Skip to main content
Key Specialties
Anesthesiology Cardiology Dermatology Diabetology Endocrinology Gastroenterology General Medicine Geriatrics Hematology Infectious Diseases Internal Medicine Nephrology Neurology Obstetrics and Gynecology Oncology Ophthalmology Pediatrics Psychiatry Radiology Respiratory Medicine Rheumatology Surgery Urology
Congress Coverage
ASH 2024 Annual Meeting 2024 ESMO Congress 2024 ERS Congress 2024 EASD Congress 2024 ESC Congress 2024 EAN Congress 2024 ASCO Annual Meeting 2024 ACC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on adolescent vaping

LIVE: Wednesday 29th January, 18:00-19:30 CET

Growing numbers of young people are using e-cigarettes, despite warnings of respiratory effects and addiction. How can doctors tackle the epidemic, and what health effects should you prepare to manage in your clinics?
ADVANCED SEARCH
Log in
Top
European Journal of Nuclear Medicine and Molecular Imaging
Published in:

Open Access 09-07-2022 | Molecular Imaging | Review Article

Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation

Authors: Dimitris Visvikis, Philippe Lambin, Kim Beuschau Mauridsen, Roland Hustinx, Michael Lassmann, Christoph Rischpler, Kuangyu Shi, Jan Pruim

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 13/2022

Login to get access

Abstract

Artificial intelligence (AI) will change the face of nuclear medicine and molecular imaging as it will in everyday life. In this review, we focus on the potential applications of AI in the field, both from a physical (radiomics, underlying statistics, image reconstruction and data analysis) and a clinical (neurology, cardiology, oncology) perspective. Challenges for transferability from research to clinical practice are being discussed as is the concept of explainable AI. Finally, we focus on the fields where challenges should be set out to introduce AI in the field of nuclear medicine and molecular imaging in a reliable manner.
Literature
1.
Visvikis D, Rest CCL, Jaouen V, Hatt M. Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications. Eur J Nucl Med Mol Imaging. 2019;46:2630–7.PubMedCrossRef
2.
Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE Inst Electr Electron Eng. 2021;86:2278–324. CrossRef
3.
Hinton GE, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18:1527–54.PubMedCrossRef
4.
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun Acm. 2017;60:84–90.CrossRef
5.
Torres-Velazquez M, Chen W-J, Li X, McMillan AB. Application and construction of deep learning networks in medical imaging. IEEE Trans Radiat Plasma Med Sci. 2021;5:137–59.
6.
McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577:89–94.PubMedCrossRef
7.
Haibe-Kains B, Adam GA, Hosny A, Khodakarami F, Shraddha T, Kusko R, et al. Transparency and reproducibility in artificial intelligence. Nature. 2020;586:E14–6.PubMedPubMedCentralCrossRef
8.
Curtis C, Liu C, Bollerman TJ, Pianykh OS. Machine learning for predicting patient wait times and appointment delays. J Am Coll Radiol. 2018;15:1310–6.PubMedCrossRef
9.
Hatt M, Rest CCL, Tixier F, Badic B, Schick U, Visvikis D. Radiomics: data are also images. J Nucl Med. 2019;60:38S-44S.PubMedCrossRef
10.
Uribe CF, Mathotaarachchi S, Gaudet V, Smith KC, Rosa-Neto P, Bénard F, et al. Machine learning in nuclear medicine: part 1-introduction. J Nucl Med. 2019;60:451–8.PubMedCrossRef
11.
Zukotynski K, Gaudet V, Uribe CF, Mathotaarachchi S, Smith KC, Rosa-Neto P, et al. Machine learning in nuclear medicine: part 2-neural networks and clinical aspects. J Nucl Med. 2021;1:22–9.CrossRef
12.
Nensa F, Demircioglu A, Rischpler C. Artificial intelligence in nuclear medicine. J Nucl Med. 2019;60:29S-37S.PubMedCrossRef
13.
Reader AJ, Corda G, Mehranian A, da Costa-Luis C, Ellis S, Schnabel JA. Deep learning for PET image reconstruction. IEEE Trans Radiat Plasma Med Sci. 2021;5:1–25.CrossRef
14.
Lee JS. A review of deep learning-based approaches for attenuation correction in positron emission tomography. IEEE Trans Radiat Plasma Med Sci. 2021;5:160–84.
15.
Sollini M, Antunovic L, Chiti A, Kirienko M. Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics. Eur J Nucl Med Mol. 2019;I(46):2656–72.CrossRef
16.
Castiglioni I, Rundo L, Codari M, Leo GD, Salvatore C, Interlenghi M, et al. AI applications to medical images: From machine learning to deep learning. Phys Medica. 2021;83:9–24.CrossRef
17.
Currie G, Rohren E. Intelligent imaging in nuclear medicine: the principles of artificial intelligence, machine learning and deep learning. Semin Nucl Med. 2020;51:102–11.PubMedCrossRef
18.
European Commission. White paper: On artificial intelligence – A European approach to excellence and trust. Brussels, 19-2-2020. COM(2020) 65 final.
19.
20.
21.
22.
Wei L, Osman S, Hatt M, ElNaqa I. Machine learning for radiomics-based multi-modality and multi-parametric modeling. Q J Nucl Med Mol Imaging. 2019;63:323–38.PubMedCrossRef
23.
Hatt M, Parmar C, Qi J, Naqa IE. Machine (Deep) Learning methods for image processing and radiomics. IEEE Trans Radiat Plasma Med Sci. 2019;3:104–8.CrossRef
24.
Häggström I, Schmidtlein CR, Campanella G, Fuchs TJ. DeepPET: a deep encoder–decoder network for directly solving the PET image reconstruction inverse problem. Med Image Anal. 2019;54:253–62.PubMedPubMedCentralCrossRef
25.
Kandarpa VSS, Bousse A, Benoit D, Visvikis D. DUG-RECON: a framework for direct image reconstruction using convolutional generative networks. IEEE Trans Radiat Plasma Med Sci. 2021;5:44–53.CrossRef
26.
Gong K, Guan J, Liu C-C, Qi J. PET image denoising using a deep neural network through fine tuninG. IEEE Trans Radiat Plasma Med Sci. 2019;3:153–61.PubMedCrossRef
27.
Mehranian A, Reader AJ. Model-based deep learning PET image reconstruction using forward-backward splitting expectation maximisation. IEEE Trans Radiat Plasma Med Sci. 2021;5:54–64.CrossRef
28.
Shao W, Pomper MG, Du Y. A learned reconstruction network for SPECT imaging. IEEE Trans Radiat Plasma Med Sci. 2021;5:26–34.PubMedCrossRef
29.
Jang H, Liu F, Zhao G, Bradshaw T, McMillan AB. Technical note: deep learning based MRAC using rapid ultrashort echo time imaging. Med Phys. 2018;45:3697–704.CrossRef
30.
Hwang D, Kang SK, Kim KY, Seo S, Paeng JC, Lee DS, et al. Generation of PET attenuation map for whole-body time-of-flight 18F-FDG PET/MRI using a deep neural network trained with simultaneously reconstructed activity and attenuation maps. J Nucl Med. 2019;60:1183–9.PubMedPubMedCentralCrossRef
31.
Dong X, Lei Y, Wang T, Higgins K, Liu T, Curran WJ, et al. Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging. Phys Med Biol. 2020;65:055011.PubMedPubMedCentralCrossRef
32.
Visvikis D, Merlin T, Bousse A, Benoit D, Laurent B. Deep learning based scatter correction for PET imaging. Eur J Nucl Med Mol Imaging. 2020;47(Suppl 1):S484.
33.
Xiang H, Lim H, Fessler JA, Dewaraja YK. A deep neural network for fast and accurate scatter estimation in quantitative SPECT/CT under challenging scatter conditions. Eur J Nucl Med Mol Imaging. 2020;47:2956–67.PubMedPubMedCentralCrossRef
34.
Yang J, Park D, Sohn JH, Wang ZJ, Gullberg GT, Seo Y. Joint correction of attenuation and scatter in image space using deep convolutional neural networks for dedicated brain 18 F-FDG PET. Phys Med Biol. 2019;64:075019.PubMedPubMedCentralCrossRef
35.
Wang Y, Zhou L, Wang L, Yu B, Zu C, Lalush DS, et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 21st International Conference, Granada, Spain, September 16–20, 2018, Proceedings, Part I. Lect Notes Comput Sc. 2018;11070:329–37.CrossRef
36.
37.
Hatt M, Laurent B, Ouahabi A, Fayad H, Tan S, Li L, et al. The first MICCAI challenge on PET tumor segmentation. Med Image Anal. 2018;44:177–95.PubMedCrossRef
38.
Pinochet P, Eude F, Becker S, Shah V, Sibille L, Toledano MN, et al. Evaluation of an automatic classification algorithm using convolutional neural networks in oncological positron emission tomography. Front Med. 2021;8:628179.CrossRef
39.
Guo Z, Li X, Huang H, Guo N, Li Q. Deep learning-based image segmentation on multimodal medical imaging. IEEE Trans Radiat Plasma Med Sci. 2019;3:162–9.PubMedPubMedCentralCrossRef
40.
Wang G, Rahmim A, Gunn RN. PET parametric imaging: past, present, and future. IEEE Trans Radiat Plasma Medical Sci. 2020;4:663–75.CrossRef
41.
Boellaard R, Delgado-Bolton R, Oyen W, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42:328–54.PubMedCrossRef
42.
43.
Kim DH, Wit H, Thurston M. Artificial intelligence in the diagnosis of Parkinson’s disease from ioflupane-123 single-photon emission computed tomography dopamine transporter scans using transfer learning. Nuc Med Commun. 2018;10:887–93.CrossRef
44.
Choi H, Kim YK, Yoon EJ, Lee J-Y, Lee DS, Initiative ADN. Cognitive signature of brain FDG PET based on deep learning: domain transfer from Alzheimer’s disease to Parkinson’s disease. Eur J Nucl Med Mol Imaging. 2020;47:403–12.PubMedCrossRef
45.
Son HJ, Oh JS, Oh M, Kim SJ, Lee J-H, Roh JH, et al. The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases. Eur J Nucl Med Mol Imaging. 2020;47:332–41.PubMedCrossRef
46.
Weehaeghe DV, Devrome M, Schramm G, Vocht JD, Deckers W, Baete K, et al. Combined brain and spinal FDG PET allows differentiation between ALS and ALS mimics. Eur J Nucl Med Mol. 2020;I(47):2681–90.CrossRef
47.
Betancur J, Rubeaux M, Fuchs TA, Otaki Y, Arnson Y, Slipczuk L, et al. Automatic valve plane localization in myocardial perfusioN SPECT/CT by machine learning: anatomic and clinical validation. J Nucl Med. 2016;58:961–7.PubMedCrossRef
48.
Arsanjani R, Xu Y, Hayes SW, Fish M, Lemley M, Gerlach J, et al. Comparison of fully automated computer analysis and visual scoring for detection of coronary artery disease from myocardial perfusion SPECT in a large population. J Nucl Med. 2013;54:221–8.PubMedCrossRef
49.
Arsanjani R, Dey D, Khachatryan T, Shalev A, Hayes SW, Fish M, et al. Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population. J Nucl Cardiol. 2015;22:877–84.PubMedCrossRef
50.
Hu L-H, Betancur J, Sharir T, Einstein AJ, Bokhari S, Fish MB, et al. Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry. Eur Heart J Cardiovasc Imaging. 2019;21:549–59.PubMedCentralCrossRef
51.
Betancur J, Hu L-H, Commandeur F, Sharir T, Einstein AJ, Fish MB, et al. Deep learning analysis of upright-supine high-efficiency SPECT myocardial perfusion imaging for prediction of obstructive coronary artery disease: a multicenter study. J Nucl Med: Off Publ Soc Nucl Med. 2019;60:664–70.CrossRef
52.
Betancur J, Otaki Y, Motwani M, Fish MB, Lemley M, Dey D, et al. Prognostic value of combined clinical and myocardial perfusion imaging data using machine learning. JACC Cardiovasc Imaging. 2018;11:1000–9.PubMedCrossRef
53.
Slart RHJA, Williams MC, Juarez-Orozco LE, Rischpler C, Dweck MR, Glaudemans AWJM, et al. Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT. Eur J Nucl Med Mol. 2021;I(48):1399–413.CrossRef
54.
Zhao Z, Pi Y, Jiang L, Xiang Y, Wei J, Yang P, et al. Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis. Sci Rep-uk. 2020;10:17046.CrossRef
55.
Ntakolia C, Diamantis DE, Papandrianos N, Moustakidis S, Papageorgiou EI. A lightweight convolutional neural network architecture applied for bone metastasis classification in nuclear medicine: a case study on prostate cancer patients. Healthc. 2020;8:493.CrossRef
56.
57.
Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749–62.PubMedCrossRef
58.
Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441–6.PubMedPubMedCentralCrossRef
59.
O’Connor JPB, Aboagye EO, Adams JE, Aerts HJWL, Barrington SF, Beer AJ, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol. 2017;14:169–86.PubMedCrossRef
60.
Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJWL. Machine learning methods for quantitative radiomic biomarkers. Sci Rep-uk. 2015;5:13087.CrossRef
61.
Deist TM, Dankers FJWM, Valdes G, Wijsman R, Hsu I, Oberije C, et al. Machine learning algorithms for outcome prediction in (chemo)radiotherapy: an empirical comparison of classifiers. Med Phys. 2018;45:3449–59.PubMedCrossRef
62.
Amyar A, Ruan S, Gardin I, Chatelain C, Decazes P, Modzelewski R. 3-D RPET-NET: development of a 3-D PET imaging convolutional neural network for radiomics analysis and outcome prediction. IEEE Trans Radiat Plasma Med Sci. 2019;3:225–31.CrossRef
63.
Hansen L, Salamon P. Neural network ensembles. IEEE Trans Pattern Anal Mach Intell. 1990;12:993–1001.
64.
Hatt M, Rest CCL, Antonorsi N, Tixier F, Tankyevych O, Jaouen V, et al. Radiomics in PET/CT: current status and future AI-based evolutions. Semin Nucl Med. 2020;51:126–33.PubMedCrossRef
65.
Afshar P, Mohammadi A, Plataniotis KN, Oikonomou A, Benali H. From handcrafted to deep-learning-based cancer radiomics. IEEE Signal Proc Mag. 2019;36:132–60.CrossRef
66.
Tixier F, Jaouen V, Hognon C, Gallinato O, Colin T, Visvikis D. Evaluation of conventional and deep learning based image harmonization methods in radiomics studies. Phys Med Biol. 2021;66:245009.CrossRef
67.
Wang Y-R (Joyce), Baratto L, Hawk KE, Theruvath AJ, Pribnow A 1 , Thakor AS, et al. Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure. Eur J Nucl Med Mol I. 2021;48:2771–81.
68.
Ljungberg M, Gleisner KS. 3-D Image-based dosimetry in radionuclide therapy. IEEE Trans Radiat Plasma Med Sci. 2018;2:527–40.CrossRef
69.
Flux GD, Gleisner KS, Chiesa C, Lassmann M, Chouin N, Gear J, et al. From fixed activities to personalized treatments in radionuclide therapy: lost in translation? Eur J Nucl Med Mol Imaging. 2018;45:152–4.PubMedCrossRef
70.
Zhao Y, Gafita A, Vollnberg B, Tetteh G, Haupt F, Afshar-Oromieh A, et al. Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT. Eur J Nucl Med Mol. 2020;I(47):603–13.CrossRef
71.
Xue S, Gafita A, Afshar-Oromieh A, Eiber M, Rominger A, Shi K. Voxel-wise prediction of post-therapy dosimetry for 177Lu-PSMA I&T therapy using deep learning. J Nucl Med. 2020;61:1424.
72.
Lee MS, Hwang D, Kim JH, Lee JS. Deep-dose: a voxel dose estimation method using deep convolutional neural network for personalized internal dosimetry. Sci Rep-uk. 2019;9:10308.CrossRef
73.
Ataeinia B, Heidari P. Artificial intelligence and the future of diagnostic and therapeutic radiopharmaceutical development: in silico smart molecular design. Pet Clin. 2021;16:513–23.PubMedPubMedCentralCrossRef
74.
Kletting P, Thieme A, Eberhardt N, Rinscheid A, D’Alessandria C, Allmann J, et al. Modeling and predicting tumor response in radioligand therapy. J Nucl Med. 2019;60:65–70.PubMedCrossRef
75.
Guo R, Hu X, Song H, Xu P, Xu H, Rominger A, et al. Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type. Eur J Nucl Med Mol. 2021;I:1–11.
76.
Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data. 2019;6:60.CrossRef
77.
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, et al. The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013;26:1045–57.PubMedPubMedCentralCrossRef
78.
Petersen RC, Aisen PS, Beckett LA, Donohue MC, Gamst AC, Harvey DJ, et al. Alzheimer’s Disease Neuroimaging Initiative (ADNI). Neurology. 2010;74:201–9.PubMedPubMedCentralCrossRef
79.
Quellec G, Charrière K, Boudi Y, Cochener B, Lamard M. Deep image mining for diabetic retinopathy screening. Med Image Anal. 2017;39:178–93.PubMedCrossRef
80.
81.
O’Neil C. Weapons of math destruction. How big data increases inequality and threatens democracy. New York: Crown Publishing Group; 2016.
82.
Bradshaw TJ, Boellaard R, Dutta J, Jha AK, Jacobs P, Li Q, et al. Nuclear medicine and artificial intelligence: best practices for algorithm development. J Nucl Med. 2022;63:500–10.PubMedCrossRef
Metadata
Title
Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation
Authors
Dimitris Visvikis
Philippe Lambin
Kim Beuschau Mauridsen
Roland Hustinx
Michael Lassmann
Christoph Rischpler
Kuangyu Shi
Jan Pruim
Publication date
09-07-2022
Publisher
Springer Berlin Heidelberg
Published in
European Journal of Nuclear Medicine and Molecular Imaging / Issue 13/2022
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI
https://doi.org/10.1007/s00259-022-05891-w

Other articles of this Issue 13/2022

Comparing lesion and feature selections to predict progression in newly diagnosed DLBCL patients with FDG PET/CT radiomics features

EARL compliance and imaging optimisation on the Biograph Vision Quadra PET/CT using phantom and clinical data

Correction to: Hetero-bivalent agents targeting FAP and PSMA

Feasibility of in vivo CAR T cells tracking using streptavidin–biotin-paired positron emission tomography

Long-term test-retest of cerebral [18F]MK-6240 binding and longitudinal evaluation of extracerebral tracer uptake in healthy controls and amnestic MCI patients

Assessing dynamic metabolic heterogeneity in non-small cell lung cancer patients via ultra-high sensitivity total-body [18F]FDG PET/CT imaging: quantitative analysis of [18F]FDG uptake in primary tumors and metastatic lymph nodes