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Dual-Domain Iterative Network with Adaptive Data Consistency for Joint Denoising and Few-Angle Reconstruction of Low-Dose Cardiac SPECT.

, , , , , , и . MILLanD@MICCAI, том 14307 из Lecture Notes in Computer Science, стр. 49-59. Springer, (2023)

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MCP-Net: Inter-frame Motion Correction with Patlak Regularization for Whole-body Dynamic PET., , , , и . MICCAI (4), том 13434 из Lecture Notes in Computer Science, стр. 163-172. Springer, (2022)Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CT., , , , , , , и . MICCAI (6), том 13436 из Lecture Notes in Computer Science, стр. 46-55. Springer, (2022)Segmentation-Free PVC for Cardiac SPECT Using a Densely-Connected Multi-Dimensional Dynamic Network., , , , , , , , , и 3 other автор(ы). IEEE Trans. Medical Imaging, 42 (5): 1325-1336 (мая 2023)TAI-GAN: Temporally and Anatomically Informed GAN for Early-to-Late Frame Conversion in Dynamic Cardiac PET Motion Correction., , , , , , , , , и 3 other автор(ы). SASHIMI@MICCAI, том 14288 из Lecture Notes in Computer Science, стр. 64-74. Springer, (2023)DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography., , , , и . Medical Image Anal., (2022)DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT., , , , , , , , , и . Medical Image Anal., (августа 2023)MCP-Net: Introducing Patlak Loss Optimization to Whole-Body Dynamic PET Inter-Frame Motion Correction., , , , , и . IEEE Trans. Medical Imaging, 42 (12): 3512-3523 (декабря 2023)Transformer-Based Dual-Domain Network for Few-View Dedicated Cardiac SPECT Image Reconstructions., , , , , , , , и . MICCAI (10), том 14229 из Lecture Notes in Computer Science, стр. 163-172. Springer, (2023)Cross-Domain Iterative Network for Simultaneous Denoising, Limited-Angle Reconstruction, and Attenuation Correction of Cardiac SPECT., , , , , , и . MLMI@MICCAI (1), том 14348 из Lecture Notes in Computer Science, стр. 12-22. Springer, (2023)FedFTN: Personalized federated learning with deep feature transformation network for multi-institutional low-count PET denoising., , , , , , , , , и 3 other автор(ы). Medical Image Anal., (декабря 2023)