Evaluation of a Metal Artifacts Reduction Algorithm Applied to Postinterventional Flat Panel Detector CT Imaging

BACKGROUND AND PURPOSE:Flat panel detector CT images are degraded by streak artifacts caused by radiodense implanted materials such as coils or clips. A new metal artifacts reduction prototype algorithm has been used to minimize these artifacts. The application of this new metal artifacts reduction algorithm was evaluated for flat panel detector CT imaging performed in a routine clinical setting.MATERIALS AND METHODS:Flat panel detector CT images were obtained from 59 patients immediately following cerebral endovascular procedures or as surveillance imaging for cerebral endovascular or surgical procedures previously performed. The images were independently evaluated by 7 physicians for metal artifacts reduction on a 3-point scale at 2 locations: immediately adjacent to the metallic implant and 3 cm away from it. The number of visible vessels before and after metal artifacts reduction correction was also evaluated within a 3-cm radius around the metallic implant.RESULTS:The metal artifacts reduction algorithm was applied to the 59 flat panel detector CT datasets without complications. The metal artifacts in the reduction-corrected flat panel detector CT images were significantly reduced in the area immediately adjacent to the implanted metal object (P = .05) and in the area 3 cm away from the metal object (P = .03). The average number of visible vessel segments increased from 4.07 to 5.29 (P = .1235) after application of the metal artifacts reduction algorithm to the flat panel detector CT images.CONCLUSIONS:Metal artifacts reduction is an effective method to improve flat panel detector CT images degraded by metal artifacts. Metal artifacts are significantly decreased by the metal artifacts reduction algorithm, and there was a trend toward increased vessel-segment visualization.

With continued technologic developments, both diagnostic and interventional neuroendovascular applications of flat panel detector CT (FDCT) have increasingly expanded. FDCT offers higher spatial resolution relative to multidetector row CT and improved visualization of clipped aneurysms and endovascular stents.¹ Imaging performed in the endovascular suite also offers rapid visualization of periprocedural complications, thus allowing faster management.² The acquired FDCT images, however, are degraded by artifacts created by radiodense cerebral endovascular and surgical implants including coils, stents, and clips, limiting the use of FDCT as a postprocedural imaging technique.Metal artifacts reduction (MAR) in CT images has been attempted by both interpolation and algebraic methods. Interpolation methods have been quite successful and, most important, require less computational power relative to algebraic methods, resulting in less computing time. One of the first interpolation-based MAR algorithms used a 1D linear interpolation for single-row CT data, which is inadequate for FDCT data.³ The MAR algorithm investigated in this work, developed by Siemens Healthcare Sector, is a modification and extension of a recently published MAR procedure by Prell et al.⁴ We present validation of the FDCT MAR prototype algorithm by using a phantom study and an objective critique by 7 clinicians.