Mapping in Cycles: Dual-Domain PET-CT Synthesis Framework with Cycle-Consistent Constraints

Zhang, Jiadong; Cui, Zhiming; Jiang, Caiwen; Zhang, Jingyang; Gao, Fei; Shen, Dinggang

doi:10.1007/978-3-031-16446-0_72

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2023

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Cited by 3 publications

References 19 publications

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Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation

Phan,

Liao,

Verjans

et al. 2023

Lecture Notes in Computer Science

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Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation

Phan,

Liao,

Verjans

et al. 2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Reconstruction of 3D Fetal Brain MRI from 2D Cross-Sectional Acquisitions Using Unsupervised Learning Network

Yang,

Gu,

Zhang

et al. 2023

Machine Learning in Medical Imaging

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A generalized dual-domain generative framework with hierarchical consistency for medical image reconstruction and synthesis

Zhang,

Sun,

Yang

et al. 2023

Commun Eng

Self Cite

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Medical image reconstruction and synthesis are critical for imaging quality, disease diagnosis and treatment. Most of the existing generative models ignore the fact that medical imaging usually occurs in the acquisition domain, which is different from, but associated with, the image domain. Such methods exploit either single-domain or dual-domain information and suffer from inefficient information coupling across domains. Moreover, these models are usually designed specifically and not general enough for different tasks. Here we present a generalized dual-domain generative framework to facilitate the connections within and across domains by elaborately-designed hierarchical consistency constraints. A multi-stage learning strategy is proposed to construct hierarchical constraints effectively and stably. We conducted experiments for representative generative tasks including low-dose PET/CT reconstruction, CT metal artifact reduction, fast MRI reconstruction, and PET/CT synthesis. All these tasks share the same framework and achieve better performance, which validates the effectiveness of our framework. This technology is expected to be applied in clinical imaging to increase diagnosis efficiency and accuracy.

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Mapping in Cycles: Dual-Domain PET-CT Synthesis Framework with Cycle-Consistent Constraints

Cited by 3 publications

References 19 publications

Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation

Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation

Reconstruction of 3D Fetal Brain MRI from 2D Cross-Sectional Acquisitions Using Unsupervised Learning Network

A generalized dual-domain generative framework with hierarchical consistency for medical image reconstruction and synthesis

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