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Glioblastoma is a highly aggressive and lethal form of brain cancer. Magneticresonance imaging (MRI) plays a significant role in the diagnosis, treatmentplanning, and follow-up of glioblastoma patients due to its non-invasive andradiation-free nature. The International Brain Tumor Segmentation (BraTS)challenge has contributed to generating numerous AI algorithms to accuratelyand efficiently segment glioblastoma sub-compartments using four structural(T1, T1Gd, T2, T2-FLAIR) MRI scans. However, these four MRI sequences may notalways be available. To address this issue, Generative Adversarial Networks(GANs) can be used to synthesize the missing MRI sequences. In this paper, weimplement and utilize an open-source GAN approach that takes any three MRIsequences as input to generate the missing fourth structural sequence. Ourproposed approach is contributed to the community-driven generally nuanced deeplearning framework (GaNDLF) and demonstrates promising results in synthesizinghigh-quality and realistic MRI sequences, enabling clinicians to improve theirdiagnostic capabilities and support the application of AI methods to braintumor MRI quantification.

Synthesizing Missing MRI Sequences from Available Modalities using Generative Adversarial Networks in BraTS Dataset