Synthesizing Missing MRI Sequences from Available Modalities using Generative Adversarial Networks in BraTS Dataset
This addresses a practical issue for clinicians and AI applications in brain tumor MRI quantification by enabling diagnostic improvements when not all MRI sequences are available, though it is incremental as it applies an existing GAN method to a specific medical imaging context.
The paper tackles the problem of missing MRI sequences in glioblastoma diagnosis by using Generative Adversarial Networks (GANs) to synthesize the missing fourth structural sequence from any three available ones in the BraTS dataset, demonstrating promising results in generating high-quality and realistic MRI sequences.
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.