Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography.
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 (...
| Published in: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 49; no. 6; pp. 1833 - 1843 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
May2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=156400580&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156400580 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: May2022 vid: 49 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 156400580 154056413 10.1007/s00259-021-05637-0 156400580 ppf: 1833 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography. aug: au: Hwang, Donghwi Kang, Seung Kwan Kim, Kyeong Yun Choi, Hongyoon Lee, Jae Sung affil: Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea sug: ab: 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-CNNNAC). 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-CNNMLAA). We investigated the improvement in the CNN performance by combining the two methods (μ-CNNMLAA+NAC) and the suitability of μ-CNNNAC 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-CNNNAC was over 7%, but that of scatter estimates was only 2.5%, indicating the validity of the scatter estimation from μ-CNNNAC. However, CNNNAC provided less accurate bone structures in the μ-maps, while the best results in recovering the fine bone structures were obtained by applying CNNMLAA+NAC. Additionally, the μ-values in the lungs were overestimated by CNNNAC. Activity images (λ) corrected for attenuation using μ-CNNMLAA and μ-CNNMLAA+NAC were superior to those corrected using μ-CNNNAC, in terms of their similarity to λ-CT. However, the improvement in the similarity with λ-CT by combining the CNNNAC and CNNMLAA approaches was insignificant (percent error for lung cancer lesions, λ-CNNNAC = 5.45% ± 7.88%; λ-CNNMLAA = 1.21% ± 5.74%; λ-CNNMLAA+NAC = 1.91% ± 4.78%; percent error for bone cancer lesions, λ-CNNNAC = 1.37% ± 5.16%; λ-CNNMLAA = 0.23% ± 3.81%; λ-CNNMLAA+NAC = 0.05% ± 3.49%). Conclusion: The use of CNNNAC was feasible for scatter estimation to address the chicken-egg dilemma in MLAA reconstruction, but CNNMLAA outperformed CNNNAC. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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