所有三个造影增强 (CE) 时相（例如动脉期、门静脉期和延迟期）对于诊断肝脏肿瘤至关重要。然而，由于造影剂 (CA) 风险、成像时间长和严格的成像标准，获取所有三个阶段受到限制。在本文中，我们提出了一种新颖的通用唯一分解驱动扩散模型（CUDD-DM），能够将三个阶段中的任意两个输入阶段转换为剩余一个阶段，从而减少患者等待时间，节省医疗资源，并减少使用CA 的数量。 1）公共-独特特征分解模块，通过利用谱分解来捕获不同输入之间的共同特征和独特特征，不仅学习两个输入相位之间高度相似区域的相关性，而且还学习不同区域的差异，从而为剩余相的合成。 2）多尺度时间重置门模块，通过双向比较当前切片和多个历史切片中的病变，在没有病变时最大化对先前切片的依赖，并在存在病变时最小化这种依赖，从而防止连续切片之间的干扰。 3）扩散模型驱动的病变细节合成模块，通过采用连续渐进的生成过程，准确捕获数据分布之间的细节特征，从而避免传统方法（例如GAN）过度关注全局分布而导致的细节丢失。对广义 CE 肝脏肿瘤数据集的大量实验表明，我们的 CUDD-DM 实现了最先进的性能（与七种领先方法相比，SSIM 提高了至少 2.2%（病变面积 5.3%））。这些结果表明，CUDD-DM 推进了 CE 肝脏肿瘤成像技术。

All three contrast-enhanced (CE) phases (e.g., Arterial, Portal Venous, and Delay) are crucial for diagnosing liver tumors. However, acquiring all three phases is constrained due to contrast agents (CAs) risks, long imaging time, and strict imaging criteria. In this paper, we propose a novel Common-Unique Decomposition Driven Diffusion Model (CUDD-DM), capable of converting any two input phases in three phases into the remaining one, thereby reducing patient wait time, conserving medical resources, and reducing the use of CAs. 1) The Common-Unique Feature Decomposition Module, by utilizing spectral decomposition to capture both common and unique features among different inputs, not only learns correlations in highly similar areas between two input phases but also learns differences in different areas, thereby laying a foundation for the synthesis of remaining phase. 2) The Multi-scale Temporal Reset Gates Module, by bidirectional comparing lesions in current and multiple historical slices, maximizes reliance on previous slices when no lesions and minimizes this reliance when lesions are present, thereby preventing interference between consecutive slices. 3) The Diffusion Model-Driven Lesion Detail Synthesis Module, by employing a continuous and progressive generation process, accurately captures detailed features between data distributions, thereby avoiding the loss of detail caused by traditional methods (e.g., GAN) that overfocus on global distributions. Extensive experiments on a generalized CE liver tumor dataset have demonstrated that our CUDD-DM achieves state-of-the-art performance (improved the SSIM by at least 2.2% (lesions area 5.3%) comparing the seven leading methods). These results demonstrate that CUDD-DM advances CE liver tumor imaging technology.