MRI in Brain Tumor Evaluation: A Comprehensive Review

Abstract

Background: Magnetic Resonance Imaging (MRI) has emerged as a quintessential tool in the assessment and control of brain tumors due to its non-invasive nature and advanced soft-tissue assessment. This evaluation examines the modern-day applications and destiny possibilities of MRI in brain tumor evaluation. Traditional structural imaging, inclusive of T1- and T2-weighted imaging, plays a critical position in figuring out tumor place, size, and related edema, at the same time as advanced strategies offer deeper insights into tumor biology. Functional imaging modalities inclusive of Diffusion-Weighted Imaging (DWI) and Perfusion-Weighted Imaging (PWI) permit differentiation of tumor grades, identity of remedy results, and evaluation of tumor cellularity and vascularity. Magnetic Resonance Spectroscopy (MRS) enables the analysis of tumor metabolism, providing treasured records on biomarkers including choline and lactate. Emerging techniques like Chemical Exchange Saturation Transfer (CEST) MRI are being advanced for extra precise molecular and metabolic characterization. Future advancements encompass the mixing of synthetic intelligence (AI) for computerized tumor detection, category, and prediction of healing responses. AI models blended with radiomic evaluation hold promise for customized treatment strategies. Intraoperative MRI has more desirable surgical effects by using allowing actual-time imaging, enhancing tumor resection accuracy, and retaining healthy tissue. Furthermore, extremely-high-area MRI (7T) gives extraordinary spatial and contrast decision, facilitating particular evaluation of tumor microenvironments. Despite its transformative effect, challenges continue to be, inclusive of excessive charges, accessibility problems, and interpretation variability. The aim of this review is to explore the current and future advancements in MRI technology for brain tumor evaluation, highlighting the integration of AI, advanced imaging techniques, and intraoperative MRI to improve tumor detection, treatment planning, and surgical outcomes. It also addresses challenges such as cost, accessibility, and interpretation variability.