Ultra-high resolution CT angiography for the assessment of intracranial stents and flow diverters using photon counting detector CT.

BACKGROUND

The patency of intracranial stents may not be reliably assessed with either CT angiography or MR angiography due to imaging artifacts. We investigated the potential of ultra-high resolution CT angiography using a photon counting detector (PCD) CT to address this limitation by optimizing scanning and reconstruction parameters.

METHODS

A phantom with different flow diverters was used to optimize PCD-CT reconstruction parameters, followed by imaging of 14 patients with intracranial stents using PCD-CT. Images were reconstructed using three kernels based on the phantom results (Hv56, Hv64, and Hv72; Hv=head vascular) and one kernel to virtually match the resolution of standard CT angiography (Hv40). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measurements were calculated. Subjective image quality and diagnostic confidence (DC) were assessed using a five point visual grading scale (5=best, 1=worst) and a three point grading scale (1=best, 3=worst), respectively, by two independent neuroradiologists.

RESULTS

Phantom images demonstrated the highest image quality across dose levels for 0.2 mm reconstructions with Hv56 (4.5), Hv64 (5), and Hv72 (5). In patient images, SNR and CNR decreased significantly with increasing kernel sharpness compared with control parameters. All reconstructions showed significantly higher image quality and DC compared with the control reconstruction with Hv40 kernel (P<0.001), with both image quality and DC being highest with Hv64 (0.2 mm) and Hv72 (0.2 mm) reconstructions.

CONCLUSION

Ultra-high resolution PDC-CT angiography provides excellent visualization of intracranial stents, with optimal reconstructions using the Hv64 and the Hv72 kernels at 0.2 mm.

REGISTRATION

BASEC 2021-00343.

Informations de copyright

© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: TF is an employee of Siemens Healthcare. HA has received institutional grants from Bayer, Canon, Guerbet, and Siemens. HA received speaker honoraria from Siemens.