37 successful site 2 FreeSurfer segmentations were analyzed. Results: All network-synthesized images had reduced noise than their ultra-low-count reconstructions. Quantitatively, image metrics improved the most using method B, where SUVRs had the least variability from the ground-truth and the highest effect size to differentiate between positive and negative images. Method A images had lower accuracy and image quality than other methods; images synthesized from methods B–D scored similarly or better than the ground-truth images. Conclusions: Deep learning can successfully produce diagnostic amyloid PET images from short frame reconstructions. Data bias should be considered when applying pre-trained deep ultra-low-count amyloid PET/MRI networks for generalization. ? 2020, Springer-Verlag GmbH Germany, part of Springer Nature.amyloidflorbetabenagedArticlecerebellum cortexconvolutional neural networkdeep learningeffect sizefemalehumanimage qualitymajor clinical studymalenuclear magnetic resonance imagingpositron emission tomographyquantitative analysissignal noise ratiostandardized uptake value ratiotransfer of learningimage processingx-ray computed tomographyAmyloidDeep LearningFemaleHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingPositron-Emission TomographyTomography, X-Ray Computed[SDGs]SDG3journal article10.1007/s00259-020-04897-6325356552-s2.0-85086446543