Glioblastoma is a highly aggressive and lethal form of brain cancer. Magnetic resonance imaging (MRI) plays a significant role in the diagnosis, treatment planning, and follow-up of glioblastoma patients due to its non-invasive and radiation-free nature. The International Brain Tumor Segmentation (BraTS) challenge has contributed to generating numerous AI algorithms to accurately and efficiently segment glioblastoma sub-compartments using four structural (T1, T1Gd, T2, T2-FLAIR) MRI scans. However, these four MRI sequences may not always be available. To address this issue, Generative Adversarial Networks (GANs) can be used to synthesize the missing MRI sequences. In this paper, we implement and utilize an open-source GAN approach that takes any three MRI sequences as input to generate the missing fourth structural sequence. Our proposed approach is contributed to the community-driven generally nuanced deep learning framework (GaNDLF) and demonstrates promising results in synthesizing high-quality and realistic MRI sequences, enabling clinicians to improve their diagnostic capabilities and support the application of AI methods to brain tumor MRI quantification.

翻译：胶质母细胞瘤是一种高度侵袭性且致命性的脑癌。磁共振成像因其非侵入性和无辐射特性，在胶质母细胞瘤患者的诊断、治疗规划及随访中发挥着重要作用。国际脑肿瘤分割挑战赛通过利用四种结构MRI扫描序列（T1、T1Gd、T2、T2-FLAIR），已促成大量人工智能算法的开发，用于精确高效地分割胶质母细胞瘤子区域。然而，这四种MRI序列并非始终可用。针对这一问题，可采用生成对抗网络合成缺失的MRI序列。本文实现并利用一种开源GAN方法，以任意三种MRI序列为输入，生成缺失的第四种结构序列。我们提出的方法已贡献给社区驱动的通用精细化深度学习框架，并在合成高质量、逼真的MRI序列方面展现出可观成果，这将提升临床医生的诊断能力，并支持人工智能方法在脑肿瘤MRI量化中的应用。