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Deep learning-based reconstruction can improve the image quality of low radiation dose head CT

  • Computed Tomography
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To evaluate the image quality of deep learning–based reconstruction (DLR), model-based (MBIR), and hybrid iterative reconstruction (HIR) algorithms for lower-dose (LD) unenhanced head CT and compare it with those of standard-dose (STD) HIR images.

Methods

This retrospective study included 114 patients who underwent unenhanced head CT using the STD (n = 57) or LD (n = 57) protocol on a 320-row CT. STD images were reconstructed with HIR; LD images were reconstructed with HIR (LD-HIR), MBIR (LD-MBIR), and DLR (LD-DLR). The image noise, gray and white matter (GM-WM) contrast, and contrast-to-noise ratio (CNR) at the basal ganglia and posterior fossa levels were quantified. The noise magnitude, noise texture, GM-WM contrast, image sharpness, streak artifact, and subjective acceptability were independently scored by three radiologists (1 = worst, 5 = best). The lesion conspicuity of LD-HIR, LD-MBIR, and LD-DLR was ranked through side-by-side assessments (1 = worst, 3 = best). Reconstruction times of three algorithms were measured.

Results

The effective dose of LD was 25% lower than that of STD. Lower image noise, higher GM-WM contrast, and higher CNR were observed in LD-DLR and LD-MBIR than those in STD (all, p ≤ 0.035). Compared with STD, the noise texture, image sharpness, and subjective acceptability were inferior for LD-MBIR and superior for LD-DLR (all, p < 0.001). The lesion conspicuity of LD-DLR (2.9 ± 0.2) was higher than that of HIR (1.2 ± 0.3) and MBIR (1.8 ± 0.4) (all, p < 0.001). Reconstruction times of HIR, MBIR, and DLR were 11 ± 1, 319 ± 17, and 24 ± 1 s, respectively.

Conclusion

DLR can enhance the image quality of head CT while preserving low radiation dose level and short reconstruction time.

Key Points

• For unenhanced head CT, DLR reduced the image noise and improved the GM-WM contrast and lesion delineation without sacrificing the natural noise texture and image sharpness relative to HIR.

• The subjective and objective image quality of DLR was better than that of HIR even at 25% reduced dose without considerably increasing the image reconstruction times (24 s vs. 11 s).

• Despite the strong noise reduction and improved GM-WM contrast performance, MBIR degraded the noise texture, sharpness, and subjective acceptance with prolonged reconstruction times relative to HIR, potentially hampering its feasibility.

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Abbreviations

AiCE:

Advanced Intelligent Clear-IQ Engine

CNR:

Contrast-to-noise ratio

DLR:

Deep learning–based reconstruction

GM:

Gray matter

HIR:

Hybrid iterative reconstruction

HU:

Hounsfield unit

MBIR:

Model-based iterative reconstruction

NPS:

Noise power spectrum

WM:

White matter

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Funding

The authors state that the study was supported by a grant from the Japan Society for the Promotion of Science KAKENHI (Grant Number 19K17173).

Author information

Authors and Affiliations

  1. Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan

    Yasunori Nagayama, Koya Iwashita, Natsuki Maruyama, Hiroyuki Uetani, Yuki Kato, Sentaro Takada, Masafumi Kidoh, Seitaro Oda, Takeshi Nakaura & Toshinori Hirai

  2. Department of Central Radiology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan

    Makoto Goto, Daisuke Sakabe, Takafumi Emoto, Kengo Nakato, Shinsuke Shigematsu & Masahiro Hatemura

  3. Department of Neurology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan

    Mitsuharu Ueda

  4. Department of Neurosurgery, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan

    Akitake Mukasa

Authors
  1. Yasunori Nagayama
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Corresponding author

Correspondence to Yasunori Nagayama.

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Guarantor

The scientific guarantor of this publication is Toshinori Hirai.

Conflict of interest

Toshinori Hirai and Koya Iwashita have received research support from Canon Medical Systems. The Canon Medical Systems had no control over the interpretation, writing, or publication of this work.

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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Institutional review board approval was obtained.

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Nagayama, Y., Iwashita, K., Maruyama, N. et al. Deep learning-based reconstruction can improve the image quality of low radiation dose head CT. Eur Radiol 33, 3253–3265 (2023). https://doi.org/10.1007/s00330-023-09559-3

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  • DOI: https://doi.org/10.1007/s00330-023-09559-3

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