Evidence map›Paper›PMID 42103976›Full record

ArticleScientific reports2026

Evaluation of image quality between hybrid iterative reconstruction and deep learning reconstruction in low dose abdominopelvic CT in low body mass index individuals.

C Abraham Jacob, Nitika C Panakkal, Shailesh Nayak, Rajagopal Kadavigere, Thejas M Shivakumar, Jyoti Garg, Shivanath Shanbhag, Suresh Sukumar

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In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

C Abraham JacobDepartment of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, India.
Nitika C PanakkalDepartment of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, India. nitika.cp@manipal.edu.ORCID http://orcid.org/0000-0003-3725-6774
Shailesh NayakDepartment of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, India.
Rajagopal KadavigereRadio-Diagnosis & Imaging, Department of Radiodiagnosis & Medical Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Thejas M ShivakumarDepartment of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, India.
Jyoti GargDepartment of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, India.
Shivanath ShanbhagDepartment of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, India.
Suresh SukumarDepartment of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study aimed to compare image quality between hybrid iterative reconstruction and deep learning image reconstruction (DLIR) in low dose contrast enhanced CT (CECT) abdomen and pelvis scans in low body mass index (BMI) individuals, to determine whether DLIR provides superior image quality at a given dose. Fifty patients with BMI < 18.5 kg/m

Indexed as

AbdomenDeep LearningImage Processing, Computer-AssistedPelvisRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAdultAgedAlgorithmsBody Mass IndexContrast MediaFemaleHumansLiverMaleMiddle AgedContrast MediaContrast to noise ratioDeep learning reconstructionImage noiseImage qualityLow BMISignal to noise ratio

Identifiers

PMID42103976
PMCPMC13342276

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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.