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01-12-2019 | Magnetic Resonance Imaging | Imaging Informatics and Artificial Intelligence

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

Authors: Isaac Shiri, Pardis Ghafarian, Parham Geramifar, Kevin Ho-Yin Leung, Mostafa Ghelichoghli, Mehrdad Oveisi, Arman Rahmim, Mohammad Reza Ay

Published in: European Radiology | Issue 12/2019

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 SUV_mean 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). SUV_max 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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Title: Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC)
Authors: Isaac Shiri
Pardis Ghafarian
Parham Geramifar
Kevin Ho-Yin Leung
Mostafa Ghelichoghli
Mehrdad Oveisi
Arman Rahmim
Mohammad Reza Ay
Publication date: 01-12-2019
Publisher: Springer Berlin Heidelberg
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Positron Emission Tomography
Published in: European Radiology / Issue 12/2019
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-019-06229-1

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Objective

Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objective

Methods

Results

Conclusions

Key Points

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