用流匹配技术生成心脏四腔3D+t形状，提升医学影像重建精度。
CardiacFlow: 3D+t Four-Chamber Cardiac Shape Completion and Generation via Flow Matching
- 基于流匹配学习生成式流，实现有限数据下的心脏形状增广。
- 3D形状补全几何误差降低16%，在英国生物银行数据集上表现更优。
- 适合心血管影像分析、生成模型研究者使用。
从多视角心脏磁共振（CMR）图像中学习3D+t形状补全与生成，需要大量高分辨率全心分割数据以捕捉形状先验。本文利用流匹配技术，学习深层生成流，用于隐式表示的四腔心脏3D+t形状的增广、补全与生成。首先，引入潜在修正流，从少量3D全心分割数据中生成3D心脏形状以实现数据增广；其次，在真实与合成数据上训练标签补全网络，从稀疏多视角CMR分割重建3D+t形状；最后，提出CardiacFlow，一种新型单步生成流模型，通过周期性高斯核编码时间帧，实现高效3D+t四腔心脏形状生成。在WHS数据集上的实验表明，基于流的数据增广使3D形状补全几何误差降低16%。在英国生物银行数据集上的评估验证了CardiacFlow在生成质量与周期一致性方面优于现有基线方法。
原文摘要 · Abstract (English)
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.
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