{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T22:58:49Z","timestamp":1771023529041,"version":"3.50.1"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Radiol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Aims<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods and material<\/jats:title><jats:p>To reproduce the quantum noise, Gaussian noise (SD: 30, 80, 150\u2005HU) was added to each inhalation and exhalation CT image. CTVI<jats:sub>ref<\/jats:sub> and CTVI<jats:sub>noise<\/jats:sub> was generated from CT<jats:sub>ref<\/jats:sub> and CT<jats:sub>noise<\/jats:sub>. A median filter and the noise reduction by the CNN were also applied to the CT image, which contained the quantum noise, and CTVI<jats:sub>med<\/jats:sub> and CTVI<jats:sub>cnn<\/jats:sub> was created in the same manner as CTVI<jats:sub>ref<\/jats:sub>. We evaluated whether the regions classified as high, middle, or low in CTVI<jats:sub>ref<\/jats:sub> were accurately represented as high, middle, or low in CTVI<jats:sub>noise<\/jats:sub>, CTVI<jats:sub>med<\/jats:sub> and CTVI<jats:sub>cnn<\/jats:sub>. Additionally, to evaluate the ventilation function of each voxel, we compared two-dimensional histograms of CTVI<jats:sub>ref<\/jats:sub>, CTVI<jats:sub>noise<\/jats:sub>, CTVI<jats:sub>med<\/jats:sub> and CTVI<jats:sub>cnn<\/jats:sub>.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical analysis used<\/jats:title><jats:p>Cohen's kappa coefficient and Spearman's correlation were used to assess the agreement between CTVI<jats:sub>ref<\/jats:sub> and each of the following: CTVI<jats:sub>noise<\/jats:sub>, CTVI<jats:sub>med<\/jats:sub>, and CTVI<jats:sub>cnn<\/jats:sub>.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>CTVI<jats:sub>cnn<\/jats:sub> significantly improved categorical consistency and voxel-level correlation of CTVI, particularly under high-noise conditions (150\u2005HU), outperforming both CTVI<jats:sub>noise<\/jats:sub> and CTVI<jats:sub>med<\/jats:sub>.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>CNN-based denoising effectively improved the accuracy and robustness of CTVI under quantum noise.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fradi.2025.1567267","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T05:43:57Z","timestamp":1752126237000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Assessing the consistency of CT-based ventilation imaging under noise reduction processing with simulated quantum noise using a nonrigid alveoli 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