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

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

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

Methods

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.

Results

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.

Conclusions

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.

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Abbreviations

AC:

Attenuation correction

CGAN :

Conditional generative adversarial networks

CNN :

Convolutional neural network

Deep-DAC:

Deep direct attenuation correction

FOV :

Field of view

GLCM :

Gray-level co-occurrence matrix

GLRLM:

Gray-level run length matrix

GLZLM:

Gray-level size zone matrix

GPU:

Graphics processing unit

LRE:

Long-run emphasis

MAC :

Measured attenuation corrected

MAE :

Mean absolute error

MLAA :

Maximum likelihood reconstruction of activity and attenuation

MRI:

Magnetic resonance imaging

MSE :

Mean squared error

NAC :

Non-attenuation corrected

OSEM:

Ordered subset expectation maximization

PET:

Positron emission tomography

PSNR :

Peak signal-to-noise ratio

RBM :

Restricted Boltzmann machine

RE:

Relative errors

ReLU :

Rectified linear unit

RFV :

Radiomic feature values

RMSE :

Root mean squared error

RP:

Run percentage

SRE:

Short-run emphasis

SSIM :

Structural similarity index metrics

SUV:

Standard uptake value

SZE :

Size zone emphasis

TLG:

Total lesion glycolysis

TOF:

Time of flight

UTE :

Ultra-short echo time

VOI :

Volumes of interest

ZP:

Zone percentage

ZTE :

Zero echo time

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Funding

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

Author information

Authors and Affiliations

  1. Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran

    Isaac Shiri & Mohammad Reza Ay

  2. Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Pardis Ghafarian

  3. PET/CT and Cyclotron Center, Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Pardis Ghafarian

  4. Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran

    Parham Geramifar

  5. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Kevin Ho-Yin Leung

  6. Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA

    Kevin Ho-Yin Leung & Arman Rahmim

  7. Department of Biomedical and Health Informatics, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran

    Mostafa Ghelichoghli & Mehrdad Oveisi

  8. Department of Computer Science, University of British Columbia, Vancouver, BC, Canada

    Mehrdad Oveisi

  9. Departments of Radiology and Physics & Astronomy, University of British Columbia, Vancouver, BC, Canada

    Arman Rahmim

  10. Department of Integrative Oncology, BC Cancer Research Centre, Vancouver, BC, Canada

    Arman Rahmim

  11. Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

    Mohammad Reza Ay

Authors
  1. Isaac Shiri
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Corresponding authors

Correspondence to Pardis Ghafarian or Mohammad Reza Ay.

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

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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.

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Shiri, I., Ghafarian, P., Geramifar, P. et al. Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC). Eur Radiol 29, 6867–6879 (2019). https://doi.org/10.1007/s00330-019-06229-1

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  • DOI: https://doi.org/10.1007/s00330-019-06229-1

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