Evidence map›Paper›PMID 40703913›Full record

ArticleFrontiers in radiology2025

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

Shin Miyakawa, Hiraku Fuse, Kenji Yasue, Norikazu Koori, Masato Takahashi, Hiroki Nosaka, Shunsuke Moriya, Fumihiro Tomita, Tatsuya Fujisaki

Abstract read
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In one paragraph

Article in Frontiers in radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Shin MiyakawaDepartment of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki, Japan.
Hiraku FuseDepartment of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki, Japan.
Kenji YasueDepartment of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki, Japan.
Norikazu KooriDepartment of Radiological Technology, Niigata University of Health and Welfare, Niigata, Japan.
Masato TakahashiDepartment of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki, Japan.
Hiroki NosakaDepartment of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki, Japan.
Shunsuke MoriyaInstitute of Medicine, University of Tsukuba, Ibaraki, Japan.
Fumihiro TomitaDepartment of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan.
Tatsuya FujisakiDepartment of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 Statistical analysis used: Cohen's kappa coefficient and Spearman's correlation were used to assess the agreement between CTVI Results: CTVI Conclusions: CNN-based denoising effectively improved the accuracy and robustness of CTVI under quantum noise.

Indexed as

computed tomography-based ventilation imagedeformable image registrationnoise reductionnonrigid alveoli phantomradiotherapy

Identifiers

PMID40703913
PMCPMC12283728

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