THE ROLE OF INTEGRATIVE MRI IN DIAGNOSING PATHOLOGICAL PROCESSES IN THE BRAIN PARENCHYMA
Keywords:
magnetic resonance imaging, neuroimaging, diffusion tensor imaging, MR spectroscopy, perfusion-weighted imaging, quantitative neuroradiologyAbstract
Introduction: Pathological processes within the brain parenchyma represent one of the leading causes of neurological impairment and mortality. The diverse nature of these pathologies and their asymptomatic progression in early stages significantly complicate their diagnosis.
Objective: To determine the prognostic value of quantitative parameters in the differential diagnosis of pathological processes in the brain parenchyma based on modern magnetic resonance imaging (MRI) protocols.
Materials and Methods: A prospective cohort study was conducted from 2021 to 2025 involving 107 patients. All subjects underwent MRI using a standard protocol (T1-WI, T2-WI, FLAIR, DWI, SWI). Gadolinium-based contrast agents were administered in 92% of cases. MR spectroscopy was performed in 23 patients, MR perfusion in 19 patients, and diffusion tensor imaging (DTI) in 13 patients. Quantitative analysis included calculation of ADC (Apparent Diffusion Coefficient), rCBV (relative Cerebral Blood Volume), and metabolite ratios.
Results: The overall diagnostic accuracy of MRI was 97.3%. ADC values enabled differentiation of metastatic lesions (0.72 ± 0.08 ×10⁻³ mm²/s) from glioblastoma multiforme (0.83 ± 0.11 ×10⁻³ mm²/s) with a sensitivity of 94.2% (p<0.001). rCBV values were significantly higher in meningiomas (8.3 ± 2.1) compared to gliomas (5.2 ± 1.8) (p<0.01). An elevated Cho/NAA ratio greater than 2.5 on MR spectroscopy showed a diagnostic accuracy of 45.7% as a prognostic indicator of malignant progression.
Conclusion: A multiparametric MRI approach provides a reproducible and quantitative methodology for the differential diagnosis of pathological processes in the brain parenchyma. Quantitative MRI parameters offer the potential to establish morphofunctional correlations and to develop individualized prognostic models.
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