Evidence map›Paper›PMID 41407995›Full record

ArticleJapanese journal of radiology2026

Accelerating imaging: deep learning for enhanced

Yoshinobu Ishiwata, Keiichi Horie, Kazuhiro Aritome, Ryo Aoki, Hitoshi Iizuka, Shinjiro Aso, Yuka Takeuchi, Yuka Misumi, Akira Haga, Shingo Kato and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Japanese journal of radiology, 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

13 authors.

Yoshinobu Ishiwata *Department of Radiology, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-Ward, Yokohama, 2360004, Japan. ishi_y@yokohama-cu.ac.jp.ORCID http://orcid.org/0000-0002-9425-0582
Keiichi Horie *Department of Radiology, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-Ward, Yokohama, 2360004, Japan.
Kazuhiro AritomeDepartment of Data Science, Yokohama City University, 2-22 Seto, Kanazawa-Ward, Yokohama, 2360027, Japan.
Ryo AokiDepartment of Radiology, Yokohama City University Medical Center, 4-57 Urafune, Minami-Ward, Yokohama, 2320024, Japan.
Hitoshi IizukaDepartment of Radiology, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-Ward, Yokohama, 2360004, Japan.
Shinjiro AsoDepartment of Radiology, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-Ward, Yokohama, 2360004, Japan.
Yuka TakeuchiDepartment of Radiology, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-Ward, Yokohama, 2360004, Japan.
Yuka MisumiDepartment of Radiology, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-Ward, Yokohama, 2360004, Japan.
Akira HagaDepartment of Radiology, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-Ward, Yokohama, 2360004, Japan.
Shingo KatoDepartment of Radiology, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-Ward, Yokohama, 2360004, Japan.
Tsuneo YamashiroDepartment of Radiology, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-Ward, Yokohama, 2360004, Japan.
Shoko TakanoDepartment of Nuclear Medicine, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-Ward, Yokohama, 2360004, Japan.
Daisuke UtsunomiyaDepartment of Radiology, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-Ward, Yokohama, 2360004, Japan.

Funding

JSPS KAKENHI 20K16705
6 · The paper itself

Abstract

backgroundConventional

methodsWe retrospectively analysed 207 studies (1035 slices) obtained between April 2018 and June 2020. After cropping to 64 × 64 striatal regions, 600, 185 and 250 images from 120, 37 and 50 patients were used for training, validation and testing. Six convolutional architectures-U-Net (one-five depths), V-Net, U-Net +  + , R2U-Net, Attention U-Net and TransUNet-were trained to translate 5-min into virtual 25-min images. Image quality was assessed with peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), analysed by Friedman and Dunn-Holm tests. A blinded reader study involved three nuclear medicine physicians grading 50 cases (100 striata) on a four-point scale; agreement with the 25-min consensus was measured by weighted κ and intra-/inter-observer intraclass correlation coefficients (ICC).

resultsAll DL reconstructions significantly outperformed raw 5-min images in PSNR and SSIM (p < 0.01). The four-layer U-Net achieved the highest quality (PSNR 32.7 ± 1.7 dB, SSIM 0.842 ± 0.069), ≈1.8 dB and 0.13 higher than baseline, and statistically indistinguishable from 25-min images (p > 0.05). Reader concordance improved from fair with baseline (κ = 0.29-0.41) to substantial with the four-layer U-Net (κ = 0.62-0.70); intra-reader ICC was 0.84-0.93 and inter-reader ICC 0.73-0.75.

conclusionsA compact four-layer U-Net restores diagnostic fidelity to 5-min

Indexed as

Deep LearningImage Processing, Computer-AssistedIodine RadioisotopesNortropanesTomography, Emission-Computed, Single-PhotonAdultAgedAged, 80 and overBrainFemaleHumansMaleMiddle AgedRadiopharmaceuticalsRetrospective StudiesSignal-To-Noise RatioIodine RadioisotopesioflupaneNortropanesRadiopharmaceuticalsDeep learningDopamine transporter imagingParkinsonismScan time reductionSingle-photon emission computed tomography (SPECT)

Identifiers

PMID41407995
PMCPMC13144268

What OpenQuestion holds

Textmetadata
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.