Combined multi-kernel head computed tomography images optimized for depicting both brain parenchyma and bone

Abstract

BACKGROUND: The hybrid convolution kernel technique for computed tomography (CT) is known to enable the depiction of an image set using different window settings.

OBJECTIVE: Our purpose was to decrease the number of artifacts in the hybrid convolution kernel technique for head CT and to determine whether our improved combined multi-kernel head CT images enabled diagnosis as a substitute for both brain (low-pass kernel-reconstructed) and bone (high-pass kernel-reconstructed) images.

METHODS: Forty-four patients with nondisplaced skull fractures were included. Our improved multi-kernel images were generated so that pixels of >100 Hounsfield unit in both brain and bone images were composed of CT values of bone images and other pixels were composed of CT values of brain images. Three radiologists compared the improved multi-kernel images with bone images.

RESULTS: The improved multi-kernel images and brain images were identically displayed on the brain window settings. All three radiologists agreed that the improved multi-kernel images on the bone window settings were sufficient for diagnosing skull fractures in all patients.

CONCLUSIONS: This improved multi-kernel technique has a simple algorithm and is practical for clinical use. Thus, simplified head CT examinations and fewer images that need to be stored can be expected.