Hybrid convolution kernel: optimized CT of the head, neck, and spine.

Objective: Conventional CT requires generation of separate images utilizing different convolution kernels to optimize lesion detection. Our goal was to develop and test a hybrid CT algorithm to simultaneously optimize bone and soft-tissue characterization, potentially halving the number of images that need to be stored, transmitted, and reviewed.

Materials And Methods: CT images generated with separate high-pass (bone) and low-pass (soft tissue) kernels were retrospectively combined so that low-pass algorithm pixels less than -150 HU or greater than 150 HU are substituted with corresponding high-pass kernel reconstructed pixels.