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Dual-cycle Constrained Bijective VAE-GAN For Tagged-to-Cine Magnetic Resonance Image Synthesis

arXiv 2101.05439 ↗First posted 14 Jan 2021eess.IV cs.CVcited by 12

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Abstract (arXiv)

Tagged magnetic resonance imaging (MRI) is a widely used imaging technique for measuring tissue deformation in moving organs. Due to tagged MRI's intrinsic low anatomical resolution, another matching set of cine MRI with higher resolution is sometimes acquired in the same scanning session to facilitate tissue segmentation, thus adding extra time and cost. To mitigate this, in this work, we propose a novel dual-cycle constrained bijective VAE-GAN approach to carry out tagged-to-cine MR image synthesis. Our method is based on a variational autoencoder backbone with cycle reconstruction constrained adversarial training to yield accurate and realistic cine MR images given tagged MR images. Our framework has been trained, validated, and tested using 1,768, 416, and 1,560 subject-independent paired slices of tagged and cine MRI from twenty healthy subjects, respectively, demonstrating superior performance over the comparison methods. Our method can potentially be used to reduce the extra acquisition time and cost, while maintaining the same workflow for further motion analyses.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 8 of its 19 references that are in the map. First posted on arXiv 14 Jan 2021.

Builds on

8 of 19 references.

References · 8

  1. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  2. 2016Improving Variational Inference with Inverse Autoregressive Flowcited by 636
  3. 2017Unsupervised Image-to-Image Translation Networkscited by 602
  4. 2015Autoencoding beyond pixels using a learned similarity metriccited by 407
  5. 2016NIPS 2016 Tutorial: Generative Adversarial Networkscited by 284
  6. 2018MedGAN: Medical Image Translation using GANscited by 152
  7. 2017Contextual-based Image Inpainting: Infer, Match, and Translatecited by 50
  8. 2020AUTO3D: Novel view synthesis through unsupervisely learned variational viewpoint and global 3D representationcited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 5; 2023: 2; 2024: 0; 2025: 1; 2026: 1.

12 papers.

  1. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
  2. 2021Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentationcited by 39
  3. 2021Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesiscited by 19
  4. 2021A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translationcited by 15
  5. 2022Self-semantic contour adaptation for cross modality brain tumor segmentationcited by 9
  6. 2022Unsupervised Domain Adaptation for Segmentation with Black-box Source Modelcited by 7
  7. 2022Structure-aware Unsupervised Tagged-to-Cine MRI Synthesis with Self Disentanglementcited by 2
  8. 2023Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translationcited by 2
  9. 2025Diffusion Bridge Models for 3D Medical Image Translationcited by 2
  10. 2022Tagged-MRI Sequence to Audio Synthesis via Self Residual Attention Guided Heterogeneous Translatorcited by 1
  11. 2023Synthesizing audio from tongue motion during speech using tagged MRI via transformercited by 1
  12. 2026SRUG: A Fusion-Driven Generator Network for Medical Image Translationcited by 1