Abstract
Magnetic resonance imaging (MRI) is a leading modality for the diagnosis of liver cancer, significantly improving the classification of the lesion and patient outcomes. However, traditional MRI faces challenges including risks from contrast agent (CA) administration, time-consuming manual assessment, and limited annotated datasets. To address these limitations, we propose a Time-Conditioned Autoregressive Contrast Enhancement (T-CACE) framework for synthesizing multi-phase contrast-enhanced MRI (CEMRI) directly from non-contrast MRI (NCMRI). T-CACE introduces three core innovations: a conditional token encoding (CTE) mechanism that unifies anatomical priors and temporal phase information into latent representations; and a dynamic time-aware attention mask (DTAM) that adaptively modulates inter-phase information flow using a Gaussian-decayed attention mechanism, ensuring smooth and physiologically plausible transitions across phases. Furthermore, a constraint for temporal classification consistency (TCC) aligns the lesion classification output with the evolution of the physiological signal, further enhancing diagnostic reliability. Extensive experiments on two independent liver MRI datasets demonstrate that T-CACE outperforms state-of-the-art methods in image synthesis, segmentation, and lesion classification. This framework offers a clinically relevant and efficient alternative to traditional contrast-enhanced imaging, improving safety, diagnostic efficiency, and reliability for the assessment of liver lesion. The implementation of T-CACE is publicly available at: this https URL.
Abstract (translated)
磁共振成像（MRI）是诊断肝癌的主要手段，能够显著提高病灶分类的准确性及患者的预后效果。然而，传统的MRI在使用对比剂（CA）时存在风险、手动评估耗时长以及标注数据集有限等问题。为了解决这些问题，我们提出了一种基于时间条件自回归对比增强（T-CACE）框架，该框架能够直接从非对比MRI（NCMRI）生成多相对比增强MRI（CEMRI）。T-CACE引入了三个核心创新：一种条件令牌编码（CTE）机制，将解剖先验和时间相位信息统一到潜在表示中；一个动态时序感知注意掩码（DTAM），通过高斯衰减注意力机制自适应地调节跨相之间信息流的流动，确保各阶段间过渡平稳且生理上合理。此外，一种关于时间分类一致性的约束（TCC）将病灶分类结果与生理信号变化保持一致，进一步增强了诊断可靠性。 在两个独立的肝脏MRI数据集上的广泛实验表明，T-CACE在图像合成、分割和病灶分类方面均优于现有的最先进的方法。该框架为传统对比增强成像提供了一种临床相关且高效的选择，提高了安全性、诊断效率及肝部病变评估的可靠性。 T-CACE框架的实现代码已公开发布于：[此链接](this https URL)。
URL
https://arxiv.org/abs/2508.09919
