Multi-modal MRI brain image translation via available modalities holds significant practical importance in modern medicine, providing robust support for early diagnosis, treatment planning, and outcome assessment of diseases. For this purpose, it is important to ensure the fidelity of the tumor regions after translation. However, existing brain image translation methods ignore the structure information of different tumor regions, which could assist translation models in enhancing the quality and clinical applicability of the translated images. In this work, we propose a novel translation model called HTSCGAN, which is a unified multi-modal brain image translation generative adversarial model integrating the structural information within tumor regions with the aim of improving the quality of brain image translation. Specifically, the generator employs three Patch Contrast Module (PCM) with different patch sizes to capture the hierarchical structural information of the tumor regions. In addition, a pretrained Patch Classifier (PC) and a pretrained Structure-Aware Encoder (SAE) are employed to derive the generated image containing the same tumor region structure as the ground truth image via patch classification loss and tumor perceptual loss, respectively. The experiments on BraTS2020 and BraTS2021 demonstrate strong performance of our model in both translation tasks and down stream segmentation tasks, highlighting its effectiveness in enhancing the quality and clinical relevance of the translated brain images. Our code is available at https://anonymous.4open.science/r/HTSCGAN.

翻译：多模态MRI脑图像翻译利用已有模态生成缺失模态，在现代医学中具有重要的实际意义，为疾病的早期诊断、治疗规划和预后评估提供了有力支持。为此，确保翻译后肿瘤区域的保真度至关重要。然而，现有脑图像翻译方法忽略了不同肿瘤区域的结构信息，而这些信息有助于提升翻译模型的图像质量和临床适用性。本文提出一种新颖的翻译模型HTSCGAN，这是一种融合肿瘤区域结构信息的统一多模态脑图像翻译生成对抗网络，旨在提升脑图像翻译质量。具体而言，生成器采用三个不同补丁大小的补丁对比模块（PCM）来捕获肿瘤区域的层级结构信息。此外，利用预训练的补丁分类器（PC）和预训练的结构感知编码器（SAE），分别通过补丁分类损失和肿瘤感知损失，使生成的图像包含与真实图像相同的肿瘤区域结构。在BraTS2020和BraTS2021上的实验表明，我们的模型在翻译任务和下游分割任务中均表现出色，凸显了其在提升翻译脑图像质量和临床相关性方面的有效性。我们的代码可在https://anonymous.4open.science/r/HTSCGAN获取。