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Journal Abstract Search

568 related items for PubMed ID: 32687608

  • 1. Full-count PET recovery from low-count image using a dilated convolutional neural network.
    Spuhler K, Serrano-Sosa M, Cattell R, DeLorenzo C, Huang C.
    Med Phys; 2020 Oct; 47(10):4928-4938. PubMed ID: 32687608
    [Abstract] [Full Text] [Related]

  • 2. Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment.
    Dutta K, Laforest R, Luo J, Jha AK, Shoghi KI.
    Med Phys; 2024 Jun; 51(6):4324-4339. PubMed ID: 38710222
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  • 3. Image reconstruction using UNET-transformer network for fast and low-dose PET scans.
    Kaviani S, Sanaat A, Mokri M, Cohalan C, Carrier JF.
    Comput Med Imaging Graph; 2023 Dec; 110():102315. PubMed ID: 38006648
    [Abstract] [Full Text] [Related]

  • 4. Spatial adaptive and transformer fusion network (STFNet) for low-count PET blind denoising with MRI.
    Zhang L, Xiao Z, Zhou C, Yuan J, He Q, Yang Y, Liu X, Liang D, Zheng H, Fan W, Zhang X, Hu Z.
    Med Phys; 2022 Jan; 49(1):343-356. PubMed ID: 34796526
    [Abstract] [Full Text] [Related]

  • 5. Attention-based deep neural network for partial volume correction in brain 18F-FDG PET imaging.
    Azimi M, Kamali-Asl A, Ay MR, Zeraatkar N, Hosseini MS, Sanaat A, Arabi H.
    Phys Med; 2024 Mar; 119():103315. PubMed ID: 38377837
    [Abstract] [Full Text] [Related]

  • 6. Utilizing deep learning techniques to improve image quality and noise reduction in preclinical low-dose PET images in the sinogram domain.
    Manoj Doss KK, Chen JC.
    Med Phys; 2024 Jan; 51(1):209-223. PubMed ID: 37966121
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  • 7. Virtual high-count PET image generation using a deep learning method.
    Liu J, Ren S, Wang R, Mirian N, Tsai YJ, Kulon M, Pucar D, Chen MK, Liu C.
    Med Phys; 2022 Sep; 49(9):5830-5840. PubMed ID: 35880541
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  • 8. Deep generative denoising networks enhance quality and accuracy of gated cardiac PET data.
    Jafaritadi M, Teuho J, Lehtonen E, Klén R, Saraste A, Levin CS.
    Ann Nucl Med; 2024 Oct; 38(10):775-788. PubMed ID: 38842629
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  • 9. Generation of18F-FDG PET standard scan images from short scans using cycle-consistent generative adversarial network.
    Ghafari A, Sheikhzadeh P, Seyyedi N, Abbasi M, Farzenefar S, Yousefirizi F, Ay MR, Rahmim A.
    Phys Med Biol; 2022 Oct 19; 67(21):. PubMed ID: 36162408
    [Abstract] [Full Text] [Related]

  • 10. Supervised learning with cyclegan for low-dose FDG PET image denoising.
    Zhou L, Schaefferkoetter JD, Tham IWK, Huang G, Yan J.
    Med Image Anal; 2020 Oct 19; 65():101770. PubMed ID: 32674043
    [Abstract] [Full Text] [Related]

  • 11. Generation of Conventional 18F-FDG PET Images from 18F-Florbetaben PET Images Using Generative Adversarial Network: A Preliminary Study Using ADNI Dataset.
    Choi HJ, Seo M, Kim A, Park SH.
    Medicina (Kaunas); 2023 Jul 10; 59(7):. PubMed ID: 37512092
    [Abstract] [Full Text] [Related]

  • 12. Anatomical-guided attention enhances unsupervised PET image denoising performance.
    Onishi Y, Hashimoto F, Ote K, Ohba H, Ota R, Yoshikawa E, Ouchi Y.
    Med Image Anal; 2021 Dec 10; 74():102226. PubMed ID: 34563861
    [Abstract] [Full Text] [Related]

  • 13. Deep-Learning Generation of Synthetic Intermediate Projections Improves 177Lu SPECT Images Reconstructed with Sparsely Acquired Projections.
    Rydén T, Van Essen M, Marin I, Svensson J, Bernhardt P.
    J Nucl Med; 2021 Apr 10; 62(4):528-535. PubMed ID: 32859710
    [Abstract] [Full Text] [Related]

  • 14. Generation of synthetic PET/MR fusion images from MR images using a combination of generative adversarial networks and conditional denoising diffusion probabilistic models based on simultaneous 18F-FDG PET/MR image data of pyogenic spondylodiscitis.
    Jung E, Kong E, Yu D, Yang H, Chicontwe P, Park SH, Jeon I.
    Spine J; 2024 Aug 10; 24(8):1467-1477. PubMed ID: 38615932
    [Abstract] [Full Text] [Related]

  • 15. Generation of PET Attenuation Map for Whole-Body Time-of-Flight 18F-FDG PET/MRI Using a Deep Neural Network Trained with Simultaneously Reconstructed Activity and Attenuation Maps.
    Hwang D, Kang SK, Kim KY, Seo S, Paeng JC, Lee DS, Lee JS.
    J Nucl Med; 2019 Aug 10; 60(8):1183-1189. PubMed ID: 30683763
    [Abstract] [Full Text] [Related]

  • 16. Intercomparison of MR-informed PET image reconstruction methods.
    Bland J, Mehranian A, Belzunce MA, Ellis S, da Costa-Luis C, McGinnity CJ, Hammers A, Reader AJ.
    Med Phys; 2019 Nov 10; 46(11):5055-5074. PubMed ID: 31494961
    [Abstract] [Full Text] [Related]

  • 17. A Lightweight Low-dose PET Image Super-resolution Reconstruction Method based on Convolutional Neural Network.
    Liu K, Yu H, Zhang M, Zhao L, Wang X, Liu S, Li H, Yang K.
    Curr Med Imaging; 2023 Nov 10; 19(12):1427-1435. PubMed ID: 36757033
    [Abstract] [Full Text] [Related]

  • 18. GapFill-Recon Net: A Cascade Network for simultaneously PET Gap Filling and Image Reconstruction.
    Huang Y, Zhu H, Duan X, Hong X, Sun H, Lv W, Lu L, Feng Q.
    Comput Methods Programs Biomed; 2021 Sep 10; 208():106271. PubMed ID: 34274612
    [Abstract] [Full Text] [Related]

  • 19. Noise reduction with cross-tracer and cross-protocol deep transfer learning for low-dose PET.
    Liu H, Wu J, Lu W, Onofrey JA, Liu YH, Liu C.
    Phys Med Biol; 2020 Sep 14; 65(18):185006. PubMed ID: 32924973
    [Abstract] [Full Text] [Related]

  • 20. Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model.
    Pan S, Abouei E, Peng J, Qian J, Wynne JF, Wang T, Chang CW, Roper J, Nye JA, Mao H, Yang X.
    Med Phys; 2024 Aug 14; 51(8):5468-5478. PubMed ID: 38588512
    [Abstract] [Full Text] [Related]

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