Synthesizing medical images while preserving their structural information is crucial in medical research. In such scenarios, the preservation of anatomical content becomes especially important. Although recent advances have been made by incorporating instance-level information to guide translation, these methods overlook the spatial coherence of structural-level representation and the anatomical invariance of content during translation. To address these issues, we introduce hierarchical granularity discrimination, which exploits various levels of semantic information present in medical images. Our strategy utilizes three levels of discrimination granularity: pixel-level discrimination using a Brain Memory Bank, structure-level discrimination on each brain structure with a re-weighting strategy to focus on hard samples, and global-level discrimination to ensure anatomical consistency during translation. The image translation performance of our strategy has been evaluated on three independent datasets (UK Biobank, IXI, and BraTS 2018), and it has outperformed state-of-the-art algorithms. Particularly, our model excels not only in synthesizing normal structures but also in handling abnormal (pathological) structures, such as brain tumors, despite the variations in contrast observed across different imaging modalities due to their pathological characteristics. The diagnostic value of synthesized MR images containing brain tumors has been evaluated by radiologists. This indicates that our model may offer an alternative solution in scenarios where specific MR modalities of patients are unavailable. Extensive experiments further demonstrate the versatility of our method, providing unique insights into medical image translation.

翻译：在医学研究中，合成保留结构信息的医学图像至关重要。在此类场景中，解剖内容的保持尤为重要。尽管近期通过融入实例级信息指导翻译取得进展，但这些方法忽视了翻译过程中结构级表征的空间连贯性以及内容的解剖不变性。为解决这些问题，我们引入层次粒度判别方法，该方法充分利用医学图像中不同层级的语义信息。我们的策略采用三种判别粒度：基于脑记忆库的像素级判别、针对各脑结构采用重加权策略聚焦困难样本的结构级判别，以及确保翻译过程中解剖一致性的全局级判别。该策略的图像翻译性能已在三个独立数据集（UK Biobank、IXI和BraTS 2018）上评估，并超越现有最优算法。值得注意的是，尽管不同成像模态因病理特征存在对比度差异，我们的模型不仅在合成正常结构方面表现优异，还能处理脑肿瘤等异常（病理）结构。放射科医生已对含脑肿瘤的合成MR图像的诊断价值进行评价。这表明，在患者特定MR模态不可用的场景中，我们的模型可能提供替代方案。大量实验进一步验证了该方法的普适性，为医学图像翻译提供了独特见解。