Clinical MRI contrast acquisition suffers from inefficient information yield, which presents as a mismatch between the risky and costly acquisition protocol and the fixed and sparse acquisition sequence. Applying world models to simulate the contrast enhancement kinetics in the human body enables continuous contrast-free dynamics. However, the low temporal resolution in MRI acquisition restricts the training of world models, leading to a sparsely sampled dataset. Directly training a generative model to capture the kinetics leads to two limitations: (a) Due to the absence of data on missing time, the model tends to overfit to irrelevant features, leading to content distortion. (b) Due to the lack of continuous temporal supervision, the model fails to learn the continuous kinetics law over time, causing temporal discontinuities. For the first time, we propose MRI Contrast Enhancement Kinetics World model (MRI CEKWorld) with SpatioTemporal Consistency Learning (STCL). For (a), guided by the spatial law that patient-level structures remain consistent during enhancement, we propose Latent Alignment Learning (LAL) that constructs a patient-specific template to constrain contents to align with this template. For (b), guided by the temporal law that the kinetics follow a consistent smooth trend, we propose Latent Difference Learning (LDL) which extends the unobserved intervals by interpolation and constrains smooth variations in the latent space among interpolated sequences. Extensive experiments on two datasets show our MRI CEKWorld achieves better realistic contents and kinetics. Codes will be available at https://github.com/DD0922/MRI-Contrast-Enhancement-Kinetics-World-Model.

翻译：临床磁共振成像对比剂采集存在信息产出效率低下的问题，表现为高风险、高成本的采集协议与固定且稀疏的采集序列之间的不匹配。应用世界模型来模拟人体内对比增强动力学，能够实现连续的无对比剂动态成像。然而，磁共振成像采集的低时间分辨率限制了世界模型的训练，导致数据集采样稀疏。直接训练生成模型以捕捉动力学存在两个局限性：(a) 由于缺失时间点数据的缺乏，模型容易过度拟合无关特征，导致内容失真。(b) 由于缺乏连续的时间监督，模型无法学习随时间变化的连续动力学规律，造成时间不连续性。我们首次提出了结合时空一致性学习的磁共振成像对比增强动力学世界模型。针对(a)，在患者层面结构在增强过程中保持一致的时空规律指导下，我们提出了潜在对齐学习，通过构建患者特异性模板来约束内容与该模板对齐。针对(b)，在动力学遵循一致平滑趋势的时间规律指导下，我们提出了潜在差分学习，通过插值扩展未观测区间，并约束插值序列间潜在空间中的平滑变化。在两个数据集上的大量实验表明，我们的磁共振成像对比增强动力学世界模型实现了更逼真的内容与动力学。代码将在 https://github.com/DD0922/MRI-Contrast-Enhancement-Kinetics-World-Model 公开。