Deep Neural Network Modeling for Brain Tumor Classification Using Magnetic Resonance Spectroscopic Imaging

This study is motivated by the intricate and expert-demanding nature of magnetic resonance spectroscopy imaging (MRSI) data processing, particularly in the context of brain tumor examinations. Traditional approaches often involve complex manual procedures, requiring substantial expertise. In response, we explore the application of deep neural networks directly on raw MRSI data in the time domain. With brain tumors posing significant health concerns, the imperative for early and accurate detection is paramount for effective treatment. While conventional MRI methods face limitations in rapid and accurate spatial evaluation of diffusive gliomas, accuracy and efficiency are compromised. In contrast, MRSI emerges as a promising tool, offering insights into tissue chemical composition and metabolic alterations. Our proposed model, leveraging deep neural networks, is specifically designed for spectral time series analysis and classification tasks. Trained on a dataset comprising synthetic and real MRSI data from brain tumor patients, the model aims to distinguish MRSI voxels indicative of pathologies from healthy ones. Our results demonstrate the model's robustness in domain transformation, seamlessly adapting from synthetic spectra to in vivo data through a fine-tuning process. Successful classification of MRSI voxels of glioma from healthy tissues underscores the model's potential in clinical applications, signifying a transformative impact on diagnostic and prognostic evaluations in brain tumor examinations. Ongoing research endeavors are directed towards validating these integrated approaches across larger datasets, with the ultimate goal of establishing standardized guidelines and further enhancing their clinical utility.