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      A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET

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          Abstract

          Purpose

          A critical bottleneck for the credibility of artificial intelligence (AI) is replicating the results in the diversity of clinical practice. We aimed to develop an AI that can be independently applied to recover high-quality imaging from low-dose scans on different scanners and tracers.

          Methods

          Brain [ 18F]FDG PET imaging of 237 patients scanned with one scanner was used for the development of AI technology. The developed algorithm was then tested on [ 18F]FDG PET images of 45 patients scanned with three different scanners, [ 18F]FET PET images of 18 patients scanned with two different scanners, as well as [ 18F]Florbetapir images of 10 patients. A conditional generative adversarial network (GAN) was customized for cross-scanner and cross-tracer optimization. Three nuclear medicine physicians independently assessed the utility of the results in a clinical setting.

          Results

          The improvement achieved by AI recovery significantly correlated with the baseline image quality indicated by structural similarity index measurement (SSIM) ( r = −0.71, p < 0.05) and normalized dose acquisition ( r = −0.60, p < 0.05). Our cross-scanner and cross-tracer AI methodology showed utility based on both physical and clinical image assessment ( p < 0.05).

          Conclusion

          The deep learning development for extensible application on unknown scanners and tracers may improve the trustworthiness and clinical acceptability of AI-based dose reduction.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s00259-021-05644-1.

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          Most cited references44

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          Image Quality Assessment: From Error Visibility to Structural Similarity

          Z. Wang, A.C. Bovik, H.R. Sheikh … (2004)
          Article 0 comments Cited 2308 times – based on 0 reviews     Review now
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            Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

            Michael Roberts, Derek Driggs, Matthew Thorpe … (2021)
            Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.
            Article 0 comments Cited 308 times – based on 0 reviews     Review now
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              First Human Imaging Studies with the EXPLORER Total-Body PET Scanner*

              Ramsey D. Badawi, Hongcheng Shi, Pengcheng Hu … (2019)
              Within the EXPLORER Consortium, the construction of the world's first total-body PET/CT scanner has recently been completed. The 194-cm axial field of view of the EXPLORER PET/CT scanner is sufficient to cover, for the first time, the entire human adult body in a single acquisition in more than 99% of the population and allows total-body pharmacokinetic studies with frame durations as short as 1 s. The large increase in sensitivity arising from total-body coverage as well as increased solid angle for detection at any point within the body allows whole-body 18F-FDG PET studies to be acquired with unprecedented count density, improving the signal-to-noise ratio of the resulting images. Alternatively, the sensitivity gain can be used to acquire diagnostic PET images with very small amounts of activity in the field of view (25 MBq, 0.7 mCi or less), with very short acquisition times (∼1 min or less) or at later time points after the tracer's administration. We report here on the first human imaging studies on the EXPLORER scanner using a range of different protocols that provide initial evidence in support of these claims. These case studies provide the foundation for future carefully controlled trials to quantitatively evaluate the improvements possible through total-body PET imaging.
              Article 0 comments Cited 190 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Biao Li:
                ORCID: http://orcid.org/0000-0002-0493-2062
                lb10363@rjh.com.cn
                Journal
                Journal ID (nlm-ta): Eur J Nucl Med Mol Imaging
                Journal ID (iso-abbrev): Eur J Nucl Med Mol Imaging
                Title: European Journal of Nuclear Medicine and Molecular Imaging
                Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )
                ISSN (Print): 1619-7070
                ISSN (Electronic): 1619-7089
                Publication date (Electronic): 24 December 2021
                Publication date PMC-release: 24 December 2021
                Publication date (Print): 2022
                Volume: 49
                Issue: 6
                Pages: 1843-1856
                Affiliations
                [1 ]GRID grid.5734.5, ISNI 0000 0001 0726 5157, Department of Nuclear Medicine, , University of Bern, ; Bern, Switzerland
                [2 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, Department of Nuclear Medicine, Ruijin Hospital, , Shanghai Jiao Tong University School of Medicine, ; Shanghai, China
                [3 ]Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Ruijin Center, Shanghai, China
                [4 ]GRID grid.6936.a, ISNI 0000000123222966, Department of Informatics, , Technical University of Munich, ; Munich, Germany
                [5 ]GRID grid.5734.5, ISNI 0000 0001 0726 5157, ARTORG Center, , University of Bern, ; Bern, Switzerland
                [6 ]GRID grid.38142.3c, ISNI 000000041936754X, Gordon Center for Medical Imaging, Massachusetts General Hospital, , Harvard Medical School, ; Boston, MA USA
                Author information
                http://orcid.org/0000-0002-0493-2062
                Article
                Publisher ID: 5644
                DOI: 10.1007/s00259-021-05644-1
                PMC ID: 9015984
                PubMed ID: 34950968
                SO-VID: f5b36d8b-8d64-4154-9d0f-e890f8d823e4
                Copyright © © The Author(s) 2021
                License:

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 29 June 2021
                Date accepted : 30 November 2021
                Funding
                Funded by: swiss national science foundation
                Award ID: 188914
                Award ID: 188350
                Award Recipient :
                Funded by: shanghai municipal key clinical specialty
                Award ID: shslczdzk03403
                Award Recipient :
                Categories
                Subject: Original Article
                Custom metadata
                issue-copyright-statement © Springer-Verlag GmbH Germany, part of Springer Nature 2022

                ScienceOpen disciplines: Radiology & Imaging
                Keywords: deep learning,low-dose,pet,recovery,cross-scanner,cross-tracer
                Data availability:
                ScienceOpen disciplines: Radiology & Imaging
                Keywords: deep learning, low-dose, pet, recovery, cross-scanner, cross-tracer

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