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

TL;DR

This paper presents a GAN-based method to synthesize missing MRI sequences from available ones in glioblastoma imaging, enhancing diagnostic tools and AI applications in brain tumor analysis.

Contribution

The study introduces a GAN approach integrated into the GaNDLF framework to generate missing MRI sequences from three available modalities, improving data completeness for glioblastoma imaging.

Findings

High-quality MRI sequence synthesis achieved

Improved diagnostic potential for clinicians

Enhanced AI-based brain tumor analysis

Abstract

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.