Assessing the consistency of CT-based ventilation imaging under noise reduction processing with simulated quantum noise using a nonrigid alveoli phantom
BackgroundPrevious 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.AimsThe purpose of this study was to evaluate the impac...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Radiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fradi.2025.1567267/full |
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| author | Shin Miyakawa Hiraku Fuse Kenji Yasue Norikazu Koori Masato Takahashi Hiroki Nosaka Shunsuke Moriya Fumihiro Tomita Tatsuya Fujisaki |
| author_facet | Shin Miyakawa Hiraku Fuse Kenji Yasue Norikazu Koori Masato Takahashi Hiroki Nosaka Shunsuke Moriya Fumihiro Tomita Tatsuya Fujisaki |
| author_sort | Shin Miyakawa |
| collection | DOAJ |
| description | BackgroundPrevious 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.AimsThe 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 materialTo 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 usedCohen's kappa coefficient and Spearman's correlation were used to assess the agreement between CTVIref and each of the following: CTVInoise, CTVImed, and CTVIcnn.ResultsCTVIcnn significantly improved categorical consistency and voxel-level correlation of CTVI, particularly under high-noise conditions (150 HU), outperforming both CTVInoise and CTVImed.ConclusionsCNN-based denoising effectively improved the accuracy and robustness of CTVI under quantum noise. |
| format | Article |
| id | doaj-art-b6d3a635d37a4de3924d0a836b526d71 |
| institution | Kabale University |
| issn | 2673-8740 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Radiology |
| spelling | doaj-art-b6d3a635d37a4de3924d0a836b526d712025-08-20T03:28:15ZengFrontiers Media S.A.Frontiers in Radiology2673-87402025-07-01510.3389/fradi.2025.15672671567267Assessing the consistency of CT-based ventilation imaging under noise reduction processing with simulated quantum noise using a nonrigid alveoli phantomShin Miyakawa0Hiraku Fuse1Kenji Yasue2Norikazu Koori3Masato Takahashi4Hiroki Nosaka5Shunsuke Moriya6Fumihiro Tomita7Tatsuya Fujisaki8Department of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki, JapanDepartment of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki, JapanDepartment of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki, JapanDepartment of Radiological Technology, Niigata University of Health and Welfare, Niigata, JapanDepartment of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki, JapanDepartment of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki, JapanInstitute of Medicine, University of Tsukuba, Ibaraki, JapanDepartment of Radiation Oncology, St. Luke’s International Hospital, Tokyo, JapanDepartment of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki, JapanBackgroundPrevious 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.AimsThe 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 materialTo 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 usedCohen's kappa coefficient and Spearman's correlation were used to assess the agreement between CTVIref and each of the following: CTVInoise, CTVImed, and CTVIcnn.ResultsCTVIcnn significantly improved categorical consistency and voxel-level correlation of CTVI, particularly under high-noise conditions (150 HU), outperforming both CTVInoise and CTVImed.ConclusionsCNN-based denoising effectively improved the accuracy and robustness of CTVI under quantum noise.https://www.frontiersin.org/articles/10.3389/fradi.2025.1567267/fullcomputed tomography-based ventilation imagedeformable image registrationnoise reductionnonrigid alveoli phantomradiotherapy |
| spellingShingle | Shin Miyakawa Hiraku Fuse Kenji Yasue Norikazu Koori Masato Takahashi Hiroki Nosaka Shunsuke Moriya Fumihiro Tomita Tatsuya Fujisaki Assessing the consistency of CT-based ventilation imaging under noise reduction processing with simulated quantum noise using a nonrigid alveoli phantom Frontiers in Radiology computed tomography-based ventilation image deformable image registration noise reduction nonrigid alveoli phantom radiotherapy |
| title | Assessing the consistency of CT-based ventilation imaging under noise reduction processing with simulated quantum noise using a nonrigid alveoli phantom |
| title_full | Assessing the consistency of CT-based ventilation imaging under noise reduction processing with simulated quantum noise using a nonrigid alveoli phantom |
| title_fullStr | Assessing the consistency of CT-based ventilation imaging under noise reduction processing with simulated quantum noise using a nonrigid alveoli phantom |
| title_full_unstemmed | Assessing the consistency of CT-based ventilation imaging under noise reduction processing with simulated quantum noise using a nonrigid alveoli phantom |
| title_short | Assessing the consistency of CT-based ventilation imaging under noise reduction processing with simulated quantum noise using a nonrigid alveoli phantom |
| title_sort | assessing the consistency of ct based ventilation imaging under noise reduction processing with simulated quantum noise using a nonrigid alveoli phantom |
| topic | computed tomography-based ventilation image deformable image registration noise reduction nonrigid alveoli phantom radiotherapy |
| url | https://www.frontiersin.org/articles/10.3389/fradi.2025.1567267/full |
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