Photon-Counting Computed Tomography for Evaluation of Coiled Intracranial Aneurysms.

Abstract

BACKGROUND AND PURPOSE: Intracranial aneurysms treated with endovascular embolization often require surveillance imaging using digital subtraction angiography, an invasive, risky, and expensive procedure. Existing non-invasive imaging modalities (standard computed tomography [CT] or magnetic resonance [MR] angiography) are often unsatisfactory for evaluating treated aneurysm due to artifacts from embolization devices. The objective of the present study was to determine whether photon-counting computer tomography (PCCT) imaging parameters could be optimized to confer satisfactory imaging resolution in an anthropomorphic phantom of treated intracranial aneurysms. MATERIALS AND METHODS: Phantom studies were performed using a model of the major intracranial arteries with appropriately sized, endovascularly treated middle cerebral artery (coil embolization) and basilar artery (woven endobridge [WEB] embolization) aneurysms. A series of imaging acquisition procedures were performed using a conventional energy-integrating CT (EICT) scanner and a photon-counting CT (PCCT) scanner. Key imaging acquisition and reconstruction parameters were varied to identify the optimum protocol for treated aneurysm characterization. Artifact reduction was performed on all images using the Siemens iterative metal artifact reduction (iMAR) algorithm. Contrast-to-noise ratio and metal artifact magnitude were quantitatively analyzed and displayed in tabular form to provide objective criteria for determination of optimal processing parameters for treated aneurysm visualization. RESULTS: Imaging was successfully obtained in phantom studies across a range of imaging parameters. Quantitative metal artifact magnitude was greater for 100keV virtual monoenergetic images (VMIs) and lowest for 55 keV VMIs without iMAR, but this trend was reversed with iMAR applied. The 55 keV VMI was chosen as the optimal reconstruction parameter for visualization of treated intracranial aneurysms as it demonstrated low magnitude of metal artifacts and the highest contrast-to-noise ratio (CNR) in adjacent vasculature. Similarly, CNR of the largest vessel adjacent to the coil mass was increased for all images after iMAR was applied. CNR was highest in the 55 keV VMR images both before (3.61±0.14) and after (6.82±0.34) application of iMAR. CONCLUSIONS: Virtual monoenergetic images combined with metal artifact reduction algorithms created from PCCT scans conferred excellent visualization of previously-treated intracranial aneurysms and adjacent vasculature. It was feasible to extend these results to preliminary clinical applications in human patients.