用CT数据训练神经隐式模型，从低分辨率MRI重建高精度心脏形状。
Fighting MRI Anisotropy: Learning Multiple Cardiac Shapes From a Single Implicit Neural Representation
- 用高分辨率CT数据训练单一隐式神经函数，联合建模多分辨率心脏形态。
- 重建右心室和心肌的4腔切片，Dice达0.91/0.75，Hausdorff距离为6.21/7.53mm。
- 适合需要精准心脏形变分析的研究者，尤其在缺乏高质量MRI标注时。
短轴心血管磁共振成像（CMRI）的各向异性限制了心脏形状分析。为解决此问题，我们利用高分辨率、近各向同性的冠状动脉计算机断层扫描血管造影（CTA）数据，训练单一神经隐式函数，以联合表示任意分辨率下的CMRI心脏形态。我们在右心室（RV）和心肌（MYO）重建上进行评估，其中心肌同时建模左心室的内膜与外膜表面。由于缺乏高分辨率短轴参考分割，我们通过从重建形状中提取4腔切片（4CH）来评估性能。与来自CMRI的参考4CH分割掩码相比，该方法在右心室和心肌上的Dice相似系数分别为0.91±0.07和0.75±0.13，豪斯多夫距离分别为6.21±3.97 mm和7.53±5.13 mm。定量与定性评估表明，该模型能重建出准确、平滑且解剖上合理的形状，支持心脏形状分析的改进。
原文摘要 · Abstract (English)
The anisotropic nature of short-axis (SAX) cardiovascular magnetic resonance imaging (CMRI) limits cardiac shape analysis. To address this, we propose to leverage near-isotropic, higher resolution computed tomography angiography (CTA) data of the heart. We use this data to train a single neural implicit function to jointly represent cardiac shapes from CMRI at any resolution. We evaluate the method for the reconstruction of right ventricle (RV) and myocardium (MYO), where MYO simultaneously models endocardial and epicardial left-ventricle surfaces. Since high-resolution SAX reference segmentations are unavailable, we evaluate performance by extracting a 4-chamber (4CH) slice of RV and MYO from their reconstructed shapes. When compared with the reference 4CH segmentation masks from CMRI, our method achieved a Dice similarity coefficient of 0.91 $\pm$ 0.07 and 0.75 $\pm$ 0.13, and a Hausdorff distance of 6.21 $\pm$ 3.97 mm and 7.53 $\pm$ 5.13 mm for RV and MYO, respectively. Quantitative and qualitative assessment demonstrate the model's ability to reconstruct accurate, smooth and anatomically plausible shapes, supporting improvements in cardiac shape analysis.
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