Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC).
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 continuo...
| Publicado en: | European Radiology Vol. 29; no. 12; pp. 6867 - 6880 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=139479345&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139479345 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Dec2019 vid: 29 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139479345 139479345 NLM31227879 10.1007/s00330-019-06229-1 NLM31227879 139479345 ppf: 6867 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC). aug: au: Shiri, Isaac Ghafarian, Pardis Geramifar, Parham Leung, Kevin Ho-Yin Ghelichoghli, Mostafa Oveisi, Mehrdad Rahmim, Arman Ay, Mohammad Reza affil: Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran sug: subj: Brain Tomography, Emission-Computed Methods Brain Diseases Image Interpretation, Computer Assisted Methods Adult Male Aged Middle Age Reproducibility of Results Female Young Adult Neuroradiography Methods Child Adolescence Ferrans and Powers Quality of Life Index Scales Adult: 19-44 years Aged: 65+ years Middle Aged: 45-64 years Child: 6-12 years Adolescent: 13-18 years Male Female ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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