Purpose of review Central nervous system (CNS) infections remain a major cause of morbidity and mortality worldwide, particularly in children, older adults, and immunocompromised patients. Timely recognition and classification of CNS infections are critical, yet early clinical and cerebrospinal fluid (CSF) findings are often non-specific. This review discusses when advanced neuroimaging adds value beyond conventional neuroimaging, where specific techniques are most useful, and how they can be integrated into pragmatic diagnostic pathways. Recent findings Multiplanar magnetic resonance imaging (MRI) with contrast is the workhorse for suspected CNS infection, but diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), MR perfusion, and MR spectroscopy (MRS) often change the leading diagnosis or management. DWI reliably distinguishes abscess from many neoplasms and helps detect early cerebritis, ventriculitis, and empyema. High-resolution SWI and vessel wall imaging improve recognition of infectious complications, such as venous thrombosis, microhemorrhage and vasculitis. Quantitative perfusion and MRS can support differentiation of infections from neoplasms and help characterize opportunistic infections in immunocompromised patients. Positron emission tomography (PET) and emerging radiomics and machine learning approaches may refine lesion characterization and treatment response assessment, although further investigation is required. Summary Advanced neuroimaging should be applied to specific clinical questions rather than used indiscriminately. In meningitis and ventriculitis, MRI with DWI and contrast enhanced T1 weighted images are key to defining complications and guiding urgent intervention. For encephalitis, cerebritis and ring-enhancing lesions, DWI, perfusion and MRS help distinguish abscess from neoplasm, tumefactive demyelination, and atypical infections. In immunocompromised patients, combining structural MRI with SWI, MRS, and metabolic imaging is particularly helpful. Future work must integrate quantitative metrics and AI-driven pattern analysis into multidisciplinary diagnostic algorithms for accurate diagnosis and management of CNS infections.
Mohan et al. (Thu,) studied this question.