ORIGINAL RESEARCH article
Front. Radiol.
Sec. Artificial Intelligence in Radiology
Volume 5 - 2025 | doi: 10.3389/fradi.2025.1567267
This article is part of the Research TopicFoundation Models for Healthcare: Innovations in Generative AI, Computer Vision, Language Models, and Multimodal SystemsView all 10 articles
Assessing the Consistency of CT-Based Ventilation Imaging under Noise Reduction Processing with Simulated Quantum Noise Using a Nonrigid Alveoli Phantom
Provisionally accepted
Shin Miyakawa1*
Hiraku Fuse1Kenji Yasue1Norikazu Koori2Masato Takahashi1
Hiroki Nosaka1Shunsuke Moriya3Fumihiro Tomita4Tatsuya Fujisaki1- 1Ibaraki Prefectural University of Health Sciences, Mito, Japan
- 2Niigata University of Health and Welfare, Niigata, Niigata, Japan
- 3University of Tsukuba, Tsukuba, Ibaraki, Japan
- 4St. Luke's International Hospital, Tokyo, Japan
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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.
Keywords: computed tomography-based ventilation image, Deformable Image Registration, Noise Reduction, nonrigid alveoli phantom, Radiotherapy
Received: 27 Jan 2025; Accepted: 17 Jun 2025.
Copyright: © 2025 Miyakawa, Fuse, Yasue, Koori, Takahashi, Nosaka, Moriya, Tomita and Fujisaki. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Shin Miyakawa, Ibaraki Prefectural University of Health Sciences, Mito, Japan
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