Evidence map›Paper›PMID 41444395›Full record

ArticleEuropean radiology2026

Conjugate gradient and deep learning reconstructions: reduced time without affecting image quality and nodule detection.

Yoshiharu Ohno, Yoshiyuki Ozawa, Takahiro Ueda, Masahiko Nomura, Natsuka Yazawa, Maiko Shinohara, Kaori Yamamoto, Yuichiro Sano, Masato Ikedo, Masanori Ozaki and 6 more

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Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

16 authors.

Yoshiharu OhnoDepartment of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Japan. yohno@fujita-hu.ac.jp.ORCID http://orcid.org/0000-0002-4431-1084
Yoshiyuki OzawaDepartment of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Japan.
Takahiro UedaDepartment of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Japan.
Masahiko NomuraDepartment of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Japan.
Natsuka YazawaCanon Medical Systems Corporation, Otawara, Japan.
Maiko ShinoharaCanon Medical Systems Corporation, Otawara, Japan.
Kaori YamamotoCanon Medical Systems Corporation, Otawara, Japan.
Yuichiro SanoCanon Medical Systems Corporation, Otawara, Japan.
Masato IkedoCanon Medical Systems Corporation, Otawara, Japan.
Masanori OzakiCanon Medical Systems Corporation, Otawara, Japan.
Masao YuiCanon Medical Systems Corporation, Otawara, Japan.
Shohei HaradaDepartment of Radiology, Fujita Health University Hospital, Toyoake, Japan.
Saki TakedaDepartment of Radiology, Fujita Health University Hospital, Toyoake, Japan.
Akiyoshi IwaseDepartment of Radiology, Fujita Health University Hospital, Toyoake, Japan.
Takeshi YoshikawaDepartment of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Japan.
Daisuke TakenakaDepartment of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe purpose of this study was to determine the utility of conjugate gradient reconstruction (CG Recon) and deep learning reconstruction (DLR) for reducing scan time while maintaining the image quality and nodule detection capability on lung MRI with ultrashort TE (UTE-MRI) as compared with grid reconstruction (Grid Recon). MATERIALS AND

methodsIn the in vitro and in vivo studies, the NEMA phantom and 35 patients with pulmonary nodules were scanned by UTE-MRI with original (TE

resultsIn in vitro and in vivo studies, all SNRs were significantly different between the standard protocol and each UTE-MRI with CG Recon and DLR (p < 0.05). Overall image quality of the standard protocol differed significantly from that of all UTE

conclusionCG Recon and DLR can reduce scan time while maintaining image quality and nodule detection capabilities on lung UTE-MRI. KEY POINTS: Question To determine the utility of conjugate gradient reconstruction (CG Recon) to reduce scan time without nodule detection capability on MRI with ultrashort TE (UTE-MRI). Findings Nodule detection capability was not significantly decreased by CG Recon with or without deep learning reconstruction when reducing scan time from the standard UTE-MRI protocol. Clinical relevance Conjugate gradient reconstruction (CG Recon) and deep learning reconstruction (DLR) have the potential to reduce scan time while maintaining image quality and nodule detection capability in lung MR imaging with ultrashort TE.

Indexed as

Deep LearningImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedLung NeoplasmsMagnetic Resonance ImagingSolitary Pulmonary NoduleAdultAgedFemaleHumansMaleMiddle AgedPhantoms, ImagingSignal-To-Noise RatioArtificial intelligenceImage processing (computer-assisted)LungMagnetic resonance imagingSolitary pulmonary nodule

Identifiers

PMID41444395

What OpenQuestion holds

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Registered trials

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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.