MRI对比增强动力学世界模型
MRI Contrast Enhancement Kinetics World Model
提出时空一致性学习框架，通过潜空间对齐和潜空间差分学习解决稀疏时序采样下的连续动力学建模问题，可迁移到其他需要从稀疏观测重建连续时序过程的视觉任务。
原文依据 we propose MRI Contrast Enhancement Kinetics World model (MRI CEKWorld) with SpatioTemporal Consistency Learning (STCL)
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论临床MRI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稀疏的采集序列之间的不匹配。应用世界模型模拟人体内对比增强动力学，可实现连续的无对比剂动态成像。然而，MRI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规律会带来两个局限：(a) 由于缺失时间点数据的缺乏，模型容易对无关特征过拟合，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，造成时序不连续。我们首次提出MRI对比增强动力学世界模型（MRI CEKWorld）与时空一致性学习（STCL）。针对(a)，在患者级结构在增强过程中保持一致的空间规律指导下，我们提出潜空间对齐学习（LAL），构建患者特异性模板以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，我们提出潜空间差分学习（LDL），通过插值扩展未观测区间，并约束插值序列在潜空间中的平滑变化。在两个数据集上的大量实验表明，我们的MRI CEKWorld实现了更逼真的内容和动力学。代码将公开。
英文原文摘要 1611 字符
Clinical MRI contrast acquisition suffers from inefficient information yield, which presents as a mismatch between the risky and costly acquisition protocol and fixed and sparse acquisition sequence. Applying world models to simulate the contrast enhancement kinetics in human body enables continuous contrast-free dynamics. However, the low temporal resolution in MRI acquisition restricts the training of world models, leading to the sparsely sampled dataset. Directly training a generative model to capture the kinetics leads to two limitations: (a) Due to the absence 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 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 follows a consistent smooth trend, we propose Latent Difference Learning (LDL) which extends the unobserved intervals by interpolation and constrain smooth variations in the latent space among interpolated sequence. Extensive experiments on two datasets show our MRI CEKWorld achieves better realistic contents and kinetics. Codes will be available.