CardiacFlow: 3D+t Four-Chamber Cardiac Shape Completion and Generation via Flow Matching
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916937574907904 |
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| author | Ma, Qiang Meng, Qingjie Qiao, Mengyun Matthews, Paul M. O'Regan, Declan P. Bai, Wenjia |
| author_facet | Ma, Qiang Meng, Qingjie Qiao, Mengyun Matthews, Paul M. O'Regan, Declan P. Bai, Wenjia |
| contents | Learning 3D+t shape completion and generation from multi-view cardiac magnetic resonance (CMR) images requires a large amount of high-resolution 3D whole-heart segmentations (WHS) to capture shape priors. In this work, we leverage flow matching techniques to learn deep generative flows for augmentation, completion, and generation of 3D+t shapes of four cardiac chambers represented implicitly by segmentations. Firstly, we introduce a latent rectified flow to generate 3D cardiac shapes for data augmentation, learnt from a limited number of 3D WHS data. Then, a label completion network is trained on both real and synthetic data to reconstruct 3D+t shapes from sparse multi-view CMR segmentations. Lastly, we propose CardiacFlow, a novel one-step generative flow model for efficient 3D+t four-chamber cardiac shape generation, conditioned on the periodic Gaussian kernel encoding of time frames. The experiments on the WHS datasets demonstrate that flow-based data augmentation reduces geometric errors by 16% in 3D shape completion. The evaluation on the UK Biobank dataset validates that CardiacFlow achieves superior generation quality and periodic consistency compared to existing baselines. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_05754 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CardiacFlow: 3D+t Four-Chamber Cardiac Shape Completion and Generation via Flow Matching Ma, Qiang Meng, Qingjie Qiao, Mengyun Matthews, Paul M. O'Regan, Declan P. Bai, Wenjia Image and Video Processing Learning 3D+t shape completion and generation from multi-view cardiac magnetic resonance (CMR) images requires a large amount of high-resolution 3D whole-heart segmentations (WHS) to capture shape priors. In this work, we leverage flow matching techniques to learn deep generative flows for augmentation, completion, and generation of 3D+t shapes of four cardiac chambers represented implicitly by segmentations. Firstly, we introduce a latent rectified flow to generate 3D cardiac shapes for data augmentation, learnt from a limited number of 3D WHS data. Then, a label completion network is trained on both real and synthetic data to reconstruct 3D+t shapes from sparse multi-view CMR segmentations. Lastly, we propose CardiacFlow, a novel one-step generative flow model for efficient 3D+t four-chamber cardiac shape generation, conditioned on the periodic Gaussian kernel encoding of time frames. The experiments on the WHS datasets demonstrate that flow-based data augmentation reduces geometric errors by 16% in 3D shape completion. The evaluation on the UK Biobank dataset validates that CardiacFlow achieves superior generation quality and periodic consistency compared to existing baselines. |
| title | CardiacFlow: 3D+t Four-Chamber Cardiac Shape Completion and Generation via Flow Matching |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2509.05754 |