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和致命性的脑癌。磁共振成像（MRI）因其无创、无辐射的特性，在胶质母细胞瘤患者的诊断、治疗规划和随访中发挥着重要作用。国际脑肿瘤分割（BraTS）挑战赛推动了许多人工智能算法的开发，这些算法利用四种结构性（T1、T1Gd、T2、T2-FLAIR）MRI扫描准确高效地分割胶质母细胞瘤亚区。然而，这四种MRI序列并非总是可用的。为解决这一问题，生成对抗网络（GANs）可用于合成缺失的MRI序列。本文实施并利用一种开源GAN方法，该方法以任意三种MRI序列为输入，生成缺失的第四种结构性序列。我们提出的方法已贡献给社区驱动的通用细微深度学习框架（GaNDLF），在合成高质量且逼真的MRI序列方面展现出令人满意的效果，从而帮助临床医生提高诊断能力，并支持人工智能方法在脑肿瘤MRI量化中的应用。