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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

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.

Keywords

Positron emission tomography Brain imaging Artificial intelligence Deep learning Radiomics 

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

Notes

Funding

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

Compliance with ethical standards

Guarantor

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.

Methodology

• Retrospective

• Experimental

• Performed at one institution

Supplementary material

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

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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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