Skip to main content

Advertisement

SpringerLink
Log in

Application of artificial intelligence in brain molecular imaging

  • Invited Review Article
  • Published:
Annals of Nuclear Medicine Aims and scope Submit manuscript

Abstract

Initial development of artificial Intelligence (AI) and machine learning (ML) dates back to the mid-twentieth century. A growing awareness of the potential for AI, as well as increases in computational resources, research, and investment are rapidly advancing AI applications to medical imaging and, specifically, brain molecular imaging. AI/ML can improve imaging operations and decision making, and potentially perform tasks that are not readily possible by physicians, such as predicting disease prognosis, and identifying latent relationships from multi-modal clinical information. The number of applications of image-based AI algorithms, such as convolutional neural network (CNN), is increasing rapidly. The applications for brain molecular imaging (MI) include image denoising, PET and PET/MRI attenuation correction, image segmentation and lesion detection, parametric image formation, and the detection/diagnosis of Alzheimer’s disease and other brain disorders. When effectively used, AI will likely improve the quality of patient care, instead of replacing radiologists. A regulatory framework is being developed to facilitate AI adaptation for medical imaging.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature. 1986;323:533–6.

    Article  Google Scholar 

  2. Fukushima K. Neural network model for a mechanism of pattern recognition unaffected by shift in position—Neocognitron. Trans IEICE. 1979;J62:658–65.

    Google Scholar 

  3. Kippenhan JS, Barker WW, Pascal S, Nagel J, Duara R. Evaluation of a neural-network classifier for PET scans of normal and Alzheimer’s disease subjects. J Nucl Med. 1992;33:1459–67.

    CAS  PubMed  Google Scholar 

  4. Kippenhan JS, Barker WW, Nagel J, Grady C, Duara R. Neural-network classification of normal and Alzheimer’s disease subjects using high-resolution and low-resolution PET cameras [see comments]. J Nucl Med. 1994;35:7–15.

    CAS  PubMed  Google Scholar 

  5. Lee JS, Lee DS, Kim SK, et al. Localization of epileptogenic zones in F-18 FDG brain PET of patients with temporal lobe epilepsy using artificial neural network. IEEE Trans Med Imaging. 2000;19:347–55.

    Article  CAS  PubMed  Google Scholar 

  6. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44.

    Article  CAS  PubMed  Google Scholar 

  7. LeCun Y, Boser B, Denker JS, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1989;1:541–51.

    Article  Google Scholar 

  8. Kravitz DJ, Saleem KS, Baker CI, Ungerleider LG, Mishkin M. The ventral visual pathway: an expanded neural framework for the processing of object quality. Trends Cogn Sci. 2013;17:26–49.

    Article  PubMed  Google Scholar 

  9. Wang X, Zhou L, Wang Y, Jiang H, Ye H. Improved low-dose positron emission tomography image reconstruction using deep learned prior. Phys Med Biol. 2021;66:115001.

    Article  Google Scholar 

  10. Spuhler K, Serrano-Sosa M, Cattell R, DeLorenzo C, Huang C. Full-count PET recovery from low-count image using a dilated convolutional neural network. Med Phys. 2020;47:4928–38.

    Article  PubMed  Google Scholar 

  11. Xiang L, Qiao Y, Nie D, An L, Wang Q, Shen D. Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI. Neurocomputing. 2017;267:406–16.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Hashimoto F, Ohba H, Ote K, Kakimoto A, Tsukada H, Ouchi Y. 4D deep image prior: dynamic PET image denoising using an unsupervised four-dimensional branch convolutional neural network. Phys Med Biol. 2021;66:015006.

    Article  CAS  PubMed  Google Scholar 

  13. Gong K, Guan J, Liu CC, Qi J. PET image denoising using a deep neural network through fine tuning. IEEE Trans Radiat Plasma Med Sci. 2019;3:153–61.

    Article  PubMed  Google Scholar 

  14. Kim K, Wu D, Gong K, et al. Penalized PET reconstruction using deep learning prior and local linear fitting. IEEE Trans Med Imaging. 2018;37:1478–87.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Mehranian A, Reader AJ. Model-based deep learning PET image reconstruction using forward-backward splitting expectation-maximization. IEEE Trans Radiat Plasma Med Sci. 2020;5:54–64.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Liu CC, Huang HM. Partial-ring PET image restoration using a deep learning based method. Phys Med Biol. 2019;64:225014.

    Article  CAS  PubMed  Google Scholar 

  17. Schramm G, Rigie D, Vahle T, et al. Approximating anatomically-guided PET reconstruction in image space using a convolutional neural network. Neuroimage. 2021;224:117399.

    Article  CAS  PubMed  Google Scholar 

  18. Xu J, Liu H. Three-dimensional convolutional neural networks for simultaneous dual-tracer PET imaging. Phys Med Biol. 2019;64:185016.

    Article  CAS  PubMed  Google Scholar 

  19. Song TA, Chowdhury SR, Yang F, Dutta J. Super-resolution PET imaging using convolutional neural networks. IEEE Trans Comput Imaging. 2020;6:518–28.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Hashimoto F, Ito M, Ote K, Isobe T, Okada H, Ouchi Y. Deep learning-based attenuation correction for brain PET with various radiotracers. Ann Nucl Med. 2021;35:691–701.

    Article  CAS  PubMed  Google Scholar 

  21. Wang T, Lei Y, Fu Y, et al. Machine learning in quantitative PET: a review of attenuation correction and low-count image reconstruction methods. Phys Med. 2020;76:294–306.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Arabi H, Bortolin K, Ginovart N, Garibotto V, Zaidi H. Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies. Hum Brain Mapp. 2020;41:3667–79.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Hwang D, Kang SK, Kim KY, et al. Generation of PET attenuation map for whole-body time-of-flight (18)F-FDG PET/MRI using a deep neural network trained with simultaneously reconstructed activity and attenuation maps. J Nucl Med. 2019;60:1183–9.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Hwang D, Kim KY, Kang SK, et al. Improving the accuracy of simultaneously reconstructed activity and attenuation maps using deep learning. J Nucl Med. 2018;59:1624–9.

    Article  CAS  PubMed  Google Scholar 

  25. Klaser K, Varsavsky T, Markiewicz P, et al. Imitation learning for improved 3D PET/MR attenuation correction. Med Image Anal. 2021;71:102079.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Gong K, Yang J, Larson PEZ, et al. MR-based attenuation correction for brain PET using 3D cycle-consistent adversarial network. IEEE Trans Radiat Plasma Med Sci. 2021;5:185–92.

    Article  PubMed  Google Scholar 

  27. Gong K, Han PK, Johnson KA, El Fakhri G, Ma C, Li Q. Attenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging. Eur J Nucl Med Mol Imaging. 2021;48:1351–61.

    Article  CAS  PubMed  Google Scholar 

  28. Massa HA, Johnson JM, McMillan AB. Comparison of deep learning synthesis of synthetic CTs using clinical MRI inputs. Phys Med Biol. 2020;65:23NT03.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ladefoged CN, Hansen AE, Henriksen OM, et al. AI-driven attenuation correction for brain PET/MRI: clinical evaluation of a dementia cohort and importance of the training group size. Neuroimage. 2020;222:117221.

    Article  CAS  PubMed  Google Scholar 

  30. Blanc-Durand P, Khalife M, Sgard B, et al. Attenuation correction using 3D deep convolutional neural network for brain 18F-FDG PET/MR: comparison with Atlas, ZTE and CT based attenuation correction. PLoS One. 2019;14:e0223141.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Jang H, Liu F, Zhao G, Bradshaw T, McMillan AB. Technical Note: Deep learning based MRAC using rapid ultrashort echo time imaging [published online ahead of print, 2018 May 15]. Med Phys. 2018;10.1002/mp.12964. https://doi.org/10.1002/mp.12964

  32. Han X. MR-based synthetic CT generation using a deep convolutional neural network method. Med Phys. 2017;44:1408–19.

    Article  CAS  PubMed  Google Scholar 

  33. Lee S, Jung JH, Kim D, et al. PET/CT for brain amyloid: a feasibility study for scan time reduction by deep learning. Clin Nucl Med. 2021;46:e133–40.

    Article  PubMed  Google Scholar 

  34. Xie N, Gong K, Guo N, et al. Rapid high-quality PET Patlak parametric image generation based on direct reconstruction and temporal nonlocal neural network. Neuroimage. 2021;240:118380.

    Article  PubMed  Google Scholar 

  35. Matsubara K, Ibaraki M, Shinohara Y, Takahashi N, Toyoshima H, Kinoshita T. Prediction of an oxygen extraction fraction map by convolutional neural network: validation of input data among MR and PET images. International Journal of Computer Assisted Radiology and Surgery 2021;16:1865–1874

  36. Liu H, Nai YH, Saridin F, et al. Improved amyloid burden quantification with nonspecific estimates using deep learning. Eur J Nucl Med Mol Imaging. 2021;48:1842–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Gao Y, Li Z, Song C, et al. Automatic rat brain image segmentation using triple cascaded convolutional neural networks in a clinical PET/MR. Phys Med Biol. 2021;66:04NT01.

    Article  PubMed  Google Scholar 

  38. Kang SK, Seo S, Shin SA, et al. Adaptive template generation for amyloid PET using a deep learning approach. Hum Brain Mapp. 2018;39:3769–78.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Hatt M, Laurent B, Ouahabi A, et al. The first MICCAI challenge on PET tumor segmentation. Med Image Anal. 2018;44:177–95.

    Article  PubMed  Google Scholar 

  40. Blanc-Durand P, Van Der Gucht A, Schaefer N, Itti E, Prior JO. Automatic lesion detection and segmentation of 18F-FET PET in gliomas: a full 3D U-Net convolutional neural network study. PLoS One. 2018;13:0195798.

    Google Scholar 

  41. Xiong X, Linhardt TJ, Liu W, et al. A 3D deep convolutional neural network approach for the automated measurement of cerebellum tracer uptake in FDG PET-CT scans. Med Phys. 2020;47:1058–66.

    Article  PubMed  Google Scholar 

  42. Reith F, Koran ME, Davidzon G, Zaharchuk G. Application of deep learning to predict standardized uptake value ratio and amyloid status on (18)F-Florbetapir PET using ADNI data. AJNR Am J Neuroradiol. 2020;41:980–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Guo J, Gong E, Fan AP, Goubran M, Khalighi MM, Zaharchuk G. Predicting (15)O-Water PET cerebral blood flow maps from multi-contrast MRI using a deep convolutional neural network with evaluation of training cohort bias. J Cereb Blood Flow Metab. 2020;40:2240–53.

    Article  PubMed  Google Scholar 

  44. Nobashi T, Zacharias C, Ellis JK, et al. Performance comparison of individual and ensemble CNN models for the classification of brain 18F-FDG-PET scans. J Digit Imaging. 2020;33:447–55.

    Article  PubMed  Google Scholar 

  45. Ma D, Yee E, Stocks JK, et al. Blinded clinical evaluation for dementia of alzheimer’s type classification using FDG-PET: a comparison between feature-engineered and non-feature-engineered machine learning methods. J Alzheimers Dis. 2021;80:715–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lee SY, Kang H, Jeong JH, Kang DY. Performance evaluation in [18F]Florbetaben brain PET images classification using 3D Convolutional Neural Network. PLoS One. 2021;16:e0258214.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Etminani K, Soliman A, Davidsson A, et al. A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer's disease, and mild cognitive impairment using brain 18F-FDG PET. Eur J Nucl Med Mol Imaging. 2021 Jul 30. https://doi.org/10.1007/s00259-021-05483-0. Online ahead of print.

  48. de Vries BM, Golla SSV, Ebenau J, et al. Classification of negative and positive (18)F-florbetapir brain PET studies in subjective cognitive decline patients using a convolutional neural network. Eur J Nucl Med Mol Imaging. 2021;48:721–8.

    Article  PubMed  Google Scholar 

  49. Yee E, Popuri K, Beg MF. Quantifying brain metabolism from FDG-PET images into a probability of Alzheimer’s dementia score. Hum Brain Mapp. 2020;41:5–16.

    Article  PubMed  Google Scholar 

  50. Kim HW, Lee HE, Oh K, Lee S, Yun M, Yoo SK. Multi-slice representational learning of convolutional neural network for Alzheimer’s disease classification using positron emission tomography. Biomed Eng Online. 2020;19:70.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Jo T, Nho K, Risacher SL, Saykin AJ, Alzheimer’s NI. Deep learning detection of informative features in tau PET for Alzheimer’s disease classification. BMC Bioinformatics. 2020;21:496.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Liu M, Cheng D, Yan W. Classification of Alzheimer’s disease by combination of convolutional and recurrent neural networks using FDG-PET images. Front Neuroinform. 2018;12:35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Song J, Zheng J, Li P, Lu X, Zhu G, Shen P. An effective multimodal image fusion method using MRI and PET for Alzheimer’s disease diagnosis. Front Digit Health. 2021;3:637386.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Lin W, Lin W, Chen G, et al. Bidirectional mapping of brain MRI and PET with 3D reversible GAN for the diagnosis of Alzheimer’s disease. Front Neurosci. 2021;15:646013.

    Article  PubMed  PubMed Central  Google Scholar 

  55. He Y, Wu J, Zhou L, Chen Y, Li F, Qian H. Quantification of cognitive function in Alzheimer’s disease based on deep learning. Front Neurosci. 2021;15:651920.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Abdelaziz M, Wang T, Elazab A. Alzheimer’s disease diagnosis framework from incomplete multimodal data using convolutional neural networks. J Biomed Inform. 2021;121:103863.

    Article  PubMed  Google Scholar 

  57. Li R, Zhang W, Suk HI, et al. Deep learning based imaging data completion for improved brain disease diagnosis. Med Image Comput Comput Assist Interv. 2014;17:305–12.

    PubMed  PubMed Central  Google Scholar 

  58. Tufail AB, Ma YK, Zhang QN, et al. 3D convolutional neural networks-based multiclass classification of Alzheimer’s and Parkinson’s diseases using PET and SPECT neuroimaging modalities. Brain Inform. 2021;8:23.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Piccardo A, Cappuccio R, Bottoni G, et al. The role of the deep convolutional neural network as an aid to interpreting brain [(18)F]DOPA PET/CT in the diagnosis of Parkinson’s disease. Eur Radiol. 2021;31:7003–11.

    Article  PubMed  Google Scholar 

  60. Reith FH, Mormino EC, Zaharchuk G. Predicting future amyloid biomarkers in dementia patients with machine learning to improve clinical trial patient selection. Alzheimers Dement (N Y). 2021;7:e12212.

    Google Scholar 

  61. Choi H, Jin KH. Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behav Brain Res. 2018;344:103–9.

    Article  CAS  PubMed  Google Scholar 

  62. Papp L, Potsch N, Grahovac M, et al. Glioma survival prediction with combined analysis of in vivo (11)C-MET PET features, ex vivo features, and patient features by supervised machine learning. J Nucl Med. 2018;59:892–9.

    Article  CAS  PubMed  Google Scholar 

  63. U.S. Food adn Drug Administratin (FDA). Artificial Intelligence and Machine Learning in Software as a Medical Device. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device. Accessed January 2021

Download references

Author information

Authors and Affiliations

  1. Department of Radiology and Imaging Sciences, University of Utah, 30 North 1900 East #1A071, Salt Lake City, UT, 84132, USA

    Satoshi Minoshima & Donna Cross

Authors
  1. Satoshi Minoshima
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Donna Cross
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Satoshi Minoshima.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Minoshima, S., Cross, D. Application of artificial intelligence in brain molecular imaging. Ann Nucl Med 36, 103–110 (2022). https://doi.org/10.1007/s12149-021-01697-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12149-021-01697-2

keywords

Search

Navigation