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Deep learning–based image reconstruction for brain CT: improved image quality compared with adaptive statistical iterative reconstruction-Veo (ASIR-V)

  • Diagnostic Neuroradiology
  • Published:
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Abstract

Purpose

To compare the image quality of brain computed tomography (CT) images reconstructed with deep learning–based image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V).

Methods

Sixty-two patients underwent routine noncontrast brain CT scans and datasets were reconstructed with 30% ASIR-V and DLIR with three selectable reconstruction strength levels (low, medium, high). Objective parameters including CT attenuation, noise, noise reduction rate, artifact index of the posterior cranial fossa, and contrast-to-noise ratio (CNR) were measured at the levels of the centrum semiovale and basal ganglia. Subjective parameters including gray matter-white matter differentiation, sharpness, and overall diagnostic quality were also assessed and compared with the interobserver agreement.

Results

There was a gradual reduction in the image noise and artifact index of the posterior cranial fossa as the strength levels of DLIR increased (all P < 0.001) compared with that of ASIR-V. CNR in both the centrum semiovale and basal ganglia levels also improved from the low to high strength levels of DLIR compared with that of ASIR-V (all P < 0.001). DLIR images with medium and high strength levels demonstrated the best subjective image quality scores among the reconstruction datasets. There was moderate to good interobserver agreement for the subjective image quality assessments with ASIR-V and DLIR.

Conclusion

On routine brain CT scans, optimized protocols with DLIR allowed significant reduction of noise and artifacts with improved subjective image quality compared with ASIR-V.

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Author contribution statement

Injoong Kim participated in material preparation, data collection and analysis, drafting the manuscript, and funding acquisition. Na-Young Shin designed the study and participated in the analysis of data and interpretation, and critical revision of the manuscript for important intellectual content. Hyunkoo Kang participated in study concept and design and critical revision of the manuscript for important intellectual content. Hyun Jung Yoon participated in study concept and design and critical revision of the manuscript for important intellectual content. Bo Mi Chung participated in study concept and design and critical revision of the manuscript for important intellectual content. All authors read and approved the final manuscript.

Funding

This study was supported by a Veterans Health Service Medical Center Research Grant, Republic of Korea (VHSMC 20057).

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Authors and Affiliations

  1. Department of Radiology, Veterans Health Service Medical Center, 53 Jinhwangdo-ro 61-gil, Gangdong-gu, Seoul, 05368, South Korea

    Injoong Kim, Hyunkoo Kang, Hyun Jung Yoon & Bo Mi Chung

  2. Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, South Korea

    Na-Young Shin

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  1. Injoong Kim
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Correspondence to Na-Young Shin.

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Kim, I., Kang, H., Yoon, H.J. et al. Deep learning–based image reconstruction for brain CT: improved image quality compared with adaptive statistical iterative reconstruction-Veo (ASIR-V). Neuroradiology 63, 905–912 (2021). https://doi.org/10.1007/s00234-020-02574-x

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