发表机构
Heidelberg University（海德堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出S2T-Unet框架，通过向量量化分离模态不变结构信息与模态特定外观，实现跨模态MRI翻译，在IXI数据集上达到或超越最先进方法。
AI 中文摘要
跨模态MRI翻译旨在从已有的采集图像中合成缺失的MRI模态，从而在保留临床相关解剖信息的同时减少额外扫描的需求。然而，现有的图像翻译方法通常学习强度映射，而没有明确地将模态不变的结构信息与模态特定的外观信息分离开来，这可能导致结构信息丢失或图像细节不真实。在这项工作中，我们提出了S2T-Unet，一种结构到风格的框架，显式地建模这两个方面。具体来说，在较低层的瓶颈处引入向量量化，利用学习到的离散码本编码模态不变的结构信息。在较高层，模态转换模块利用解码器特征来调节并将编码器表示转换为目标模态，从而恢复模态特定的强度和对比度信息。在IXI多对比度MRI数据集上的四个翻译任务实验中，S2T-Unet与最先进的方法相比表现相当或更优。
英文摘要
Inter-modality MRI translation aims to synthesize missing MRI modalities from available acquisitions, reducing the need for additional scanning while preserving clinically relevant anatomical information. However, existing image translation methods often learn intensity mappings without explicitly separating modality-invariant structural information from modality-specific appearance, which may lead to structural information loss or unrealistic image details. In this work, we propose S2T-Unet, a structure-to-style framework that explicitly models these two aspects. Specifically, vector quantization is introduced at the lower-level bottleneck to encode modality-invariant structural information using a learned discrete codebook. At higher levels, a modality transformation module uses decoder features to condition and transform encoder representations toward the target modality, thereby recovering modality-specific intensity and contrast information. Experiments on the IXI multi-contrast MRI dataset across four translation tasks demonstrate that S2T-Unet is comparable or outperform with state-of-art method.
