European Radiology

, Volume 29, Issue 12, pp 6867–6879 | Cite as

Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC)

  • Isaac Shiri
  • Pardis GhafarianEmail author
  • Parham Geramifar
  • Kevin Ho-Yin Leung
  • Mostafa Ghelichoghli
  • Mehrdad Oveisi
  • Arman Rahmim
  • Mohammad Reza AyEmail author
Imaging Informatics and Artificial Intelligence



To obtain attenuation-corrected PET images directly from non-attenuation-corrected images using a convolutional encoder-decoder network.


Brain PET images from 129 patients were evaluated. The network was designed to map non-attenuation-corrected (NAC) images to pixel-wise continuously valued measured attenuation-corrected (MAC) PET images via an encoder-decoder architecture. Image quality was evaluated using various evaluation metrics. Image quantification was assessed for 19 radiomic features in 83 brain regions as delineated using the Hammersmith atlas (n30r83). Reliability of measurements was determined using pixel-wise relative errors (RE; %) for radiomic feature values in reference MAC PET images.


Peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM) values were 39.2 ± 3.65 and 0.989 ± 0.006 for the external validation set, respectively. RE (%) of SUVmean was − 0.10 ± 2.14 for all regions, and only 3 of 83 regions depicted significant differences. However, the mean RE (%) of this region was 0.02 (range, − 0.83 to 1.18). SUVmax had mean RE (%) of − 3.87 ± 2.84 for all brain regions, and 17 regions in the brain depicted significant differences with respect to MAC images with a mean RE of − 3.99 ± 2.11 (range, − 8.46 to 0.76). Homogeneity amongst Haralick-based radiomic features had the highest number (20) of regions with significant differences with a mean RE (%) of 7.22 ± 2.99.


Direct AC of PET images using deep convolutional encoder-decoder networks is a promising technique for brain PET images. The proposed deep learning method shows significant potential for emission-based AC in PET images with applications in PET/MRI and dedicated brain PET scanners.

Key Points

• We demonstrate direct emission-based attenuation correction of PET images without using anatomical information.

• We performed radiomics analysis of 83 brain regions to show robustness of direct attenuation correction of PET images.

• Deep learning methods have significant promise for emission-based attenuation correction in PET images with potential applications in PET/MRI and dedicated brain PET scanners.


Positron emission tomography Brain imaging Artificial intelligence Deep learning Radiomics 



Attenuation correction


Conditional generative adversarial networks


Convolutional neural network


Deep direct attenuation correction


Field of view


Gray-level co-occurrence matrix


Gray-level run length matrix


Gray-level size zone matrix


Graphics processing unit


Long-run emphasis


Measured attenuation corrected


Mean absolute error


Maximum likelihood reconstruction of activity and attenuation


Magnetic resonance imaging


Mean squared error


Non-attenuation corrected


Ordered subset expectation maximization


Positron emission tomography


Peak signal-to-noise ratio


Restricted Boltzmann machine


Relative errors


Rectified linear unit


Radiomic feature values


Root mean squared error


Run percentage


Short-run emphasis


Structural similarity index metrics


Standard uptake value


Size zone emphasis


Total lesion glycolysis


Time of flight


Ultra-short echo time


Volumes of interest


Zone percentage


Zero echo time



This study has received funding the Tehran University of Medical Sciences under grant number 97-01-30-38001.

Compliance with ethical standards


The scientific guarantor of this publication is Mohammad Reza Ay, PhD, Professor of Medical Physics.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise. And no complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.


• Retrospective

• Experimental

• Performed at one institution

Supplementary material

330_2019_6229_MOESM1_ESM.docx (184 kb)
ESM 1 (DOCX 184 kb)


  1. 1.
    Catana C, Procissi D, Wu Y et al (2008) Simultaneous in vivo positron emission tomography and magnetic resonance imaging. Proc Natl Acad Sci U S A 105:3705–3710CrossRefGoogle Scholar
  2. 2.
    Zanotti-Fregonara P, Chen K, Liow J-S, Fujita M, Innis RB (2011) Image-derived input function for brain PET studies: many challenges and few opportunities. J Cereb Blood Flow Metab 31:1986–1998CrossRefGoogle Scholar
  3. 3.
    Schöll M, Lockhart SN, Schonhaut DR et al (2016) PET imaging of tau deposition in the aging human brain. Neuron 89:971–982CrossRefGoogle Scholar
  4. 4.
    Sedvall G, Farde L, Persson A, Wiesel FA (1986) Imaging of neurotransmitter receptors in the living human brain. Arch Gen Psychiatry 43:995–1005CrossRefGoogle Scholar
  5. 5.
    Innis RB, Cunningham VJ, Delforge J et al (2007) Consensus nomenclature for in vivo imaging of reversibly binding radioligands. J Cereb Blood Flow Metab 27:1533–1539CrossRefGoogle Scholar
  6. 6.
    Okamura N, Yanai K (2017) Brain imaging: applications of tau PET imaging. Nat Rev Neurol 13:197CrossRefGoogle Scholar
  7. 7.
    Nordberg A, Rinne JO, Kadir A, Långström B (2010) The use of PET in Alzheimer disease. Nat Rev Neurol 6:78CrossRefGoogle Scholar
  8. 8.
    Sarikaya I (2015) PET studies in epilepsy. Am J Nucl Med Mol Imaging 5:416PubMedPubMedCentralGoogle Scholar
  9. 9.
    Lammertsma AA (2017) Forward to the past: the case for quantitative PET imaging. J Nucl Med 58:1019–1024CrossRefGoogle Scholar
  10. 10.
    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 43:1130–1155CrossRefGoogle Scholar
  11. 11.
    Delso G, Nuyts J (2018) PET/MRI: attenuation correction. In: Iagaru A, Hope T, Veit-Haibach P (eds) PET/MRI in Oncology. Springer, Cham, pp 53–75Google Scholar
  12. 12.
    Yang J, Wiesinger F, Kaushik S et al (2017) Evaluation of sinus/edge-corrected zero-echo-time–based attenuation correction in brain PET/MRI. J Nucl Med 58:1873–1879CrossRefGoogle Scholar
  13. 13.
    Khateri P, Saligheh Rad H, Jafari AH et al (2015) Generation of a four-class attenuation map for MRI-based attenuation correction of PET data in the head area using a novel combination of STE/Dixon-MRI and FCM clustering. Mol Imaging Biol 17:884–892Google Scholar
  14. 14.
    Mehranian A, Zaidi H (2015) Joint estimation of activity and attenuation in whole-body TOF PET/MRI using constrained Gaussian mixture models. IEEE Trans Med Imaging 34:1808–1821CrossRefGoogle Scholar
  15. 15.
    Akbarzadeh A, Ay MR, Ahmadian A, Alam NR, Zaidi H (2013) MRI-guided attenuation correction in whole-body PET/MR: assessment of the effect of bone attenuation. Ann Nucl Med 27:152–162CrossRefGoogle Scholar
  16. 16.
    Shandiz MS, Rad HS, Ghafarian P, Karam MB, Akbarzadeh A, Ay MR (2017) MR-guided attenuation map for prostate PET-MRI: an intensity and morphologic-based segmentation approach for generating a five-class attenuation map in pelvic region. Ann Nucl Med 31:29–39CrossRefGoogle Scholar
  17. 17.
    Khateri P, Saligheh Rad H, Fathi A, Ay MR (2013) Generation of attenuation map for MR-based attenuation correction of PET data in the head area employing 3D short echo time MR imaging. Nucl Instrum Meth A 702:133–136Google Scholar
  18. 18.
    Kazerooni AF, A’arabi MH, Ay M, Saligheh Rad H (2015) Generation of MR-based attenuation correction map of PET images in the brain employing joint segmentation of skull and soft-tissue from single short-TE MR imaging modality. In: Gao F, Shi K, Li S (eds) Computational Methods for Molecular Imaging. Lecture Notes in Computational Vision and Biomechanics, vol 22. Springer, Cham, pp 139–147Google Scholar
  19. 19.
    Hope T, Tosun D, Khalighi MM et al (2017) Improvement in quantitative amyloid imaging using ZTE-based attenuation correction in PET/MRI. J Nucl Med 58:645Google Scholar
  20. 20.
    Shandiz MS, Saligheh Rad H, Ghafarian P, Yaghoubi K, Ay MR (2018) Capturing bone signal in MRI of pelvis, as a large FOV region, using TWIST sequence and generating a 5-class attenuation map for prostate PET/MRI imaging. Mol Imaging 17:1536012118789314Google Scholar
  21. 21.
    Defrise M, Rezaei A, Nuyts J (2012) Time-of-flight PET data determine the attenuation sinogram up to a constant. Phys Med Biol 57:885CrossRefGoogle Scholar
  22. 22.
    Sevigny J, Suhy J, Chiao P et al (2016) Amyloid PET screening for enrichment of early-stage Alzheimer disease clinical trials. Alzheimer Dis Assoc Disord 30:1–7CrossRefGoogle Scholar
  23. 23.
    Censor Y, Gustafson DE, Lent A, Tuy H (1979) A new approach to the emission computerized tomography problem: simultaneous calculation of attenuation and activity coefficients. IEEE Trans Nucl Sci 26:2775–2779CrossRefGoogle Scholar
  24. 24.
    Rezaei A, Defrise M, Nuyts J (2014) ML-reconstruction for TOF-PET with simultaneous estimation of the attenuation factors. IEEE Trans Med Imaging 33:1563–1572CrossRefGoogle Scholar
  25. 25.
    Mehranian A, Zaidi H (2015) Clinical assessment of emission-and segmentation-based MR-guided attenuation correction in whole-body time-of-flight PET/MR imaging. J Nucl Med 56:877–883CrossRefGoogle Scholar
  26. 26.
    Hemmati H, Kamali-Asl A, Ghafarian P, Ay MR (2018) Reconstruction/segmentation of attenuation map in TOF-PET based on mixture models. Ann Nucl Med 1–11, 32(7):474–484Google Scholar
  27. 27.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436CrossRefGoogle Scholar
  28. 28.
    Yang Q, Li N, Zhao Z, Fan X, Chang EI, Xu Y (2018) MRI image-to-image translation for cross-modality image registration and segmentation. arXiv:180106940Google Scholar
  29. 29.
    Han X (2017) MR-based synthetic CT generation using a deep convolutional neural network method. Med Phys 44:1408–1419CrossRefGoogle Scholar
  30. 30.
    Ben-Cohen A, Klang E, Raskin SP et al (2018) Cross-modality synthesis from CT to PET using FCN and GAN networks for improved automated lesion detection. arXiv:180207846Google Scholar
  31. 31.
    Jin C-B, Jung W, Joo S et al (2018) Deep CT to MR synthesis using paired and unpaired data. arXiv:180510790Google Scholar
  32. 32.
    Liu F, Jang H, Kijowski R, Bradshaw T, McMillan AB (2017) Deep learning MR imaging–based attenuation correction for PET/MR imaging. Radiology 286:676–684CrossRefGoogle Scholar
  33. 33.
    Gong K, Yang J, Kim K, El Fakhri G, Seo Y, Li Q (2018) Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images. Phys Med Biol 63(12):125011Google Scholar
  34. 34.
    Spuhler KD, Gardus J, Gao Y, DeLorenzo C, Parsey R, Huang C (2018) Synthesis of patient-specific transmission image for PET attenuation correction for PET/MR imaging of the brain using a convolutional neural network. J Nucl Med 60(4):555–560Google Scholar
  35. 35.
    Lu Y, Fontaine K, Germino M et al (2018) Investigation of sub-centimeter lung nodule quantification for low-dose PET. IEEE TRPMS 2:41–50Google Scholar
  36. 36.
    Abadi M, Barham P, Chen J et al (2016) TensorFlow: a system for large-scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation, pp 265–283Google Scholar
  37. 37.
    Hammers A, Allom R, Koepp MJ et al (2003) Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe. Hum Brain Mapp 19:224–247CrossRefGoogle Scholar
  38. 38.
    Shiri I, Rahmim A, Ghaffarian P, Geramifar P, Abdollahi H, Bitarafan-Rajabi A (2017) The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies. Eur Radiol 27:4498–4509CrossRefGoogle Scholar
  39. 39.
    van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107CrossRefGoogle Scholar
  40. 40.
    Hemmati H, Kamali-Asl A, Ghafarian P, Ay M (2018) Mixture model based joint-MAP reconstruction of attenuation and activity maps in TOF-PET. J Instrum 13:P06005CrossRefGoogle Scholar
  41. 41.
    Rezaei A, Defrise M, Bal G et al (2012) Simultaneous reconstruction of activity and attenuation in time-of-flight PET. IEEE Trans Med Imaging 31:2224–2233CrossRefGoogle Scholar
  42. 42.
    Salomon A, Goedicke A, Schweizer B, Aach T, Schulz V (2011) Simultaneous reconstruction of activity and attenuation for PET/MR. IEEE Trans Med Imaging 30:804–813CrossRefGoogle Scholar
  43. 43.
    Ladefoged CN, Law I, Anazodo U et al (2017) A multi-centre evaluation of eleven clinically feasible brain PET/MRI attenuation correction techniques using a large cohort of patients. Neuroimage 147:346–359CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Research Center for Molecular and Cellular ImagingTehran University of Medical SciencesTehranIran
  2. 2.Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD)Shahid Beheshti University of Medical SciencesTehranIran
  3. 3.PET/CT and Cyclotron Center, Masih Daneshvari HospitalShahid Beheshti University of Medical SciencesTehranIran
  4. 4.Research Center for Nuclear Medicine, Shariati HospitalTehran University of Medical SciencesTehranIran
  5. 5.Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreUSA
  6. 6.Department of Radiology and Radiological ScienceJohns Hopkins UniversityBaltimoreUSA
  7. 7.Department of Biomedical and Health Informatics, Rajaie Cardiovascular Medical and Research CenterIran University of Medical ScienceTehranIran
  8. 8.Department of Computer ScienceUniversity of British ColumbiaVancouverCanada
  9. 9.Departments of Radiology and Physics & AstronomyUniversity of British ColumbiaVancouverCanada
  10. 10.Department of Integrative Oncology, BC Cancer Research CentreVancouverCanada
  11. 11.Department of Medical Physics and Biomedical Engineering, School of MedicineTehran University of Medical SciencesTehranIran

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