1932

Abstract

The widespread availability of high-performance computing and the popularity of artificial intelligence (AI) with machine learning and deep learning (ML/DL) algorithms at the helm have stimulated the development of many applications involving the use of AI-based techniques in molecular imaging research. Applications reported in the literature encompass various areas, including innovative design concepts in positron emission tomography (PET) instrumentation, quantitative image reconstruction and analysis techniques, computer-aided detection and diagnosis, as well as modeling and prediction of outcomes. This review reflects the tremendous interest in quantitative molecular imaging using ML/DL techniques during the past decade, ranging from the basic principles of ML/DL techniques to the various steps required for obtaining quantitatively accurate PET data, including algorithms used to denoise or correct for physical degrading factors as well as to quantify tracer uptake and metabolic tumor volume for treatment monitoring or radiation therapy treatment planning and response prediction.This review also addresses future opportunities and current challenges facing the adoption of ML/DL approaches and their role in multimodality imaging.

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2021-07-13
2024-03-28
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Literature Cited

  1. 1. 
    Krizhevsky A, Sutskever I, Hinton GE. 2017. ImageNet classification with deep convolutional neural networks. Commun. ACM 60:84–90
    [Google Scholar]
  2. 2. 
    Choi H. 2018. Deep learning in nuclear medicine and molecular imaging: current perspectives and future directions. Nucl. Med. Mol. Imaging 52:109–18
    [Google Scholar]
  3. 3. 
    Giger ML. 2018. Machine learning in medical imaging. J. Am. Coll. Radiol. 15:512–20
    [Google Scholar]
  4. 4. 
    Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts H. 2018. Artificial intelligence in radiology. Nat. Rev. Cancer 18:500–10
    [Google Scholar]
  5. 5. 
    Liu S, Wang Y, Yang X, Lei B, Liu L et al. 2019. Deep learning in medical ultrasound analysis: a review. Engineering 5:261–75
    [Google Scholar]
  6. 6. 
    Lundervold AS, Lundervold A. 2019. An overview of deep learning in medical imaging focusing on MRI. Z. Med. Phys. 29:102–27
    [Google Scholar]
  7. 7. 
    Langlotz CP, Allen B, Erickson BJ, Kalpathy-Cramer J, Bigelow K et al. 2019. A roadmap for foundational research on artificial intelligence in medical imaging: from the 2018 NIH/RSNA/ACR/The Academy workshop. Radiology 291:190613
    [Google Scholar]
  8. 8. 
    Sahiner B, Pezeshk A, Hadjiiski LM, Wang X, Drukker K et al. 2019. Deep learning in medical imaging and radiation therapy. Med. Phys. 46:e1–36
    [Google Scholar]
  9. 9. 
    Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F et al. 2017. A survey on deep learning in medical image analysis. Med. Image Anal. 42:60–88
    [Google Scholar]
  10. 10. 
    Shen D, Wu G, Suk HI. 2017. Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19:221–48
    [Google Scholar]
  11. 11. 
    El Naqa I, Haider MA, Giger ML, Ten Haken RK 2020. Artificial intelligence: reshaping the practice of radiological sciences in the 21st century. Br. J. Radiol. 93:20190855
    [Google Scholar]
  12. 12. 
    Wang T, Lei Y, Fu Y, Curran WJ, Liu T et al. 2020. Machine learning in quantitative PET: a review of attenuation correction and low-count image reconstruction methods. Phys. Med. 76:294–306
    [Google Scholar]
  13. 13. 
    Arabi H, Zaidi H. 2020. Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy. Eur. J. Hybrid Imaging 4:17
    [Google Scholar]
  14. 14. 
    Hooker JM, Carson RE. 2019. Human positron emission tomography neuroimaging. Annu. Rev. Biomed. Eng. 21:551–81
    [Google Scholar]
  15. 15. 
    Jones T, Townsend D. 2017. History and future technical innovation in positron emission tomography. J. Med. Imaging 4:011013
    [Google Scholar]
  16. 16. 
    Lell MM, Wildberger JE, Alkadhi H, Damilakis J, Kachelriess M. 2015. Evolution in computed tomography: the battle for speed and dose. Investig. Radiol. 50:629–44
    [Google Scholar]
  17. 17. 
    Zhang J, Maniawski P, Knopp MV. 2018. Performance evaluation of the next generation solid-state digital photon counting PET/CT system. EJNMMI Res 8:97
    [Google Scholar]
  18. 18. 
    van Sluis JJ, de Jong J, Schaar J, Noordzij W, van Snick P et al. 2019. Performance characteristics of the digital Biograph Vision PET/CT system. J. Nucl. Med. 60:1031–36
    [Google Scholar]
  19. 19. 
    Pan T, Einstein SA, Kappadath SC, Grogg KS, Lois Gomez C et al. 2019. Performance evaluation of the 5-ring GE Discovery MI PET/CT system using the National Electrical Manufacturers Association NU 2-2012 standard. Med. Phys. 46:3025–33
    [Google Scholar]
  20. 20. 
    Surti S, Pantel AR, Karp JS. 2020. Total body PET: Why, how, what for?. IEEE Trans. Radiat. Plasma Med. Sci. 4:283–92
    [Google Scholar]
  21. 21. 
    Zhang X, Cherry SR, Xie Z, Shi H, Badawi RD, Qi J 2020. Subsecond total-body imaging using ultrasensitive positron emission tomography. PNAS 117:2265–67
    [Google Scholar]
  22. 22. 
    Schaart DR, Ziegler S, Zaidi H. 2020. Achieving 10 ps coincidence time resolution in TOF-PET is an impossible dream. Med. Phys. 47:2721–24
    [Google Scholar]
  23. 23. 
    Lecoq P, Morel C, Prior J, Visvikis D, Gundacker S et al. 2020. Roadmap toward the 10 ps time-of-flight PET challenge. Phys. Med. Biol. 65:21RM01
    [Google Scholar]
  24. 24. 
    Zaidi H, Becker M. 2016. The promise of hybrid PET/MRI: technical advances and clinical applications. IEEE Signal Process. Mag. 33:67–85
    [Google Scholar]
  25. 25. 
    Judenhofer MS, Wehrl HF, Newport DF, Catana C, Siegel SB et al. 2008. Simultaneous PET-MRI: a new approach for functional and morphological imaging. Nat. Med. 14:459–65
    [Google Scholar]
  26. 26. 
    Zaidi H, Ojha N, Morich M, Griesmer J, Hu Z et al. 2011. Design and performance evaluation of a whole-body Ingenuity TF PET-MRI system. Phys. Med. Biol. 56:3091–106
    [Google Scholar]
  27. 27. 
    Delso G, Fürst S, Jakoby B, Ladebeck R, Ganter C et al. 2011. Performance measurements of the Siemens mMR integrated whole-body PET/MR scanner. J. Nucl. Med. 52:1914–22
    [Google Scholar]
  28. 28. 
    Grant AM, Deller TW, Khalighi MM, Maramraju SH, Delso G, Levin CS. 2016. NEMA NU 2-2012 performance studies for the SiPM-based ToF-PET component of the GE SIGNA PET/MR system. Med. Phys. 43:2334–43
    [Google Scholar]
  29. 29. 
    Veit-Haibach P, Kuhn F, Wiesinger F, Delso G, von Schulthess G 2013. PET–MR imaging using a tri-modality PET/CT–MR system with a dedicated shuttle in clinical routine. Magn. Reson. Mater. Phys. Biol. Med. 26:25–35
    [Google Scholar]
  30. 30. 
    Kolb A, Wehrl HF, Hofmann M, Judenhofer MS, Eriksson L et al. 2012. Technical performance evaluation of a human brain PET/MRI system. Eur. Radiol. 22:1776–88
    [Google Scholar]
  31. 31. 
    Cho ZH, Son YD, Kim HK, Kim KN, Oh SH et al. 2008. A fusion PET-MRI system with a high-resolution research tomograph PET and ultra-high field 7.0 T MRI for the molecular-genetic imaging of the brain. Proteomics 8:1302–23
    [Google Scholar]
  32. 32. 
    Liu G, Cao T, Hu L, Zheng J, Pang L et al. 2019. Validation of MR-based attenuation correction of a newly released whole-body simultaneous PET/MR system. Biomed. Res. Int. 2019:8213215
    [Google Scholar]
  33. 33. 
    van der Vos CS, Koopman D, Rijnsdorp S, Arends AJ, Boellaard R et al. 2017. Quantification, improvement, and harmonization of small lesion detection with state-of-the-art PET. Eur. J. Nucl. Med. Mol. Imaging 44:4–16
    [Google Scholar]
  34. 34. 
    Zaidi H, Karakatsanis N. 2018. Towards enhanced PET quantification in clinical oncology. Br. J. Radiol. 91:20170508
    [Google Scholar]
  35. 35. 
    Reader AJ, Zaidi H. 2007. Advances in PET image reconstruction. PET Clin 2:173–90
    [Google Scholar]
  36. 36. 
    Rahmim A, Qi J, Sossi V. 2013. Resolution modeling in PET imaging: theory, practice, benefits, and pitfalls. Med. Phys. 40:064301
    [Google Scholar]
  37. 37. 
    Yip SS, Aerts HJ. 2016. Applications and limitations of radiomics. Phys. Med. Biol. 61:R150–66
    [Google Scholar]
  38. 38. 
    Avanzo M, Stancanello J, El Naqa I 2017. Beyond imaging: the promise of radiomics. Phys. Med. 38:122–39
    [Google Scholar]
  39. 39. 
    Rahmim A, Lodge MA, Karakatsanis NA, Panin VY, Zhou Y et al. 2019. Dynamic whole-body PET imaging: principles, potentials and applications. Eur. J. Nucl. Med. Mol. Imaging 46:501–18
    [Google Scholar]
  40. 40. 
    Zaker N, Kotasidis F, Garibotto V, Zaidi H. 2020. Assessment of lesion detectability in dynamic whole-body PET imaging using compartmental and Patlak parametric mapping. Clin. Nucl. Med. 45:e221–31
    [Google Scholar]
  41. 41. 
    Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
    [Google Scholar]
  42. 42. 
    Hinton GE, Salakhutdinov RR. 2006. Reducing the dimensionality of data with neural networks. Science 313:504–7
    [Google Scholar]
  43. 43. 
    LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
    [Google Scholar]
  44. 44. 
    Lecun Y, Bottou L, Bengio Y, Haffner P. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86:2278–324
    [Google Scholar]
  45. 45. 
    Goodfellow I, Bengio Y, Courville A. 2016. Deep Learning Cambridge, MA: MIT Press
  46. 46. 
    Khan A, Sohail A, Zahoora U, Qureshi AS. 2019. A survey of the recent architectures of deep convolutional neural networks. arXiv:1901.06032v7 [cs.CV]
  47. 47. 
    Hochreiter S, Schmidhuber J. 1997. Long short-term memory. Neural Comput 9:1735–80
    [Google Scholar]
  48. 48. 
    Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F et al. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv:1406.1078 [cs.CL]
  49. 49. 
    Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D et al. 2014. Generative adversarial networks. arXiv:1406.2661 [stat.ML]
  50. 50. 
    Liu X, Faes L, Kale AU, Wagner SK, Fu DJ et al. 2019. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit. Health 1:e271–97
    [Google Scholar]
  51. 51. 
    Gong K, Berg E, Cherry SR, Qi J. 2020. Machine learning in PET: from photon detection to quantitative image reconstruction. Proc. IEEE 108:51–68
    [Google Scholar]
  52. 52. 
    Zaharchuk G. 2019. Next generation research applications for hybrid PET/MR and PET/CT imaging using deep learning. Eur. J. Nucl. Med. Mol. Imaging 46:2700–7
    [Google Scholar]
  53. 53. 
    Ravishankar H, Sudhakar P, Venkataramani R, Thiruvenkadam S, Annangi P et al. 2016. Understanding the mechanisms of deep transfer learning for medical images. Deep Learning and Data Labeling for Medical Applications G Carneiro, D Mateus, P Loïc, A Bradley, JMRS Tavares et al.188–96 Berlin: Springer
    [Google Scholar]
  54. 54. 
    Wang H, Zhou Z, Li Y, Chen Z, Lu P et al. 2017. Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small-cell lung cancer from 18F-FDG PET/CT images. EJNMMI Res 7:11
    [Google Scholar]
  55. 55. 
    Zhu B, Liu JZ, Cauley SF, Rosen BR, Rosen MS. 2018. Image reconstruction by domain-transform manifold learning. Nature 555:487–92
    [Google Scholar]
  56. 56. 
    Wang Q, Liu Z, Ziegler SI, Shi K. 2015. Enhancing spatial resolution of 18F positron imaging with the Timepix detector by classification of primary fired pixels using support vector machine. Phys. Med. Biol. 60:5261–78
    [Google Scholar]
  57. 57. 
    Sanaat A, Zaidi H. 2020. Depth of interaction estimation in a preclinical PET scanner equipped with monolithic crystals coupled to SiPMs using a deep neural network. Appl. Sci. 10:4753
    [Google Scholar]
  58. 58. 
    Berg E, Cherry SR. 2018. Using convolutional neural networks to estimate time-of-flight from PET detector waveforms. Phys. Med. Biol. 63:02LT1
    [Google Scholar]
  59. 59. 
    Whiteley W, Gregor J. 2019. CNN-based PET sinogram repair to mitigate defective block detectors. Phys. Med. Biol. 64:235017
    [Google Scholar]
  60. 60. 
    Häggström I, Schmidtlein CR, Campanella G, Fuchs TJ. 2019. DeepPET: a deep encoder–decoder network for directly solving the PET image reconstruction inverse problem. Med. Image Anal. 54:253–62
    [Google Scholar]
  61. 61. 
    Reader AJ, Corda G, Mehranian A, da Costa-Luis C, Ellis S, Schnabel JA 2021. Deep learning for PET image reconstruction. IEEE Trans. Radiat. Plasma Med. Sci. 5:1–25
    [Google Scholar]
  62. 62. 
    Gong K, Guan J, Kim K, Zhang X, Yang J et al. 2019. Iterative PET image reconstruction using convolutional neural network representation. IEEE Trans. Med. Imaging 38:675–85
    [Google Scholar]
  63. 63. 
    Mehranian A, Reader AJ. 2021. Model-based deep learning PET image reconstruction using forward-backward splitting expectation maximisation. IEEE Trans. Radiat. Plasma Med. Sci. 5:54–64
    [Google Scholar]
  64. 64. 
    Mehranian A, Arabi H, Zaidi H. 2016. Vision 20/20: magnetic resonance imaging–guided attenuation correction in PET/MRI: challenges, solutions, and opportunities. Med. Phys. 43:1130–55
    [Google Scholar]
  65. 65. 
    Arabi H, Zeng G, Zheng G, Zaidi H. 2019. Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI. Eur. J. Nucl. Med. Mol. Imaging 46:2746–59
    [Google Scholar]
  66. 66. 
    Liu F, Jang H, Kijowski R, Bradshaw T, McMillan AB. 2018. Deep learning MR imaging–based attenuation correction for PET/MR imaging. Radiology 286:676–84
    [Google Scholar]
  67. 67. 
    Leynes AP, Yang J, Wiesinger F, Kaushik SS, Shanbhag DD et al. 2018. Zero-echo-time and Dixon deep pseudo-CT (ZeDD CT): direct generation of pseudo-CT images for pelvic PET/MRI attenuation correction using deep convolutional neural networks with multiparametric MRI. J. Nucl. Med. 58:852–58
    [Google Scholar]
  68. 68. 
    Hwang D, Kang SK, Kim KY, Seo S, Paeng JC et al. 2019. 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. 60:1183–89
    [Google Scholar]
  69. 69. 
    Torrado-Carvajal A, Vera-Olmos J, Izquierdo-Garcia D, Catalano OA, Morales MA et al. 2019. Dixon-VIBE deep learning (DIVIDE) pseudo-CT synthesis for pelvis PET/MR attenuation correction. J. Nucl. Med. 60:429–35
    [Google Scholar]
  70. 70. 
    Pozaruk A, Pawar K, Li S, Carey A, Cheng J et al. 2021. Augmented deep learning model for improved quantitative accuracy of MR-based PET attenuation correction in PSMA PET-MRI prostate imaging. Eur. J. Nucl. Med. Mol. Imaging 41:9–20
    [Google Scholar]
  71. 71. 
    Ladefoged CN, Hansen AE, Henriksen OM, Bruun FJ, Eikenes L et al. 2020. AI-driven attenuation correction for brain PET/MRI: clinical evaluation of a dementia cohort and importance of the training group size. NeuroImage 222:117221
    [Google Scholar]
  72. 72. 
    Mostafapour S, Gholamiankhah F, Dadgar H, Arabi H, Zaidi H. 2021. Feasibility of deep learning–guided attenuation and scatter correction of whole-body 68Ga-PSMA PET studies in the image domain. Clin. Nucl. Med In press. https://doi.org/10.1097/RLU.0000000000003585
    [Crossref] [Google Scholar]
  73. 73. 
    Arabi H, Koutsouvelis N, Rouzaud M, Miralbell R, Zaidi H. 2016. Atlas-guided generation of pseudo-CT images for MRI-only and hybrid PET-MRI-guided radiotherapy treatment planning. Phys. Med. Biol. 61:6531–52
    [Google Scholar]
  74. 74. 
    Arabi H, Zaidi H. 2020. Truncation compensation and metallic dental implant artefact reduction in PET/MRI attenuation correction using deep learning–based object completion. Phys. Med. Biol. 65:195002
    [Google Scholar]
  75. 75. 
    Zaidi H, Koral KF. 2004. Scatter modelling and compensation in emission tomography. Eur. J. Nucl. Med. Mol. Imaging 31:761–82
    [Google Scholar]
  76. 76. 
    Xiang H, Lim H, Fessler JA, Dewaraja YK. 2020. A deep neural network for fast and accurate scatter estimation in quantitative SPECT/CT under challenging scatter conditions. Eur. J. Nucl. Med. Mol. Imaging 47:2956–67
    [Google Scholar]
  77. 77. 
    Yang J, Park D, Gullberg GT, Seo Y. 2019. Joint correction of attenuation and scatter in image space using deep convolutional neural networks for dedicated brain 18F-FDG PET. Phys. Med. Biol. 64:075019
    [Google Scholar]
  78. 78. 
    Shiri I, Ghafarian P, Geramifar P, Leung KH, Ghelichoghli M et al. 2019. Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder–decoder (Deep-DAC). Eur. Radiol. 29:6867–79
    [Google Scholar]
  79. 79. 
    Dong X, Lei Y, Wang T, Higgins K, Liu T et al. 2020. Deep learning–based attenuation correction in the absence of structural information for whole-body PET imaging. Phys. Med. Biol. 65:055011
    [Google Scholar]
  80. 80. 
    Shiri I, Arabi H, Geramifar P, Hajianfar G, Ghafarian P et al. 2020. Deep-JASC: joint attenuation and scatter correction in whole-body 18F-FDG PET using a deep residual network. Eur. J. Nucl. Med. Mol. Imaging 47:2533–48
    [Google Scholar]
  81. 81. 
    Arabi H, Bortolin K, Ginovart N, Garibotto V, Zaidi H. 2020. Deep learning–guided joint attenuation and scatter correction in multitracer neuroimaging studies. Hum. Brain Mapp. 41:3667–79
    [Google Scholar]
  82. 82. 
    Pham DL, Xu C, Prince JL. 2000. Current methods in medical image segmentation. Annu. Rev. Biomed. Eng. 2:315–37
    [Google Scholar]
  83. 83. 
    Seo H, Badiei Khuzani M, Vasudevan V, Huang C, Ren H et al. 2020. Machine learning techniques for biomedical image segmentation: an overview of technical aspects and introduction to state-of-art applications. Med. Phys. 47:e148–67
    [Google Scholar]
  84. 84. 
    Zaidi H, Xu XG. 2007. Computational anthropomorphic models of the human anatomy: the path to realistic Monte Carlo modeling in medical imaging. Annu. Rev. Biomed. Eng. 9:471–500
    [Google Scholar]
  85. 85. 
    Hatt M, Lee J, Schmidtlein CR, El Naqa I, Caldwell C et al. 2017. Classification and evaluation strategies of auto-segmentation approaches for PET: report of AAPM Task Group no. 211. Med. Phys. 44:e1–42
    [Google Scholar]
  86. 86. 
    Xie T, Zaidi H. 2019. Estimation of the radiation dose in pregnancy: an automated patient-specific model using convolutional neural networks. Eur. Radiol. 29:6805–15
    [Google Scholar]
  87. 87. 
    Chen L, Shen C, Zhou Z, Maquilan G, Albuquerque K et al. 2019. Automatic PET cervical tumor segmentation by combining deep learning and anatomic prior. Phys. Med. Biol. 64:085019
    [Google Scholar]
  88. 88. 
    Li L, Zhao X, Lu W, Tan S 2020. Deep learning for variational multimodality tumor segmentation in PET/CT. Neurocomputing 392:277–95
    [Google Scholar]
  89. 89. 
    Blanc-Durand P, Van Der Gucht A, Schaefer N, Itti E, Prior JO. 2018. Automatic lesion detection and segmentation of 18F-FET PET in gliomas: a full 3D U-Net convolutional neural network study. PLOS ONE 13:e0195798
    [Google Scholar]
  90. 90. 
    Lu Y, Lin J, Chen S, He H, Cai Y. 2020. Automatic tumor segmentation by means of deep convolutional U-Net with pre-trained encoder in PET images. IEEE Access 8:113636–48
    [Google Scholar]
  91. 91. 
    Arabi H, Shiri I, Janebi E, Becker M, Zaidi H. 2020. Deep learning–based automated delineation of head and neck malignant lesions from PET images Paper presented at IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), online, Oct. 31–Nov. 7
  92. 92. 
    Fu Y, Lei Y, Wang T, Curran WJ, Liu T, Yang X 2020. Deep learning in medical image registration: a review. Phys. Med. Biol. 65:20TR01
    [Google Scholar]
  93. 93. 
    de Vos BD, Berendsen FF, Viergever MA, Sokooti H, Staring M, Isgum I. 2019. A deep learning framework for unsupervised affine and deformable image registration. Med. Image Anal. 52:128–43
    [Google Scholar]
  94. 94. 
    Li T, Zhang M, Qi W, Asma E, Qi J. 2020. Motion correction of respiratory-gated PET images using deep learning based image registration framework. Phys. Med. Biol. 65:155003
    [Google Scholar]
  95. 95. 
    Wang Y, Yu B, Wang L, Zu C, Lalush DS et al. 2018. 3D conditional generative adversarial networks for high-quality PET image estimation at low dose. NeuroImage 174:550–62
    [Google Scholar]
  96. 96. 
    Ouyang J, Chen KT, Gong E, Pauly J, Zaharchuk G 2019. Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss. Med. Phys. 46:3555–64
    [Google Scholar]
  97. 97. 
    Chen KT, Gong E, de Carvalho Macruz FB, Xu J, Boumis A et al. 2019. Ultra-low-dose 18F-florbetaben amyloid PET imaging using deep learning with multi-contrast MRI inputs. Radiology 290:649–56
    [Google Scholar]
  98. 98. 
    Kaplan S, Zhu Y-M. 2019. Full-dose PET image estimation from low-dose PET image using deep learning: a pilot study. J. Digit. Imaging 32:773–78
    [Google Scholar]
  99. 99. 
    Zhou L, Schaefferkoetter JD, Tham IWK, Huang G, Yan J 2020. Supervised learning with cyclegan for low-dose FDG PET image denoising. Med. Image Anal. 65:101770
    [Google Scholar]
  100. 100. 
    Sanaat A, Arabi H, Mainta I, Garibotto V, Zaidi H. 2020. Projection-space implementation of deep learning–guided low-dose brain PET imaging improves performance over implementation in image-space. J. Nucl. Med. 61:1388–96
    [Google Scholar]
  101. 101. 
    Cui J, Gong K, Guo N, Wu C, Meng X et al. 2019. PET image denoising using unsupervised deep learning. Eur. J. Nucl. Med. Mol. Imaging 46:2780–89
    [Google Scholar]
  102. 102. 
    Lu W, Onofrey JA, Lu Y, Shi L, Ma T et al. 2019. An investigation of quantitative accuracy for deep learning based denoising in oncological PET. Phys. Med. Biol. 64:165019
    [Google Scholar]
  103. 103. 
    Gong K, Guan J, Liu C, Qi J. 2019. PET image denoising using a deep neural network through fine tuning. IEEE Trans. Radiat. Plasma Med. Sci. 3:153–61
    [Google Scholar]
  104. 104. 
    Lei Y, Dong X, Wang T, Higgins K, Liu T et al. 2019. Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks. Phys. Med. Biol. 64:215017
    [Google Scholar]
  105. 105. 
    Lei Y, Wang T, Dong X, Higgins K, Liu T et al. 2020. Low dose PET imaging with CT-aided cycle-consistent adversarial networks. Proc. SPIE 11312:1131247
    [Google Scholar]
  106. 106. 
    Sanaat A, Shiri I, Arabi H, Mainta I, Nkoulou R, Zaidi H. 2020. Whole-body PET image synthesis from low-dose images using cycle-consistent generative adversarial networks Paper presented at IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), online, Oct. 31–Nov. 7
  107. 107. 
    Shiri I, AmirMozafari Sabet K, Arabi H, Pourkeshavarz M, Teimourian B et al. 2020. Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks. J. Nucl. Cardiol. https://doi.org/10.1007/s12350-020-02119-y
    [Crossref] [Google Scholar]
  108. 108. 
    Ramon AJ, Yang Y, Pretorius PH, Johnson KL, King MA, Wernick MN. 2020. Improving diagnostic accuracy in low-dose SPECT myocardial perfusion imaging with convolutional denoising networks. IEEE Trans. Med. Imaging 39:2893–903
    [Google Scholar]
  109. 109. 
    Song T-A, Chowdhury SR, Yang F, Dutta J 2020. PET image super-resolution using generative adversarial networks. Neural Netw 125:83–91
    [Google Scholar]
  110. 110. 
    Nguyen D, Long T, Jia X, Lu W, Gu X et al. 2019. A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning. Sci. Rep. 9:1076
    [Google Scholar]
  111. 111. 
    Xing Y, Zhang Y, Nguyen D, Lin M-H, Lu W, Jiang S 2020. Boosting radiotherapy dose calculation accuracy with deep learning. J. Appl. Clin. Med. Phys. 21:149–59
    [Google Scholar]
  112. 112. 
    Mao X, Pineau J, Keyes R, Enger SA. 2020. RapidBrachyDL: rapid radiation dose calculations in brachytherapy via deep learning. Int. J. Radiat. Oncol. Biol. Phys. 108:802–12
    [Google Scholar]
  113. 113. 
    Lee MS, Hwang D, Kim JH, Lee JS. 2019. Deep-dose: a voxel dose estimation method using deep convolutional neural network for personalized internal dosimetry. Sci. Rep. 9:10308
    [Google Scholar]
  114. 114. 
    Götz TI, Schmidkonz C, Chen S, Al-Baddai S, Kuwert T, Lang EW. 2020. A deep learning approach to radiation dose estimation. Phys. Med. Biol. 65:035007
    [Google Scholar]
  115. 115. 
    Akhavanallaf A, Shiri I, Arabi H, Zaidi H. 2020. Whole-body voxel-based internal dosimetry using deep learning. Eur. J. Nucl. Med. Mol. Imaging. https://doi.org/10.1007/s00259-020-05013-4
    [Crossref] [Google Scholar]
  116. 116. 
    Ding Y, Sohn JH, Kawczynski MG, Trivedi H, Harnish R et al. 2019. A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the brain. Radiology 290:456–64
    [Google Scholar]
  117. 117. 
    Sibille L, Seifert R, Avramovic N, Vehren T, Spottiswoode B et al. 2019. 18F-FDG PET/CT uptake classification in lymphoma and lung cancer by using deep convolutional neural networks. Radiology 294:191114
    [Google Scholar]
  118. 118. 
    Avanzo M, Wei L, Stancanello J, Vallieres M, Rao A et al. 2020. Machine and deep learning methods for radiomics. Med. Phys. 47:e185–202
    [Google Scholar]
  119. 119. 
    Zaidi H, Alavi A, El Naqa I 2018. Novel quantitative PET techniques for clinical decision support in oncology. Semin. Nucl. Med. 48:548–64
    [Google Scholar]
  120. 120. 
    Papp L, Poetsch N, Grahovac M, Schmidbauer V, Woehrer A et al. 2018. Glioma survival prediction with the combined analysis of in vivo 11C-MET-PET, ex vivo and patient features by supervised machine learning. J. Nucl. Med. 59:892–99
    [Google Scholar]
  121. 121. 
    Waninger JJ, Green MD, Cheze Le Rest C, Rosen B, El Naqa I 2019. Integrating radiomics into clinical trial design. Q. J. Nucl. Med. Mol. Imaging 63:339–46
    [Google Scholar]
  122. 122. 
    Li Z, Wang Y, Yu J, Guo Y, Cao W. 2017. Deep learning based radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma. Sci. Rep. 7:5467
    [Google Scholar]
  123. 123. 
    Wei L, Osman S, Hatt M, El Naqa I 2019. Machine learning for radiomics-based multimodality and multiparametric modeling. Q. J. Nucl. Med. Mol. Imaging 63:323–38
    [Google Scholar]
  124. 124. 
    Shiri I, Maleki H, Hajianfar G, Abdollahi H, Ashrafinia S et al. 2020. Next-generation radiogenomics sequencing for prediction of EGFR and KRAS mutation status in NSCLC patients using multimodal imaging and machine learning algorithms. Mol. Imaging Biol. 22:1132–48
    [Google Scholar]
  125. 125. 
    Hutson M. 2018. Artificial intelligence faces reproducibility crisis. Science 359:725–26
    [Google Scholar]
  126. 126. 
    Carter RE, Attia ZI, Lopez-Jimenez F, Friedman PA. 2019. Pragmatic considerations for fostering reproducible research in artificial intelligence. npj Digit. Med. 2:42
    [Google Scholar]
  127. 127. 
    Prior F, Smith K, Sharma A, Kirby J, Tarbox L et al. 2017. The public cancer radiology imaging collections of the Cancer Imaging Archive. Sci. Data 4:170124
    [Google Scholar]
  128. 128. 
    Hatt M, Laurent B, Ouahabi A, Fayad H, Tan S et al. 2018. The first MICCAI challenge on PET tumor segmentation. Med. Image Anal. 44:177–95
    [Google Scholar]
  129. 129. 
    Andrearczyk V, Oreiller V, Jreige M, Vallières M, Castelli J et al. 2021. Overview of the HECKTOR Challenge at MICCAI 2020: automatic head and neck tumor segmentation in PET/CT. Head and Neck Tumor Segmentation: HECKTOR 2020 V Andrearczyk, V Oreiller, A Depeursinge 1–21 Cham, Switz: Springer
    [Google Scholar]
  130. 130. 
    Elhalawani H. 2018. 18F-FDG PET risk stratifiers in head and neck cancer: a MICCAI 2018 CPM Grand Challenge Grand Challenge Overview, MICCAI Granada, Spain: https://www.kaggle.com/c/pet-radiomics-challenges Accessed February 2021
  131. 131. 
    Dewaraja Y, Frey E, Sunderland J, Uribe C. 2021. 177Lu dosimetry challenge of the SNMMI Dosimetry Task Force Grand Challenge Overview, SNMMI Reston, VA: https://therapy.snmmi.org/SNMMI-THERAPY/Dosimetry_Challenge.aspx
  132. 132. 
    Armato SG 3rd, Farahani K, Zaidi H 2020. Biomedical image analysis challenges should be considered as an academic exercise, not an instrument that will move the field forward in a real, practical way. Med. Phys. 47:2325–28
    [Google Scholar]
  133. 133. 
    Maier-Hein L, Eisenmann M, Reinke A, Onogur S, Stankovic M et al. 2018. Why rankings of biomedical image analysis competitions should be interpreted with care. Nat. Commun. 9:5217
    [Google Scholar]
  134. 134. 
    Kagadis GC, Kloukinas C, Moore K, Philbin J, Papadimitroulas P et al. 2013. Cloud computing in medical imaging. Med. Phys. 40:070901
    [Google Scholar]
  135. 135. 
    Guinney J, Saez-Rodriguez J. 2018. Alternative models for sharing confidential biomedical data. Nat. Biotechnol. 36:391–92
    [Google Scholar]
  136. 136. 
    Castro DC, Walker I, Glocker B. 2020. Causality matters in medical imaging. Nat. Commun. 11:3673
    [Google Scholar]
  137. 137. 
    Cui S, Tseng HH, Pakela J, Ten Haken RK, El Naqa I 2020. Introduction to machine and deep learning for medical physicists. Med. Phys. 47:e127–47
    [Google Scholar]
  138. 138. 
    Cherkassky VS, Mulier F. 2007. Learning from Data: Concepts, Theory, and Methods Hoboken, NJ: Wiley Intersci.
  139. 139. 
    El Naqa I, Ruan D, Valdes G, Dekker A, McNutt T et al. 2018. Machine learning and modeling: data, validation, communication challenges. Med. Phys. 45:e834–40
    [Google Scholar]
  140. 140. 
    Collins GS, Reitsma JB, Altman DG, Moons KM. 2015. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. Ann. Intern. Med. 162:55–63
    [Google Scholar]
  141. 141. 
    Luo Y, Tseng H-H, Cui S, Wei L, Haken RKT, El Naqa I 2019. Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling. BJR|Open 1:20190021
    [Google Scholar]
  142. 142. 
    Philbrick KA, Yoshida K, Inoue D, Akkus Z, Kline TL et al. 2018. What does deep learning see? Insights from a classifier trained to predict contrast enhancement phase from CT images. Am. J. Roentgenol. 211:1184–93
    [Google Scholar]
  143. 143. 
    Seah JCY, Tang JSN, Kitchen A, Gaillard F, Dixon AF. 2019. Chest radiographs in congestive heart failure: visualizing neural network learning. Radiology 290:514–22
    [Google Scholar]
  144. 144. 
    Luna JM, Gennatas ED, Ungar LH, Eaton E, Diffenderfer ES et al. 2019. Building more accurate decision trees with the additive tree. PNAS 116:19887–93
    [Google Scholar]
  145. 145. 
    Nazmul Haque K, Latif S, Rana R 2019. Disentangled representation learning with information maximizing autoencoder. arXiv:1904.08613 [cs.LG]
  146. 146. 
    Maier AK, Syben C, Stimpel B, Würfl T, Hoffmann M et al. 2019. Learning with known operators reduces maximum error bounds. Nat. Mach. Intell. 1:373–80
    [Google Scholar]
  147. 147. 
    Cova TFGG, Bento DJ, Nunes SCC. 2019. Computational approaches in theranostics: mining and predicting cancer data. Pharmaceutics 11:119
    [Google Scholar]
  148. 148. 
    Liu CC, Huang HM. 2019. Partial-ring PET image restoration using a deep learning based method. Phys. Med. Biol. 64:225014
    [Google Scholar]
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