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European Journal of Nuclear Medicine and Molecular Imaging

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15-12-2023 | Artificial Intelligence | Editorial

Perspectives of the European Association of Nuclear Medicine on the role of artificial intelligence (AI) in molecular brain imaging

Authors: Francesco Fraioli, Nathalie Albert, Ronald Boellaard, Ilaria Boscolo Galazzo, Matthias Brendel, Irene Buvat, Marco Castellaro, Diego Cecchin, Pablo Aguiar Fernandez, Eric Guedj, Alexander Hammers, Zoltan Kaplar, Silvia Morbelli, Laszlo Papp, Kuangyu Shi, Nelleke Tolboom, Tatjana Traub-Weidinger, Antoine Verger, Donatienne Van Weehaeghe, Igor Yakushev, Henryk Barthel

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 4/2024

Excerpt

As for the whole nuclear medicine community, there is great interest in the molecular brain imaging field to advance the use of AI in research and translation but foremost in daily clinical routine settings. …

Pianykh OS, Langs G, Dewey M, Enzmann DR, Herold CJ, Schoenberg SO, Brink JA. Continuous learning AI in radiology: Implementation principles and early applications. Radiology. 2020;297(1):6–14. https://doi.org/10.1148/radiol.2020200038.CrossRefPubMed

Paredes-Pacheco J, López-González FJ, Silva-Rodríguez J, Efthimiou N, Niñerola-Baizán A, Ruibal Á, Roé-Vellvé N, Aguiar P. SimPET-an open online platform for the Monte Carlo simulation of realistic brain PET data. Validation for 18 F-FDG scans. Med Phys. 2021;48(5):2482–93. https://doi.org/10.1002/mp.14838.CrossRefPubMed

Flaus A, Deddah T, Reilhac A, Leiris N, Janier M, Merida I, Grenier T, McGinnity CJ, Hammers A, Lartizien C, Costes N. PET image enhancement using artificial intelligence for better characterization of epilepsy lesions. Front Med (Lausanne). 2022;16(9):1042706. https://doi.org/10.3389/fmed.2022.1042706.CrossRef

Shiri I, Vafaei Sadr A, Amini M, Salimi Y, Sanaat A, Akhavanallaf A, Razeghi B, Ferdowsi S, Saberi A, Arabi H, Becker M, Voloshynovskiy S, Gündüz D, Rahmim A, Zaidi H. Decentralized distributed multi-institutional pet image segmentation using a federated deep learning framework. Clin Nucl Med. 2022;47(7):606–17. https://doi.org/10.1097/RLU.0000000000004194.CrossRefPubMed

Warnat-Herresthal S, Schultze H, Shastry KL, Manamohan S, Mukherjee S, Garg V, Sarveswara R, Händler K, Pickkers P, Aziz NA, Ktena S, Tran F, Bitzer M, Ossowski S, Casadei N, Herr C, Petersheim D, Behrends U, Kern F, et al. Swarm Learning for decentralized and confidential clinical machine learning. Nature. 2021;594(7862):265–70. https://doi.org/10.1038/s41586-021-03583-3.ADSCrossRefPubMedPubMedCentral

Sanaat A, Akhavanalaf A, Shiri I, Salimi Y, Arabi H, Zaidi H. Deep-TOF-PET: Deep learning-guided generation of time-of-flight from non-TOF brain PET images in the image and projection domains. Hum Brain Mapp. 2022;43(16):5032–43. https://doi.org/10.1002/hbm.26068.CrossRefPubMedPubMedCentral

Reader AJ, Pan B. AI for PET image reconstruction. Br J Radiol. 2023;96(1150):20230292. https://doi.org/10.1259/bjr.20230292.CrossRefPubMedPubMedCentral

Sari H, Teimoorisichani M, Mingels C, Alberts I, Panin V, Bharkhada D, Xue S, Prenosil G, Shi K, Conti M, Rominger A. Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners. Eur J Nucl Med Mol Imaging. 2022;49(13):4490–502. https://doi.org/10.1007/s00259-022-05909-3.CrossRefPubMedPubMedCentral

Chen KT, Schürer M, Ouyang J, Koran MEI, Davidzon G, Mormino E, Tiepolt S, Hoffmann KT, Sabri O, Zaharchuk G, Barthel H. Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning. Eur J Nucl Med Mol Imaging. 2020;47(13):2998–3007. https://doi.org/10.1007/s00259-020-04897-6.CrossRefPubMedPubMedCentral

10.

Oh KT, Lee S, Lee H, Yun M, Yoo SK. semantic segmentation of white matter in FDG-PET using generative adversarial network. J Digit Imaging. 2020;33(4):816–25. https://doi.org/10.1007/s10278-020-00321-5.CrossRefPubMedPubMedCentral

11.

Kang SK, Kim D, Shin SA, Kim YK, Choi H, Lee JS. Fast and accurate amyloid brain pet quantification without MRI using deep neural networks. J Nucl Med. 2023;64(4):659–66. https://doi.org/10.2967/jnumed.122.264414.CrossRefPubMedPubMedCentral

12.

Kuttner S, Wickstrøm KK, Lubberink M, Tolf A, Burman J, Sundset R, Jenssen R, Appel L, Axelsson J. Cerebral blood flow measurements with 15O-water PET using a non-invasive machine-learning-derived arterial input function. J Cereb Blood Flow Metab. 2021;41(9):2229–41. https://doi.org/10.1177/0271678X21991393.CrossRefPubMedPubMedCentral

13.

Salahuddin Z, Woodruff HC, Chatterjee A, Lambin P. Transparency of deep neural networks for medical image analysis: A review of interpretability methods. Comput Biol Med. 2021;4(140):105111. https://doi.org/10.1016/j.compbiomed.2021.105111.CrossRef

14.

van der Velden BHM, Kuijf HJ, Gilhuijs KGA, Viergever MA. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis, vol. 79. Medical Image Analysis. Elsevier B.V; 2022. https://doi.org/10.1016/j.media.2022.102470.CrossRef

15.

Rogeau A, Lilburn DML, Kaplar Z, Anderson C, Scott CJ, Chowdhury FA, Fraioli F, Bomanji JB. Identifying and troubleshooting the pitfalls of ictal/interictal brain perfusion SPECT studies. Nucl Med Commun. 2023;44(11):1053–8. https://doi.org/10.1097/MNM.0000000000001755.CrossRefPubMed

16.

Flaus A, Jung J, Ostrowky-Coste K, Rheims S, Guénot M, Bouvard S, Janier M, Yaakub SN, Lartizien C, Costes N, Hammers A. Deep-learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co-registered to MRI to identify the epileptogenic zone in focal epilepsy. Epilepsia Open. 2023;8(4):1440–51. https://doi.org/10.1002/epi4.12820.CrossRefPubMedPubMedCentral

17.

Lin Y, Mo J, Jin H, Cao X, Zhao Y, Wu C, Zhang K, Hu W, Lin Z. Automatic analysis of integrated magnetic resonance and positron emission tomography images improves the accuracy of detection of focal cortical dysplasia type IIb lesions. Eur J Neurosci. 2021;53(9):3231–41. https://doi.org/10.1111/ejn.15185.CrossRefPubMed

18.

Royse SK, Minhas DS, Lopresti BJ, Murphy A, Ward T, Koeppe RA, Bullich S, DeSanti S, Jagust WJ, Landau SM. alzheimer’s disease neuroimaging initiative. validation of amyloid PET positivity thresholds in centiloids: a multisite PET study approach. Alzheimers Res Ther. 2021;13(1):99. https://doi.org/10.1186/s13195-021-00836-1.CrossRefPubMedPubMedCentral

19.

Lohmann P, Galldiks N, Kocher M, Heinzel A, Filss CP, Stegmayr C, Mottaghy FM, Fink GR, Jon Shah N, Langen KJ. Radiomics in neuro-oncology: Basics, workflow, and applications. Methods. 2021;188:112–21. https://doi.org/10.1016/j.ymeth.2020.06.003.CrossRefPubMed

20.

Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, Ashrafinia S, Bakas S, Beukinga RJ, Boellaard R, Bogowicz M, Boldrini L, Buvat I, Cook GJR, Davatzikos C, Depeursinge A, Desseroit MC, Dinapoli N, Dinh CV, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295(2):328–38. https://doi.org/10.1148/radiol.2020191145.CrossRefPubMed

21.

Coughlin JM, Horti AG, Pomper MG. Opportunities in precision psychiatry using PET neuroimaging in psychosis. Neurobiol Dis. 2019;131:104428. https://doi.org/10.1016/j.nbd.2019.03.016.CrossRefPubMedPubMedCentral

22.

Boyle AJ, Gaudet VC, Black SE, Vasdev N, Rosa-Neto P, Zukotynski KA. Artificial intelligence for molecular neuroimaging. Ann Transl Med. 2021;9(9):822. https://doi.org/10.21037/atm-20-6220.CrossRefPubMedPubMedCentral

23.

Kautzky A, Vanicek T, Philippe C, Kranz GS, Wadsak W, Mitterhauser M, Hartmann A, Hahn A, Hacker M, Rujescu D, Kasper S, Lanzenberger R. Machine learning classification of ADHD and HC by multimodal serotonergic data. Transl Psychiatry. 2020;10(1):104. https://doi.org/10.1038/s41398-020-0781-2.CrossRefPubMedPubMedCentral

24.

Mete M, Sakoglu U, Spence JS, Devous MD Sr, Harris TS, Adinoff B. Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach. BMC Bioinformatics. 2016;17(Suppl 13):357. https://doi.org/10.1186/s12859-016-1218-z.CrossRefPubMedPubMedCentral

25.

Jeong B, Lee J, Kim H, Gwak S, Kim YK, Yoo SY, Lee D, Choi JS. Multiple-kernel support vector machine for predicting internet gaming disorder using multimodal fusion of PET, EEG, and clinical features. Front Neurosci. 2022;30(16):856510. https://doi.org/10.3389/fnins.2022.856510.CrossRef

26.

Brown EC, Clark DL, Forkert ND, Molnar CP, Kiss ZHT, Ramasubbu R. Metabolic activity in subcallosal cingulate predicts response to deep brain stimulation for depression. Neuropsychopharmacology. 2020;45(10):1681–8. https://doi.org/10.1038/s41386-020-0745-5.CrossRefPubMedPubMedCentral

27.

Veronese M, Santangelo B, Jauhar S, D’Ambrosio E, Demjaha A, Salimbeni H, et al. A potential biomarker for treatment stratification in psychosis: evaluation of an [18F] FDOPA PET imaging approach. Neuropsychopharmacology. 2021;46(6):1122–32.CrossRefPubMed

Title: Perspectives of the European Association of Nuclear Medicine on the role of artificial intelligence (AI) in molecular brain imaging
Authors: Francesco Fraioli
Nathalie Albert
Ronald Boellaard
Ilaria Boscolo Galazzo
Matthias Brendel
Irene Buvat
Marco Castellaro
Diego Cecchin
Pablo Aguiar Fernandez
Eric Guedj
Alexander Hammers
Zoltan Kaplar
Silvia Morbelli
Laszlo Papp
Kuangyu Shi
Nelleke Tolboom
Tatjana Traub-Weidinger
Antoine Verger
Donatienne Van Weehaeghe
Igor Yakushev
Henryk Barthel
Publication date: 15-12-2023
Publisher: Springer Berlin Heidelberg
Keywords: Artificial Intelligence
Epilepsy
Published in: European Journal of Nuclear Medicine and Molecular Imaging / Issue 4/2024
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-023-06553-1

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Perspectives of the European Association of Nuclear Medicine on the role of artificial intelligence (AI) in molecular brain imaging

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