Cross-modality image-to-image translation from MR to synthetic 18F-FDOPA PET/MR fusion images using conditional GAN in brain cancer.
Objective: This study aims to identify the possibility of cross-modality image-to-image translation from magnetic resonance (MR) to synthetic positron emission tomography (PET)/MR fusion images using conditional generative adversarial networks (CGAN). Methods: Retrospective study was conducted invol...
| Publicado en: | Neuroradiology Vol. 67; no. 10; pp. 2727 - 2741 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2025
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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=189357970&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189357970 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Oct2025 vid: 67 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 189357970 186710880 189357970 189357970 10.1007/s00234-025-03704-z 189357970 ppf: 2727 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Cross-modality image-to-image translation from MR to synthetic 18F-FDOPA PET/MR fusion images using conditional GAN in brain cancer. aug: au: Seo, Youngbeom Yang, Heesung Kong, Eunjung Sanker, Vivek Desai, Atman Lee, Jungwon Park, So Hee Song, You Seon Jeon, Ikchan affil: https://ror.org/05yp5js06 Department of Neurosurgery, Korea University Ansan Hospital, Ansan, Republic of Korea sug: subj: Brain Neoplasms Radiography Magnetic Resonance Imaging Methods Positron Emission Tomography Computed Tomography Methods Image Processing, Computer Assisted Generative Adversarial Networks Utilization Funding Source Human Descriptive Statistics Retrospective Design Record Review Confidence Intervals Correlation Coefficient Quantitative Studies Neoplasm Recurrence, Local Pearson's Correlation Coefficient Spearman's Rank Correlation Coefficient Data Analysis Software ab: Objective: This study aims to identify the possibility of cross-modality image-to-image translation from magnetic resonance (MR) to synthetic positron emission tomography (PET)/MR fusion images using conditional generative adversarial networks (CGAN). Methods: Retrospective study was conducted involving 32 simultaneous 6-[18F]-fluoro-L-3,4-dihydroxyphenylalanine (18F-FDOPA) PET/MR imaging examinations from 27 patients diagnosed with brain cancer. We applied paired axial T1-weighted contrast MR (T1C) and PET/T1C fusion images to translate from T1C to synthetic PET/T1C fusion images using the Pix2Pix algorithm of CGAN. To access the image similarity between real and synthetic PET/T1C fusion images, we calculated correlation coefficients for the maximum/mean tumor-to-background ratio (TBRmax/mean) and quantitative analyses were performed using peak signal-to-noise ratio (PSNR), mean squared error (MSE), structural similarity index (SSIM), and feature similarity index measure (FSIM). Results: Total 2167 pairs of T1C and PET/T1C fusion images were obtained, which were randomly assigned to training and test datasets in 9:1 ratio (1950 and 217 pairs), and training data were further divided into training and validation datasets in 4:1 ratio (1560 and 390 pairs). The correlation coefficients were 0.706 (CI:0.533–0.822) for TBRmax (p < 0.001) and 0.901 (CI:0.831–0.943) for TBRmean (p < 0.001). The quantitative analyses were PSNR of 31.075 ± 3.976, MSE of 0.001 ± 0.001, SSIM of 0.868 ± 0.079, and FSIM of 0.922 ± 0.044, respectively. Conclusion: CGAN based on simultaneous 18F-FDOPA PET/MR imaging data demonstrated the potential for cross-modality image-to-image translation from T1C to PET/T1C fusion images, though limitations in small dataset and lack of external validation requiring further research. Highlights: This study tried image-to-image translation from MR to synthetic PET/MR fusion images in the patients with brain cancer using Pix2Pix of conditional generative adversarial networks (CGAN). Paired brain axial T1-weighted contrast MR (T1C) and PET/T1C fusion images of simultaneous 18F-FDOPA PET/MR imaging examination were used as dataset. CGAN based on simultaneous 18F-FDOPA PET/MR imaging data demonstrated the potential for cross-modality image-to-image translation from T1C to PET/T1C fusion images in brain cancer. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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