Evidence map›Paper›PMID 35678857›Full record

ArticleEuropean radiology2022

Clinical validation of an AI-based motion correction reconstruction algorithm in cerebral CT.

Leilei Zhou, Hao Liu, Yi-Xuan Zou, Guozhi Zhang, Bin Su, Liyan Lu, Yu-Chen Chen, Xindao Yin, Hong-Bing Jiang

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In one paragraph

Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  4. Clinical applications of portable CT scanners.The British journal of radiology · 2025
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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

9 authors.

Leilei Zhou *Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China.
Hao Liu *Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China.
Yi-Xuan ZouUnited Imaging Healthcare Co., Ltd., Shanghai, China.
Guozhi ZhangUnited Imaging Healthcare Co., Ltd., Shanghai, China.
Bin SuUnited Imaging Healthcare Co., Ltd., Shanghai, China.
Liyan LuDepartment of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China.
Yu-Chen ChenDepartment of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China. chenyuchen1989@126.com.ORCID http://orcid.org/0000-0002-8539-7224
Xindao YinDepartment of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China.
Hong-Bing JiangDepartment of Medical Equipment, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China. cmdjhb@126.com.

Funding

Jiangsu Provincial Special Program of Medical Science BE2021604
6 · The paper itself

Abstract

objectivesTo evaluate the clinical performance of an artificial intelligence (AI)-based motion correction (MC) reconstruction algorithm for cerebral CT.

methodsA total of 53 cases, where motion artifacts were found in the first scan so that an immediate rescan was taken, were retrospectively enrolled. While the rescanned images were reconstructed with a hybrid iterative reconstruction (IR) algorithm (reference group), images of the first scan were reconstructed with both the hybrid IR (motion group) and the MC algorithm (MC group). Image quality was compared in terms of standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mutual information (MI), as well as subjective scores. The diagnostic performance for each case was evaluated accordingly by lesion detectability or the Alberta Stroke Program Early CT Score (ASPECTS) assessment.

resultsCompared with the motion group, the SNR and CNR of the MC group were significantly increased. The MSE, PSNR, SSIM, and MI with respect to the reference group were improved by 44.1%, 15.8%, 7.4%, and 18.3%, respectively (all p < 0.001). Subjective image quality indicators were scored higher for the MC than the motion group (p < 0.05). Improved lesion detectability and higher AUC (0.817 vs 0.614) in the ASPECTS assessment were found for the MC to the motion group.

conclusionsThe AI-based MC reconstruction algorithm has been clinically validated for reducing motion artifacts and improving diagnostic performance of cerebral CT. KEY POINTS: • An artificial intelligence-based motion correction (MC) reconstruction algorithm has been clinically validated in both qualitative and quantitative manner. • The MC algorithm reduces motion artifacts in cerebral CT and increases the diagnostic confidence for brain lesions. • The MC algorithm can help avoiding rescans caused by motion and improving the efficiency of cerebral CT in the emergency department.

Indexed as

Artificial IntelligenceRadiographic Image Interpretation, Computer-AssistedAlgorithmsHumansRadiation DosageRetrospective StudiesSignal-To-Noise RatioTomography, X-Ray ComputedArtifactsArtificial intelligenceBrainMotionTomography, X-ray computed

Identifiers

PMID35678857

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