Assessing the consistency of CT-based ventilation imaging under noise reduction processing with simulated quantum noise using a nonrigid alveoli phantom.

BACKGROUND

Previous studies have reported that quantum noise inherently present in CT images hinders the generation of CT-based ventilation image (CTVI), while quantum noise reduction approaches that do not affect CTVI have not yet been reported.

AIMS: The purpose of this study was to evaluate the impact of noise reduction preprocessing on the accuracy and robustness of CTVI in relation to quantum noise present in CT images.

METHODS AND MATERIAL: To reproduce the quantum noise, Gaussian noise (SD: 30, 80, 150 HU) was added to each inhalation and exhalation CT image. CTVIref and CTVInoise was generated from CTref and CTnoise. A median filter and the noise reduction by the CNN were also applied to the CT image, which contained the quantum noise, and CTVImed and CTVIcnn was created in the same manner as CTVIref. We evaluated whether the regions classified as high, middle, or low in CTVIref were accurately represented as high, middle, or low in CTVInoise, CTVImed and CTVIcnn. Additionally, to evaluate the ventilation function of each voxel, we compared two-dimensional histograms of CTVIref, CTVInoise, CTVImed and CTVIcnn.

STATISTICAL ANALYSIS USED: Cohen's kappa coefficient and Spearman's correlation were used to assess the agreement between CTVIref and each of the following: CTVInoise, CTVImed, and CTVIcnn.

RESULTS

CTVIcnn significantly improved categorical consistency and voxel-level correlation of CTVI, particularly under high-noise conditions (150 HU), outperforming both CTVInoise and CTVImed.

CONCLUSIONS

CNN-based denoising effectively improved the accuracy and robustness of CTVI under quantum noise.