COMPARATIVE STUDY
JOURNAL ARTICLE
REVIEW
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Current Perspectives in Imaging Modalities for the Assessment of Unruptured Intracranial Aneurysms: A Comparative Analysis and Review.

BACKGROUND: Intracranial aneurysms (IAs) are pathologic dilatations of cerebral arteries. This systematic review summarizes and compares imaging techniques for assessing unruptured IAs (UIAs). This review also addresses their uses in different scopes of practice. Pathophysiologic mechanisms are reviewed to better understand the clinical usefulness of each imaging modality.

METHODS: A literature review was performed using PubMed with these search terms: "intracranial aneurysm," "cerebral aneurysm," "magnetic resonance angiography (MRA)," computed tomography angiography (CTA)," "catheter angiography," "digital subtraction angiography," "molecular imaging," "ferumoxytol," and "myeloperoxidase". Only studies in English were cited.

RESULTS: Since the development and improvement of noninvasive diagnostic imaging (computed tomography angiography and magnetic resonance angiography), many prospective studies and meta-analyses have compared these tests with gold standard digital subtraction angiography (DSA). Although computed tomography angiography and magnetic resonance angiography have lower detection rates for UIAs, they are vital in the treatment and follow-up of UIAs. The reduction in ionizing radiation and lack of endovascular instrumentation with these modalities provide benefits compared with DSA. Novel molecular imaging techniques to detect inflammation within the aneurysmal wall with the goal of stratifying risk based on level of inflammation are under investigation.

CONCLUSIONS: DSA remains the gold standard for preoperative planning and follow-up for patients with IA. Newer imaging modalities such as ferumoxytol-enhanced magnetic resonance imaging are emerging techniques that provide critical in vivo information about the inflammatory milieu within aneurysm walls. With further study, these techniques may provide aneurysm rupture risk and prediction models for individualized patient care.

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