320-Row Multidetector CT Angiography in the Detection of Critical Cerebrovascular Anomalies

Can J Neurol Sci. 2016 Jul;43(4):543-8. doi: 10.1017/cjn.2016.11. Epub 2016 Mar 10.

Abstract

Background: The acquisition of a new 320-row multidetector computed tomography angiography (CTA) scanner at the Montreal Neurological Institute and Hospital has provided higher quality imaging with less radiation exposure and shorter time of acquisition. However, its reliability has not been fully proven in critical vascular lesions when it comes to replacing a more invasive examination such as cerebral angiography. We wished to validate the accuracy of this equipment to investigate four common indications for patients to undergo conventional digital subtraction angiography: subarachnoid hemorrhage, vasospasm, unusual intracerebral hemorrhage, and unruptured aneurysm.

Methods: Radiological reports and relevant imaging from 82 consecutive subjects who underwent a 320-row multidetector CTA followed by cerebral angiography from February 2010 to February 2014 were retrospectively analysed. A total of 102 cerebrovascular anomalies were found. Reports from both imaging modalities were compared to determine the diagnostic accuracy of CTA.

Results: The overall sensitivity and specificity of 320-row multidetector CTA for detecting cerebrovascular abnormalities were, respectively, 97.60% and 63.20%. Similar results were obtained for all four categories of clinical indications.

Conclusion: Results obtained from CTA were consistent with those obtained on digital subtraction angiography regardless of the vascular pathology. To our knowledge, this study is the first validating the accuracy of 320-row CTA in diagnosing critical cerebrovascular lesions.

Keywords: Neuroimaging; neurovascular.

MeSH terms

  • Adult
  • Aged
  • Angiography, Digital Subtraction / methods*
  • Cerebrovascular Disorders / diagnostic imaging*
  • Computed Tomography Angiography / methods*
  • Databases, Factual / statistics & numerical data
  • Female
  • Humans
  • Image Processing, Computer-Assisted*
  • Male
  • Middle Aged
  • Retrospective Studies