Evidence map›Paper›PMID 41591139›Full record

ArticleTomography (Ann Arbor, Mich.)2026

Super-Resolution Deep Learning Reconstruction Improves Image Quality of Dynamic Myocardial Computed Tomography Perfusion Imaging.

Yusuke Kobayashi, Yuki Tanabe, Tomoro Morikawa, Kazuki Yoshida, Kentaro Ohara, Takaaki Hosokawa, Takanori Kouchi, Shota Nakano, Osamu Yamaguchi, Teruhito Kido

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Tomography (Ann Arbor, Mich.), 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
–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.

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

10 authors.

Yusuke KobayashiDepartment of Radiology, Graduate School of Medicine, Ehime University, Toon 791-0295, Japan.ORCID 0009-0006-1047-9213
Yuki TanabeDepartment of Radiology, Graduate School of Medicine, Ehime University, Toon 791-0295, Japan.ORCID 0000-0001-6782-888X
Tomoro MorikawaDepartment of Radiology, Graduate School of Medicine, Ehime University, Toon 791-0295, Japan.
Kazuki YoshidaDepartment of Radiology, Graduate School of Medicine, Ehime University, Toon 791-0295, Japan.
Kentaro OharaDepartment of Radiology, Graduate School of Medicine, Ehime University, Toon 791-0295, Japan.ORCID 0009-0007-1680-7799
Takaaki HosokawaDepartment of Radiology, Ehime Prefectural Imabari Hospital, Imabari 794-0006, Japan.ORCID 0000-0001-9557-5189
Takanori KouchiDepartment of Radiology, Graduate School of Medicine, Ehime University, Toon 791-0295, Japan.
Shota NakanoCanon Medical Systems Corporation, Otawara 324-8550, Japan.ORCID 0009-0002-3015-0168
Osamu YamaguchiDepartment of Cardiology, Pulmonology, Hypertension, and Nephrology, Graduate School of Medicine, Ehime University, Toon 791-0295, Japan.
Teruhito KidoDepartment of Radiology, Graduate School of Medicine, Ehime University, Toon 791-0295, Japan.ORCID 0000-0002-7645-4311

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Coronary Artery DiseaseDeep LearningImage Processing, Computer-AssistedMyocardial Perfusion ImagingRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAgedFemaleHumansMaleMiddle AgedRetrospective StudiesSignal-To-Noise Ratiocomputed tomographydeep learningmyocardial blood flowmyocardial perfusion imagingsuper resolutionvariability

Identifiers

PMID41591139
PMCPMC12845698

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.