Abstract
Accurate reconstruction of cardiac anatomy from sparse clinical images remains a major challenge in patient-specific modeling. While neural implicit functions have previously been applied to this task, their application to mapping anatomical consistency across subjects has been limited. In this work, we introduce Neural Implicit Heart Coordinates (NIHCs), a standardized implicit coordinate system, based on universal ventricular coordinates, that provides a common anatomical reference frame for the human heart. Our method predicts NIHCs directly from a limited number of 2D segmentations (sparse acquisition) and subsequently decodes them into dense 3D segmentations and high-resolution meshes at arbitrary output resolution. Trained on a large dataset of 5,000 cardiac meshes, the model achieves high reconstruction accuracy on clinical contours, with mean Euclidean surface errors of 2.51$\pm$0.33 mm in a diseased cohort (n=4549) and 2.3$\pm$0.36 mm in a healthy cohort (n=5576). The NIHC representation enables anatomically coherent reconstruction even under severe slice sparsity and segmentation noise, faithfully recovering complex structures such as the valve planes. Compared with traditional pipelines, inference time is reduced from over 60 s to 5-15 s. These results demonstrate that NIHCs constitute a robust and efficient anatomical representation for patient-specific 3D cardiac reconstruction from minimal input data.
Abstract (translated)
从稀疏临床图像准确重建心脏解剖结构仍然是个性化建模中的一个重大挑战。虽然神经隐式函数之前已被应用于此任务，但它们在跨个体映射解剖一致性的应用上受到限制。在这项工作中，我们引入了基于通用心室坐标的标准化隐式坐标系——神经隐式心脏坐标（NIHCs），它为人类心脏提供了一个共同的解剖参考框架。我们的方法直接从有限数量的2D分割图像（稀疏获取）预测NIHC，并随后将其解码为密集的3D分割和高分辨率网格，输出分辨率为任意大小。 在5000个心脏网格的大数据集上进行训练后，该模型在临床轮廓上的重建精度很高，在患病人群中的平均欧几里得表面误差为2.51±0.33毫米（n=4549），健康人群中为2.3±0.36毫米（n=5576）。NIHC表示使得即使在严重的切片稀疏和分割噪声下也能进行解剖上一致的重建，忠实恢复复杂结构如瓣膜平面。与传统流程相比，推理时间从超过60秒减少到5-15秒。 这些结果表明，NIHC为从最少输入数据实现患者特定的心脏3D重建提供了一种稳健且高效的解剖表示方法。
URL
https://arxiv.org/abs/2512.19316
