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European Journal of Nuclear Medicine and Molecular Imaging

Erschienen in:

08.01.2021 | Original Article

True ultra-low-dose amyloid PET/MRI enhanced with deep learning for clinical interpretation

verfasst von: Kevin T. Chen, Tyler N. Toueg, Mary Ellen Irene Koran, Guido Davidzon, Michael Zeineh, Dawn Holley, Harsh Gandhi, Kim Halbert, Athanasia Boumis, Gabriel Kennedy, Elizabeth Mormino, Mehdi Khalighi, Greg Zaharchuk

Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging | Ausgabe 8/2021

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Abstract

Purpose

While sampled or short-frame realizations have shown the potential power of deep learning to reduce radiation dose for PET images, evidence in true injected ultra-low-dose cases is lacking. Therefore, we evaluated deep learning enhancement using a significantly reduced injected radiotracer protocol for amyloid PET/MRI.

Methods

Eighteen participants underwent two separate ¹⁸F-florbetaben PET/MRI studies in which an ultra-low-dose (6.64 ± 3.57 MBq, 2.2 ± 1.3% of standard) or a standard-dose (300 ± 14 MBq) was injected. The PET counts from the standard-dose list-mode data were also undersampled to approximate an ultra-low-dose session. A pre-trained convolutional neural network was fine-tuned using MR images and either the injected or sampled ultra-low-dose PET as inputs. Image quality of the enhanced images was evaluated using three metrics (peak signal-to-noise ratio, structural similarity, and root mean square error), as well as the coefficient of variation (CV) for regional standard uptake value ratios (SUVRs). Mean cerebral uptake was correlated across image types to assess the validity of the sampled realizations. To judge clinical performance, four trained readers scored image quality on a five-point scale (using 15% non-inferiority limits for proportion of studies rated 3 or better) and classified cases into amyloid-positive and negative studies.

Results

The deep learning–enhanced PET images showed marked improvement on all quality metrics compared with the low-dose images as well as having generally similar regional CVs as the standard-dose. All enhanced images were non-inferior to their standard-dose counterparts. Accuracy for amyloid status was high (97.2% and 91.7% for images enhanced from injected and sampled ultra-low-dose data, respectively) which was similar to intra-reader reproducibility of standard-dose images (98.6%).

Conclusion

Deep learning methods can synthesize diagnostic-quality PET images from ultra-low injected dose simultaneous PET/MRI data, demonstrating the general validity of sampled realizations and the potential to reduce dose significantly for amyloid imaging.

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Titel: True ultra-low-dose amyloid PET/MRI enhanced with deep learning for clinical interpretation
verfasst von: Kevin T. Chen
Tyler N. Toueg
Mary Ellen Irene Koran
Guido Davidzon
Michael Zeineh
Dawn Holley
Harsh Gandhi
Kim Halbert
Athanasia Boumis
Gabriel Kennedy
Elizabeth Mormino
Mehdi Khalighi
Greg Zaharchuk
Publikationsdatum: 08.01.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging / Ausgabe 8/2021
Print ISSN: 1619-7070
Elektronische ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-020-05151-9

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True ultra-low-dose amyloid PET/MRI enhanced with deep learning for clinical interpretation

Abstract

Purpose

Methods

Results

Conclusion

Live-Webinar "Urologie und Sexualmedizin in der Praxis"

Springer Medizin

Abstract

Purpose

Methods

Results

Conclusion

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