The DOI matches its Crossref record; 38 of the 39 references it cites were found in a registry or an index.
Adaptive iterative dose reduction using 3D processing for reduced-and low-dose pulmonary CT: comparison with standard-dose CT for image noise reduction and radiological findings
Abstract
Objective The purpose of this study was to determine the utility of adaptive iterative dose reduction using 3D processing (AIDR 3D) for image noise reduction and assessment of radiologic findings obtained with reduced- and low-dose chest CT in patients with various pulmonary diseases. Subjects and methods Chest CT examinations at three different tube current settings and using 16- and 64-MDCT scanners were performed for 37 patients. Standard-dose (150 mAs) data were reconstructed as thin-section CT without AIDR 3D, and low-dose (25 mAs) and reduced-dose (50 mAs) data were reconstructed as thin-section CT without and with AIDR 3D. To compare image quality, image noises at all CT doses were quantitatively assessed by region of interest measurements. For comparison of radiologic finding assessments, likelihoods of occurrence of emphysema, ground-glass opacity, reticular opacity, bronchiectasis, honeycomb pattern, and nodules were evaluated on a 5-point scale. Then, image noise and agreements of radiologic findings between standard-dose CT and others were statistically evaluated. Results The image quality scores of reduced- and low-dose CT without AIDR 3D were significantly lower than those of both protocols with AIDR 3D and standard-dose CT (p 0.81). Conclusion AIDR 3D is useful for image noise reduction and assessment of radiologic findings obtained with reduced- and low-dose CT for patients with various pulmonary diseases.
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“Based on the fact that the amount of noise on an image is inversely proportional to the square root of the radiation dose, reducing tube current and tube voltage can inevitably lead to the degradation of image quality, thereby generating a larger amount of noise on the image [30,31].”
From A Novel Singular Value Decomposition-Based Denoising Method in 4-Dimensional Computed Tomography of the Brain in Stroke Patients with Statistical Evaluation (2020) · cites this paper as [31]
In the citing paper
“Based on the fact that the amount of noise on an image is inversely proportional to the square root of the radiation dose, reducing tube current and tube voltage can inevitably lead to the degradation of image quality, thereby generating a larger amount of noise on the image [30,31].”
From A Novel Singular Value Decomposition-Based Denoising Method in 4-Dimensional Computed Tomography of the Brain in Stroke Patients with Statistical Evaluation (2020) · cites this paper as [31]
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Schindera, S. T., Odedra, D., Raza, S. A., Kim, T. K., Jang, H.-J., Szucs-Farkas, Z., & Rogalla, P. (2013) · Radiology · doi:10.1148/radiol.13122349 · Cited by 159
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Metadata
- Authors
- Ohno, Y., Takenaka, D., Kanda, T., Yoshikawa, T., Matsumoto, S., Sugihara, N., & Sugimura, K.
- Affiliations
- Kobe University (JP)
- Hyogo Prefectural Cancer Center (JP)
- Toshiba (Japan) (JP)
- Venue
- American Journal of Roentgenology
- Publisher
- American Roentgen Ray Society
- Published
- 2012-10
- Volume
- 199
- Issue
- 4
- Pages
- W477-W485
- ISSN
- 0002-9580 (ISSN-L)
- 0361-803X
- 1546-3141
- OpenAlex
- W1964060591
- Cited by
- 79
- References
- 39
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 3 · 2023: 4 · 2022: 4 · 2021: 3 · 2020: 4 · 2019: 5 · 2018: 8 · 2017: 3 · 2016: 8 · 2015: 13 · 2014: 11 · 2013: 7 · 2012: 1
- FWCI
- 7.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- American Journal of Roentgenology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiation Dose and Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Keywords
- adaptive iterative dose reduction, low-dose CT, chest CT, image denoising, image quality, lung disease, emphysema, ground-glass opacities, bronchiectasis, honeycomb pattern, pulmonary nodules, interobserver agreement, tube current reduction
- MeSH terms
- Adult, Aged, Aged, 80 and over, Female, Humans, Image Processing, Computer-Assisted, Lung, Lung Diseases, Male, Middle Aged, Radiation Dosage, Radiographic Image Enhancement, Tomography, X-Ray Computed, Phantoms, Imaging, Imaging, Three-Dimensional, Signal-To-Noise Ratio, Multidetector Computed Tomography
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- CoChat. (2026). Citation check: Adaptive iterative dose reduction using 3D processing for reduced-and low-dose pulmonary CT: comparison with standard-dose CT for image noise reduction and radiological findings. https://app.cochat.ai/papers/10.2214/ajr.11.8275
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- Ohno, Y., Takenaka, D., Kanda, T., Yoshikawa, T., Matsumoto, S., Sugihara, N., & Sugimura, K. (2012). Adaptive iterative dose reduction using 3D processing for reduced-and low-dose pulmonary CT: comparison with standard-dose CT for image noise reduction and radiological findings. American Journal of Roentgenology. https://doi.org/10.2214/ajr.11.8275
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