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To investigate a novel locally adaptive projection space denoising algorithm for low‐dose CT data.

The denoising algorithm is based on bilateral filtering, which smooths values using a weighted average in a local neighborhood, with weights determined according to both spatial proximity and intensity similarity between the center pixel and the neighboring pixels. This filtering is locally adaptive and can preserve important edge information in the sinogram, thus maintaining high spatial resolution. A CT noise model that takes into account the bowtie filter and patient‐specific automatic exposure control effects is also incorporated into the denoising process. The authors evaluated the noise‐resolution properties of bilateral filtering incorporating such a CT noise model in phantom studies and preliminary patient studies with contrast‐enhanced abdominal CT exams.

On a thin wire phantom …