Evidence map›Paper›PMID 42192227›Full record

ArticleJournal of applied clinical medical physics2026

Evaluation of a super-resolution deep learning reconstruction algorithm in abdominal CT imaging-A qualitative and quantitative performance analysis.

Aria M Salyapongse, Martin G Wagner, Meghan G Lubner, Kelsey L Schluter, Matthew R Smith, Michio D Taya, Andrew L Wentland, Timothy P Szczykutowicz, Giuseppe V Toia

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of applied clinical medical physics, 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

9 authors.

Aria M SalyapongseDepartments of Medical Physics and Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Martin G WagnerDepartments of Medical Physics and Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Meghan G LubnerDepartment of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Kelsey L SchluterDepartment of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Matthew R SmithDepartment of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Michio D TayaDepartment of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Andrew L WentlandDepartments of Medical Physics and Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Timothy P SzczykutowiczDepartments of Medical Physics and Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Giuseppe V ToiaDepartments of Medical Physics and Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA super-resolution deep learning (DL) image reconstruction algorithm (Precise Image Quality Engine (PIQE)) was originally designed for cardiac CT, but is now available for abdominal CT. PURPOSE: To examine objective and subjective image quality (IQ) improvements PIQE compared to Advanced Intelligent Clear-IQ Engine (AiCE) in abdominal CT.

methodsA retrospective analysis was conducted on 69 adult patient routine contrast enhanced abdominopelvic CT exams on a single Aquilion ONE/INSIGHT CT system (Canon Medical Systems, Otawara Japan). Images were reconstructed using PIQE (strength level L1 and L2) and AiCE (L1- the institutional standard). Four blinded radiologists assessed image noise, image contrast, small structure visibility, image sharpness, artifacts, and overall image preference with Likert scales. Reader agreement was assessed with Krippendorff's alpha. Circular regions of interest were placed on five slices on the left and right liver, portal vein, aorta, subcutaneous fat, and bilateral psoas muscles. CT number, noise, signal-to-noise ratios (SNRs), and contrast-to-noise ratios (CNRs) were determined. All significant differences between reconstructions were assessed via the Friedman test with post-hoc Dunn-Sidak corrections.

resultsReader agreement was fair (

conclusionThe best subjective IQ metrics for image contrast, image noise, and image sharpness were obtained with PIQE. The best objective IQ metrics (SNR and CNR) were obtained with PIQE L2. This work supports improved image contrast and decreased noise when using PIQE as compared to AiCE.

Indexed as

AlgorithmsDeep LearningImage Processing, Computer-AssistedRadiographic Image Interpretation, Computer-AssistedRadiography, AbdominalTomography, X-Ray ComputedAdultAgedContrast MediaFemaleHumansMaleMiddle AgedRetrospective StudiesSignal-To-Noise RatioContrast Mediaabdominal computed tomography deep learning image reconstructioncontrast‐to‐noisesignal‐to‐noise

Identifiers

PMID42192227
PMCPMC13239069

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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.