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European Journal of Nuclear Medicine and Molecular Imaging

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01-05-2022 | Computed Tomography | Original Article

Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography

Authors: Donghwi Hwang, Seung Kwan Kang, Kyeong Yun Kim, Hongyoon Choi, Jae Sung Lee

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 6/2022

Abstract

Purpose

This study aims to compare two approaches using only emission PET data and a convolution neural network (CNN) to correct the attenuation (μ) of the annihilation photons in PET.

Methods

One of the approaches uses a CNN to generate μ-maps from the non-attenuation-corrected (NAC) PET images (μ-CNN_NAC). In the other method, CNN is used to improve the accuracy of μ-maps generated using maximum likelihood estimation of activity and attenuation (MLAA) reconstruction (μ-CNN_MLAA). We investigated the improvement in the CNN performance by combining the two methods (μ-CNN_MLAA+NAC) and the suitability of μ-CNN_NAC for providing the scatter distribution required for MLAA reconstruction. Image data from ¹⁸F-FDG (n = 100) or ⁶⁸ Ga-DOTATOC (n = 50) PET/CT scans were used for neural network training and testing.

Results

The error of the attenuation correction factors estimated using μ-CT and μ-CNN_NAC was over 7%, but that of scatter estimates was only 2.5%, indicating the validity of the scatter estimation from μ-CNN_NAC. However, CNN_NAC provided less accurate bone structures in the μ-maps, while the best results in recovering the fine bone structures were obtained by applying CNN_MLAA+NAC. Additionally, the μ-values in the lungs were overestimated by CNN_NAC. Activity images (λ) corrected for attenuation using μ-CNN_MLAA and μ-CNN_MLAA+NAC were superior to those corrected using μ-CNN_NAC, in terms of their similarity to λ-CT. However, the improvement in the similarity with λ-CT by combining the CNN_NAC and CNN_MLAA approaches was insignificant (percent error for lung cancer lesions, λ-CNN_NAC = 5.45% ± 7.88%; λ-CNN_MLAA = 1.21% ± 5.74%; λ-CNN_MLAA+NAC = 1.91% ± 4.78%; percent error for bone cancer lesions, λ-CNN_NAC = 1.37% ± 5.16%; λ-CNN_MLAA = 0.23% ± 3.81%; λ-CNN_MLAA+NAC = 0.05% ± 3.49%).

Conclusion

The use of CNN_NAC was feasible for scatter estimation to address the chicken-egg dilemma in MLAA reconstruction, but CNN_MLAA outperformed CNN_NAC.

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Title: Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography
Authors: Donghwi Hwang
Seung Kwan Kang
Kyeong Yun Kim
Hongyoon Choi
Jae Sung Lee
Publication date: 01-05-2022
Publisher: Springer Berlin Heidelberg
Keywords: Computed Tomography
Computed Tomography
Positron Emission Tomography
Published in: European Journal of Nuclear Medicine and Molecular Imaging / Issue 6/2022
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-021-05637-0

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography

Abstract

Purpose

Methods

Results

Conclusion

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

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