用神经网络统一建模多模态脑瘤MRI，自动补全缺失序列并预测可信度。
Implicit Neural Representations for Multimodal Longitudinal Image Imputation and Interpolation
- 将多模态时间序列MRI建为连续函数，支持时空插值
- 在儿童脑瘤数据上，T1CE的MS-SSIM达0.95±0.02，优于线性插值
- 自洽性评估可准确预测重建质量，适合临床异构数据
纵向多参数MRI在肿瘤随访中至关重要，但真实临床数据常存在序列缺失、采集协议不一和空间分辨率差异。我们提出一种患者特异性条件隐式神经表示（INR），将多模态纵向MRI建模为世界坐标、时间和模态条件的连续函数。模型通过随机模态丢弃训练以应对不完整数据，其连续坐标空间形式支持无需重采样到固定体素网格的时空插值。推理时基于跨模态重建性能构建自洽性置信度估计器。在儿童脑瘤患者纵向MRI上评估，对T1CE和FLAIR的插值效果显著优于线性插值（p < 0.05），T1CE的平均MS-SSIM为0.95 ± 0.02。预测置信度与真实重建质量高度相关（皮尔逊相关系数高达0.996），表明其在异构临床环境中的可靠部署潜力。
原文摘要 · Abstract (English)
Longitudinal multiparametric MRI is central to follow-up imaging in oncology, yet real-world clinical data are characterised by missing sequences, heterogeneous acquisition protocols, and varying spatial resolutions across time points. We propose a patient-specific conditional implicit neural representation (INR) that models multimodal longitudinal MRI as a continuous function of world coordinates, time, and modality conditioning. The model is trained with stochastic modality dropout to handle incomplete data, and its continuous coordinate-space formulation enables both spatial and temporal interpolation without resampling to a fixed voxel grid. A self-consistency-based confidence estimator is derived from cross-modal reconstruction performance at inference time. We evaluate the framework on longitudinal MRI from paediatric brain tumour patients, demonstrating statistically significant improvements over linear interpolation for T1CE and FLAIR (p < 0.05), with mean MS-SSIM of 0.95 $\pm$ 0.02 for T1CE. Predicted confidence correlates strongly with true reconstruction quality (Pearson r up to 0.996), suggesting reliable deployment potential in heterogeneous clinical settings.
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