Evidence map›Paper›PMID 41091157›Full record

ArticleNeuroradiology2025

Deep learning-based computed tomography reconstruction improves image quality but does not significantly affect Alberta stroke program early CT score evaluation in acute middle cerebral artery territory infarction.

Motoko Konno, Takahiro Otani, Masazumi Matsuda, Takuya Konno, Kento Hatakeyama, Aoi Otaka, Yusuke Takahashi, Hiroaki Shimizu, Naoko Mori

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Article in Neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Motoko KonnoAkita University, Akita, Japan. motoko@med.akita-u.ac.jp.
Takahiro OtaniAkita University, Akita, Japan.
Masazumi MatsudaAkita University, Akita, Japan.
Takuya KonnoAkita University, Akita, Japan.
Kento HatakeyamaAkita University, Akita, Japan.
Aoi OtakaYuri Union General Hospital, Yurihonjō, Japan.
Yusuke TakahashiAkita University, Akita, Japan.
Hiroaki ShimizuAkita University, Akita, Japan.
Naoko MoriAkita University, Akita, Japan.

Funding

Japan Society for the Promotion of Science 24K18820
6 · The paper itself

Abstract

purposeTo evaluate whether deep learning image reconstruction (DLIR), a deep learning-based computed tomography (CT) reconstruction method, improves image quality, Alberta Stroke Program Early CT Score (ASPECTS), and the detectability of early ischemic changes (EIC) in acute middle cerebral artery (MCA) territory infarction compared to filtered back projection (FBP) and Iterative Reconstruction (IR).

methodsThis retrospective study included 30 patients with confirmed acute MCA infarction who underwent non-contrast brain CT and follow-up diffusion-weighted imaging (DWI). CT images reconstructed with FBP, IR, and DLIR were assessed for image quality using quantitative metrics (signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR]) and qualitative evaluations of visibility and noise. ASPECTS was calculated for each reconstruction method, and diagnostic performance metrics, including sensitivity, specificity, and accuracy, were analyzed using DWI ASPECTS as the reference.

resultsDLIR demonstrated significantly reduced image noise and superior SNR compared to FBP and IR (p < 0.0001). The CNR of DLIR was significantly higher than that of FBP (p = 0.0036), but no significant difference was observed compared to IR (p = 0.4238). Qualitative assessments indicated improved visibility and noise with DLIR. However, there were no significant differences in ASPECTS scores or the supplementary diagnostic performance metrics (sensitivity, specificity, accuracy) in predicting acute MCA territory infarction between DLIR and FBP, and between DLIR and IR.

conclusionDLIR improved image quality in thin-slice CT for acute MCA territory infarction but did not lead to significant differences in ASPECTS evaluation or the detection of EIC compared to FBP and IR.

Indexed as

Deep LearningInfarction, Middle Cerebral ArteryRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAgedAged, 80 and overDiffusion Magnetic Resonance ImagingFemaleHumansMaleMiddle AgedRetrospective StudiesSensitivity and Specificity

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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.