Background: The aim of this study was (1) to assess the diagnostic accuracy of 320-detector row computed tomography (CT) for paraclinoid and intracavernous aneurysms, and (2) to investigate whether this method provides sufficient information for surgery. Methods: A total of 14 patients with 16 unruptured proximal ICA aneurysms underwent three-dimensional CT angiography (3D-CTA) fusion imaging, which was created by superimposing 3D-CT venography data and/or 3D-bone data onto 3D-CTA data using 320-detector row CT, magnetic resonance imaging (MRI), and 3D digital subtraction angiography (DSA). The images of each modality were assessed using intraoperative findings as the reference standard. Results: All aneurysms were clearly visualized on 320-detector row CT. Bone subtraction and arterio-venous discrimination were accurate. On 3D-CTA fusion images, 11 aneurysms were diagnosed as "extracavernous" and five as "intracavernous". No discordance in aneurysm location between the 3D-CTA fusion images and the intraoperative findings was found. In contrast, discordance between MRI and intraoperative findings were found in five of the 16 cases (31 %), which was significantly more frequent than with 3D-CTA (p = 0.043). The findings DSA, which was performed in nine patients, were also in excellent agreement with the intraoperative findings. However, 3D-CTA fusion imaging provided more comprehensive information, including venous and osseous structures, than 3D-DSA. The 320-detector row CTA after surgery demonstrated a clear relationship between the clip and aneurysmal neck with notably few artifacts, which suggested the utility of this modality for postoperative assessment. Conclusions: The 320-detector row CT provided high accuracy for the diagnosis of paraclinoid and intracavernous aneurysms. This technique also provided comprehensive depiction of the aneurysms and surrounding structures. Therefore, this modality might be useful for the diagnosis of the paraclinoid and intracavernous aneurysms and for developing a surgical treatment plan.
All Science Journal Classification (ASJC) codes
- Clinical Neurology