Evidence map›Paper›PMID 42043547›Full record

ArticleClinical neuroradiology2026

Deep Learning Reconstruction Versus Hybrid Iterative Reconstruction for Acute Cerebral Infarction Detection on 135 kVp Non-Contrast Brain CT.

Hirofumi Sekino, Shiro Ishii, Tatsuya Ando, Yumi Saito, Anna Yamaki, Ryo Yamakuni, Kenji Fukushima, Hiroshi Ito

Abstract readComparative Study
PubMed Publisher
In one paragraph

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

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Hirofumi SekinoFukushima Medical University, Fukushima, Japan. sekino@fmu.ac.jp.
Shiro IshiiFukushima Medical University, Fukushima, Japan.
Tatsuya AndoFukushima Medical University, Fukushima, Japan.
Yumi SaitoFukushima Medical University, Fukushima, Japan.
Anna YamakiFukushima Medical University, Fukushima, Japan.
Ryo YamakuniFukushima Medical University, Fukushima, Japan.
Kenji FukushimaFukushima Medical University, Fukushima, Japan.
Hiroshi ItoFukushima Medical University, Fukushima, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeDeep learning reconstruction (DLR) is useful to reduce image noise and improve contrast resolution compared with hybrid iterative reconstruction (Hybrid IR). This study compared image quality and infarct detection between DLR and Hybrid IR using thin-slice brain CT. MATERIALS AND

methodsEighty-one patients (39 with acute infarction, 42 without) underwent 135 kVp non-contrast brain CT and MRI within 24 h of admission. CT images (2-mm thickness) were reconstructed using both Hybrid IR and DLR. Image noise was measured in white matter, gray matter, and infarct lesions. Three general radiologists independently assessed infarct presence. Sensitivity was evaluated using patient- and region-based analyses.

resultsDLR demonstrated significantly lower image noise than Hybrid IR in white matter, gray matter, and infarct lesions (1.69 vs. 4.40, 1.43 vs. 3.93, and 1.68 vs. 3.94 HU, respectively; all p < 0.001). Contrast-to-noise ratio was significantly higher with DLR (5.10 vs. 2.36, p < 0.001). In patient-based analysis, infarct detection sensitivity was higher with DLR (66.7%-71.8%) than with Hybrid IR (59.0%-69.2%) (p > 0.05). In region-based analysis, DLR showed significantly higher sensitivity for one reader (60.5% vs. 50.0%, p = 0.004).

conclusionIn this study, DLR significantly reduces image noise and improves contrast-to-noise ratio in thin-slice brain CT. These improvements may help general radiologists in diagnosing acute cerebral infarction.

Indexed as

Cerebral InfarctionDeep LearningRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAgedAged, 80 and overFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedSensitivity and SpecificityComputed tomographyDeep learning reconstructionDiagnostic sensitivityHybrid iterative reconstructionStroke lesion

Identifiers

PMID42043547

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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.