Objective: This review examines the diagnostic utility, clinical implications, and emerging advancements of diffusion-weighted imaging (DWI) in central nervous system (CNS) infections, synthesizing evidence to inform evidence-based practice.

Methods: A literature search of PubMed, Scopus, and Google Scholar (2018–2023) was conducted using keywords such as diffusion-weighted imaging, CNS infections, and apparent diffusion coefficient. Inclusion criteria prioritized peer-reviewed studies in high-impact journals (e.g., JAMA Neurology, The Lancet Infectious Diseases) evaluating DWI’s diagnostic or prognostic value. Case reports and animal studies were excluded.

Results: DWI demonstrated high diagnostic accuracy in distinguishing pyogenic abscesses (sensitivity: 94%, specificity: 96%) from necrotic tumors and identifying early cytotoxic edema in herpes simplex encephalitis (HSE), enabling timely antiviral therapy. In COVID-19-associated rhino-orbital-cerebral mucormycosis, DWI detected fungal angioinvasion earlier than conventional MRI, guiding surgical intervention. Apparent diffusion coefficient (ADC) histogram analysis differentiated tuberculomas from metastases (89% accuracy), averting misdiagnosis. Prognostically, DWI predicted hydrocephalus risk in cryptococcal meningitis (OR: 4.2) and cortical infarcts in bacterial meningitis, reducing mortality by 15%. Emerging innovations, including artificial intelligence (92% classification accuracy) and 7T MRI, enhanced diagnostic precision for parasitic infections like neurocysticercosis.

Conclusion: DWI is a cornerstone modality for diagnosing and managing CNS infections, offering rapid, non-invasive insights that improve therapeutic decision-making. Future integration of AI and hybrid imaging techniques holds promise for overcoming current limitations, advocating DWI’s expanded role in neuroinfectious disease protocols

Categories: Radiology, Neurology, Infectious Disease