Evidence map›Paper›PMID 42095993›Full record

ArticleEuropean radiology experimental2026

Reconstruction algorithms and arm positioning effects on abdominal CT image quality and radiation dose: a phantom study.

Han Song Mun, Sanghyeok Lim, Shinhyung Kang

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Article in European radiology experimental, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Han Song MunDepartment of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.ORCID http://orcid.org/0000-0003-4718-1788
Sanghyeok LimDepartment of Radiology, Soonchunhyang University Hospital Bucheon, Soonchunhyang University College of Medicine, Bucheon-si, Gyeonggi-do, Republic of Korea. parcel79@gmail.com.ORCID http://orcid.org/0000-0002-1607-3744
Shinhyung KangGE Healthcare Co., Ltd, Seoul, Republic of Korea.ORCID http://orcid.org/0009-0003-9428-6029

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate the effects of arm positioning and reconstruction algorithms on radiation dose and image quality of abdominal CT. MATERIALS AND

methodsAbdominal CT scans were performed using a customized body phantom at 100 kVp with automatic tube current modulation based on noise indices (NI) of 9, 11, and 13 across seven arm positions: arms up (AU), arms down (AD), arms down with one or two-layered air cushions (ADAC, ADAC2), arms on the belly (AB), and arms on the belly with one or two-layered air cushions (ABAC, ABAC2). Images were reconstructed using filtered back projection (FBP), iterative reconstruction, and deep learning-based reconstruction (DLIR). Radiation dose was recorded. Quantitative image quality metrics included image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), sharpness, and structural similarity. Qualitative assessment of sharpness, noise, artifacts, and overall image quality was performed using fixed-dose AU-FBP images as the reference standard.

resultsCompared with the AU position, radiation dose increased by up to 83% in non-AU configurations, with similar trends across all NI settings. In contrast, air cushion use caused only minor, configuration-dependent dose changes. Qualitatively, non-AU FBP images showed marked degradation, whereas DLIR enabled selected non-AU configurations to approach near-reference diagnostic quality. Air cushion-related benefits were modest overall but more apparent in AB than AD configurations.

conclusionArm positioning is the primary determinant of radiation dose and image quality in abdominal CT. When arm elevation is not feasible, DLIR effectively mitigates image quality degradation, while air cushions provide limited, configuration-dependent benefits. RELEVANCE STATEMENT: When arms cannot be raised during abdominal CT, avoiding arms-on-belly and using air cushions may help maintain image quality without substantially increasing radiation dose, while deep learning image reconstruction further enhances diagnostic performance under restricted positioning conditions. KEY POINTS: Arm positioning is a major determinant of radiation dose and image quality in abdominal CT. Arms-on-belly positioning produces poorer image quality despite increased radiation dose. DLIR improves image quality across arm positions but does not eliminate positioning-related degradation.

Indexed as

AlgorithmsImage Processing, Computer-AssistedPatient PositioningRadiation DosageRadiographic Image Interpretation, Computer-AssistedRadiography, AbdominalTomography, X-Ray ComputedHumansPhantoms, ImagingSignal-To-Noise RatioImage reconstructionMultidetector computed tomographyPatient positioningPhantoms (imaging)Radiation dosage

Identifiers

PMID42095993
PMCPMC13153281

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.