Lu Xiangjiang, Liang Xiaoshuang, Liu Wenjing, Miao Xiuxia, Guan Xianglong
Guangxi Key Lab of Multi-Source Information Mining & Security, School of Computer Science and Engineering & School of Software, Guangxi Normal University, Guilin, 541004, China.
Med Biol Eng Comput. 2024 Jun;62(6):1851-1868. doi: 10.1007/s11517-024-03035-w. Epub 2024 Feb 24.
As a crucial medical examination technique, different modalities of magnetic resonance imaging (MRI) complement each other, offering multi-angle and multi-dimensional insights into the body's internal information. Therefore, research on MRI cross-modality conversion is of great significance, and many innovative techniques have been explored. However, most methods are trained on well-aligned data, and the impact of misaligned data has not received sufficient attention. Additionally, many methods focus on transforming the entire image and ignore crucial edge information. To address these challenges, we propose a generative adversarial network based on multi-feature fusion, which effectively preserves edge information while training on noisy data. Notably, we consider images with limited range random transformations as noisy labels and use an additional small auxiliary registration network to help the generator adapt to the noise distribution. Moreover, we inject auxiliary edge information to improve the quality of synthesized target modality images. Our goal is to find the best solution for cross-modality conversion. Comprehensive experiments and ablation studies demonstrate the effectiveness of the proposed method.
作为一种关键的医学检查技术，不同模态的磁共振成像（MRI）相互补充，能够从多角度、多维度洞察人体内部信息。因此，MRI跨模态转换研究具有重要意义，并且已经探索了许多创新技术。然而，大多数方法是在对齐良好的数据上进行训练的，而未对齐数据的影响尚未得到充分关注。此外，许多方法专注于变换整个图像，而忽略了关键的边缘信息。为应对这些挑战，我们提出了一种基于多特征融合的生成对抗网络，该网络在对噪声数据进行训练时能有效保留边缘信息。值得注意的是，我们将具有有限范围随机变换的图像视为噪声标签，并使用一个额外的小型辅助配准网络来帮助生成器适应噪声分布。此外，我们注入辅助边缘信息以提高合成目标模态图像的质量。我们的目标是找到跨模态转换的最佳解决方案。综合实验和消融研究证明了所提方法的有效性。
