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Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques

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A Correction to this article was published on 15 February 2021

This article has been updated

Abstract

Objective

To compare the image quality between the vendor-agnostic and vendor-specific algorithms on ultralow-dose chest CT.

Methods

Vendor-agnostic deep learning post-processing model (DLM), vendor-specific deep learning image reconstruction (DLIR, high level), and adaptive statistical iterative reconstruction (ASiR, 70%) algorithms were employed. One hundred consecutive ultralow-dose noncontrast CT scans (CTDIvol; mean, 0.33 ± 0.056 mGy) were reconstructed with five algorithms: DLM-stnd (standard kernel), DLM-shrp (sharp kernel), DLIR, ASiR-stnd, and ASiR-shrp. Three thoracic radiologists blinded to the reconstruction algorithms reviewed five sets of 100 images and assessed subjective noise, spatial resolution, distortion artifact, and overall image quality. They selected the most preferred algorithm among five image sets for each case. Image noise and signal-to-noise ratio were measured. Edge-rise-distance was measured at a pulmonary vessel, i.e., the distance between two points where attenuation was 10% and 90% of maximal intravascular intensity. The skewness of attenuation was calculated in homogeneous areas.

Results

DLM-stnd, followed by DLIR, showed the best subjective noise on both lung and mediastinal windows, while DLIR yielded the least measured noise (ps < .0001). Compared to DLM-stnd, DLIR showed inferior subjective spatial resolution on lung window and higher edge-rise-distance (ps < .0001). Additionally, DLIR showed the most frequent distortion artifacts and deviated skewness (ps < .0001). DLM-stnd scored the best overall image quality, followed by DLM-shrp and DLIR (mean score 3.89 ± 0.19, 3.68 ± 0.24, and 3.53 ± 0.33; ps < .001). Two among three readers preferred DLM-stnd on both windows.

Conclusion

Although DLIR provided the best quantitative noise profile, DLM-stnd showed the best overall image quality with fewer artifacts and was preferred by two among three readers.

Key Points

• A vendor-agnostic deep learning post-processing algorithm applied to ultralow-dose chest CT exhibited the best image quality compared to vendor-specific deep learning algorithm and ASiR techniques.

• Two out of three readers preferred a vendor-agnostic deep learning post-processing algorithm in comparison to vendor-specific deep learning algorithm and ASiR techniques.

• A vendor-specific deep learning reconstruction algorithm yielded the least image noise, but showed significantly more frequent specific distortion artifacts and increased skewness of attenuation compared to a vendor-agnostic algorithm.

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Abbreviations

ASiR:

Adaptive statistical iterative reconstruction

CNN:

Convolutional neural network

DLIR:

A vendor-specific deep learning image reconstruction

DLM:

A vendor-agnostic deep learning model

ERD:

Edge-rise-distance

FBP:

Filtered back projection

ICC:

Intraclass correlation coefficients

ROI:

Region-of-interest

shrp:

Sharp kernel

SNR:

Signal-to-noise ratio

stnd:

Standard kernel

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Funding

This work was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI19C1129).

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Authors and Affiliations

  1. Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea

    Ju Gang Nam, Hyewon Choi, Wonju Hong, Jongsoo Park, Jong Hyo Kim & Jin Mo Goo

  2. Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826, Republic of Korea

    Chulkyun Ahn & Jong Hyo Kim

  3. Center for Medical-IT Convergence Technology Research, Advanced Institutes of Convergence Technology, Suwon, 16229, Republic of Korea

    Jong Hyo Kim

  4. Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea

    Jin Mo Goo

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  1. Ju Gang Nam
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Correspondence to Jin Mo Goo.

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The scientific guarantor of this publication is Jin Mo Goo.

Conflict of interest

One author (J.H.K.) was a stockholder of ClariPI, but did not have control over any of the data or information submitted for publication.

Other authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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• retrospective

• observational study performed at one institution

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The original online version of this article was revised: The information that Ju Gang Nam and Chulkyun Ahn contributed equally to this work was missing.

Ju Gang Nam and Chulkyun Ahn contributed equally to this work.

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Nam, J.G., Ahn, C., Choi, H. et al. Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques. Eur Radiol 31, 5139–5147 (2021). https://doi.org/10.1007/s00330-020-07537-7

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