Impact of noise reduction schemes on quantitative accuracy of CT numbers

Purpose In previous works, it has been demonstrated that for filtered backprojection (FBP) reconstruction‐based computed tomography (CT) images, the measured CT numbers are biased and the bias level decreases with increasing radiation dose. Low‐dose scans typically include noise reduction schemes to reduce noise level. The purpose of this work was to investigate the potential impact of different noise reduction schemes on the CT number bias. Methods Three different filtration methods: Gaussian, adaptive trimmed mean (ATM), and anisotropic diffusion (AD) were implemented to reduce noise. All filters were independently applied in three different domains: raw counts, log‐processed sinogram, or reconstructed image domain. A quality assurance phantom was scanned on a benchtop CT cone beam CT system, at dose levels ranging from 0.6 to 4.0 mGy. The conventional FBP reconstructions were performed to reconstruct CT images for the study of CT number biases. The CT number bias of different material inserts in the phantom was then measured. To further study the overall impact of CT number bias together with the potential consequences of noise reduction schemes on both the spatial resolution and noise characteristics, the task‐based detectability of a high‐contrast and high spatial resolution imaging task was used as an example to assess the performance of each noise reduction scheme. To qualitatively assess the impact of these noise reduction schemes on image, an anthropomorphic head phantom was also scanned on the benchtop CT system and processed with the above noise reduction schemes to generate images for demonstration. Results Our results demonstrated the following major findings: (a) CT number bias can be significantly reduced when the noise reduction schemes are implemented in the raw counts domain; CT number bias cannot be reduced when these noise reduction schemes are implemented either in the reconstructed image domain or in the log‐processed sinogram domain. (b) The extent of CT number bias reduction is dependent on both the material composition and noise reduction parameters. (c) The overall impact of the noise reduction schemes can be studied using the task‐based detectability analysis framework and this framework can be used to select the appropriate parameters in each noise reduction scheme to optimize the performance for a given imaging task. Conclusions Noise reduction schemes can be used to considerably reduce CT number bias when they are implemented in the raw counts domain; however, their application cannot be arbitrarily extended to either the log‐processed sinogram data domain or image domain. Trade‐offs between bias reduction and overall image quality must be studied for an optimal performance of a given imaging task.