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. CTVI_ref and CTVI_noise was generated from CT_ref and CT_noise. A median filter and the noise reduction by the CNN were also applied to the CT image, which contained the quantum noise, and CTVI_med and CTVI_cnn was created in the same manner as CTVI_ref. We evaluated whether the regions classified as high, middle, or low in CTVI_ref were accurately represented as high, middle, or low in CTVI_noise, CTVI_med and CTVI_cnn. Additionally, to evaluate the ventilation function of each voxel, we compared two-dimensional histograms of CTVI_ref, CTVI_noise, CTVI_med and CTVI_cnn.

Statistical analysis used: Cohen's kappa coefficient and Spearman's correlation were used to assess the agreement between CTVI_ref and each of the following: CTVI_noise, CTVI_med, and CTVI_cnn.

Results: CTVI_cnn significantly improved categorical consistency and voxel-level correlation of CTVI, particularly under high-noise conditions (150 HU), outperforming both CTVI_noise and CTVI_med.

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