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European Radiology

Published in:

09-06-2022 | Computed Tomography | Computed Tomography

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

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

Published in: European Radiology | Issue 12/2022

Abstract

Objectives

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

Methods

A 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.

Results

Compared 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.

Conclusions

The 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.

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Title: Clinical validation of an AI-based motion correction reconstruction algorithm in cerebral CT
Authors: Leilei Zhou
Hao Liu
Yi-Xuan Zou
Guozhi Zhang
Bin Su
Liyan Lu
Yu-Chen Chen
Xindao Yin
Hong-Bing Jiang
Publication date: 09-06-2022
Publisher: Springer Berlin Heidelberg
Keywords: Computed Tomography
Computed Tomography
Artificial Intelligence
Published in: European Radiology / Issue 12/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-022-08883-4

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

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

Abstract

Objectives

Methods

Results

Conclusions

Key Points

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

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