Evidence map›Paper›PMID 30448900›Full record

ArticleEmergency radiology2019

Low-dose CT imaging of the acute abdomen using model-based iterative reconstruction: a prospective study.

Fiachra Moloney, Karl James, Maria Twomey, David Ryan, Tyler M Grey, Amber Downes, Richard G Kavanagh, Niamh Moore, Mary Jane Murphy, Jackie Bye and 7 more

Registry-linked trialAbstract read
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In one paragraph

Article in Emergency radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05651360 (Diagnostic Performance of Deep Learning Image Reconstruction in Low Dose CT for the Detection of Acute Abdominal Conditions), which is not on this map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05651360 completednot on this mapstarted 2022, after this paper: background citation

Diagnostic Performance of Deep Learning Image Reconstruction in Low Dose CT for the Detection of Acute Abdominal Conditions

TypeobservationalSponsorOslo University HospitalRan2022 to 2023Enrolled246ConditionsAbdominal Pain, Acute Pain, Acute Cholecystitis, Acute AppendicitisArmslow-dose CT
3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Fiachra MoloneyDepartment of Radiology, Cork University Hospital, University College Cork, Cork, Ireland.
Karl JamesDepartment of Radiology, Cork University Hospital, University College Cork, Cork, Ireland.
Maria TwomeyDepartment of Radiology, Cork University Hospital, University College Cork, Cork, Ireland.
David RyanDepartment of Radiology, Cork University Hospital, University College Cork, Cork, Ireland.
Tyler M GreySchool of Medicine, University College Cork, Cork, Ireland.
Amber DownesSchool of Medicine, University College Cork, Cork, Ireland.
Richard G KavanaghDepartment of Radiology, Cork University Hospital, University College Cork, Cork, Ireland.
Niamh MooreDepartment of Radiology, Cork University Hospital, University College Cork, Cork, Ireland.
Mary Jane MurphyDepartment of Radiology, Cork University Hospital, University College Cork, Cork, Ireland.
Jackie ByeGE Healthcare, Chicago, IL, USA.
Brian W CareyDepartment of Radiology, Cork University Hospital, University College Cork, Cork, Ireland. brian_carey@ucc.ie.ORCID http://orcid.org/0000-0002-6584-8032
Sean E McSweeneyDepartment of Radiology, Cork University Hospital, University College Cork, Cork, Ireland.
Conor DeasyDepartment of Emergency Medicine, Cork University Hospital, Cork, Ireland.
Emmett AndrewsDepartment of Surgery, Cork University Hospital, University College Cork, Cork, Ireland.
Fergus ShanahanDepartment of Medicine, Cork University Hospital, University College Cork, Cork, Ireland.
Michael M MaherDepartment of Radiology, Cork University Hospital, University College Cork, Cork, Ireland.
Owen J O'ConnorDepartment of Radiology, Cork University Hospital, University College Cork, Cork, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesPerformance of a modified abdominopelvic CT protocol reconstructed using full iterative reconstruction (IR) was assessed for imaging patients presenting with acute abdominal symptoms. MATERIALS AND

methodsFifty-seven patients (17 male, 40 female; mean age of 56.5 ± 8 years) were prospectively studied. Low-dose (LD) and conventional-dose (CD) CTs were contemporaneously acquired between November 2015 and March 2016. The LD and CD protocols imparted radiation exposures approximating 10-20% and 80-90% those of routine abdominopelvic CT, respectively. The LD images were reconstructed with model-based iterative reconstruction (MBIR), and CD images with hybrid IR (40% adaptive statistical iterative reconstruction (ASIR)). Image quality was assessed quantitatively and qualitatively. Independent clinical interpretations were performed with a 6-week delay between reviews.

resultsA 74.7% mean radiation dose reduction was achieved: LD effective dose (ED) 2.38 ± 1.78 mSv (size-specific dose estimate (SSDE) 3.77 ± 1.97 mGy); CD ED 7.04 ± 4.89 mSv (SSDE 10.74 ± 5.5 mGy). LD-MBIR images had significantly lower objective and subjective image noise compared with CD-ASIR (p < 0.0001). Noise reduction for LD-MBIR studies was greater for patients with BMI < 25 kg/m

conclusionsLow-dose abdominopelvic CT performed with MBIR is a feasible radiation dose reduction strategy for imaging patients presenting with acute abdominal pain.

Indexed as

Abdomen, AcuteContrast MediaDiatrizoate MeglumineFemaleHumansIohexolMaleMiddle AgedProspective StudiesRadiation DosageRadiographic Image Interpretation, Computer-AssistedSensitivity and SpecificityTomography, X-Ray ComputedContrast MediaDiatrizoate MeglumineIohexolAcute abdominal imagingComputed tomographyDose optimizationIterative reconstructionRadiation dose reduction

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Registered trials

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.