Despite significant advancements in automatic brain tumor segmentation methods, their performance is not guaranteed when certain MR sequences are missing. Addressing this issue, it is crucial to synthesize the missing MR images that reflect the unique characteristics of the absent modality with precise tumor representation. Typically, MRI synthesis methods generate partial images rather than full-sized volumes due to computational constraints. This limitation can lead to a lack of comprehensive 3D volumetric information and result in image artifacts during the merging process. In this paper, we propose a two-stage approach that first synthesizes MR images from 2D slices using a novel intensity encoding method and then refines the synthesized MRI. The proposed intensity encoding reduces artifacts when synthesizing MRI on a 2D slice basis. Then, the \textit{Refiner}, which leverages complete 3D volume information, further improves the quality of the synthesized images and enhances their applicability to segmentation methods. Experimental results demonstrate that the intensity encoding effectively minimizes artifacts in the synthesized MRI and improves perceptual quality. Furthermore, using the \textit{Refiner} on synthesized MRI significantly improves brain tumor segmentation results, highlighting the potential of our approach in practical applications.

翻译：尽管脑肿瘤自动分割方法已取得显著进展，但当某些磁共振序列缺失时，其性能无法得到保证。为解决这一问题，合成能够反映缺失模态独特特征且具有精确肿瘤表征的磁共振图像至关重要。通常，受计算资源限制，磁共振图像合成方法生成的是局部图像而非完整三维体数据。这一局限可能导致三维体数据信息不完整，并在图像融合过程中产生伪影。本文提出一种两阶段方法：首先采用新颖的强度编码方法从二维切片合成磁共振图像，随后对合成图像进行精炼处理。所提出的强度编码方法在基于二维切片的磁共振图像合成中有效减少了伪影。随后，利用完整三维体数据信息的\textit{Refiner}模块进一步提升了合成图像的质量，并增强了其在分割方法中的适用性。实验结果表明，强度编码方法能有效减少合成磁共振图像中的伪影并提升感知质量。此外，对合成磁共振图像使用\textit{Refiner}模块可显著改善脑肿瘤分割结果，这凸显了本方法在实际应用中的潜力。