Effective noise reduction and equalization in projection domain

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CT image quality is affected by various artifacts including noise. Among these artifacts of different causes, noisy data due to photon starvation should be contained in early processing stage to better mitigate other artifacts as they can cause severe streaks and noise in reconstructed CT image. For low dose imaging, it is critical to use effective processing method to handle the photon starved data in order to obtain required image quality with desired resolution, texture, low contrast detectability. In this paper, two promising projection domain noise reduction methods are proposed. They are derived from (1) the noise model that connects the noise behaviors in count and attenuation; (2) predicted noise reduction from a finite impulse response (FIR) filter; (3) two pre-determined noise reduction requirements (noise equalization and electronic noise suppression). Both methods showed significant streaks and noise reduction in tested cases while reasonably maintaining the resolution of the images.

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