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Ultra-low-dose computed tomography with deep learning reconstruction for craniosynostosis at radiation doses comparable to skull radiographs: a pilot study

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Abstract

Background

Craniofacial computed tomography (CT) is the diagnostic investigation of choice for craniosynostosis, but high radiation dose remains a concern.

Objective

To evaluate the image quality and diagnostic performance of an ultra-low-dose craniofacial CT protocol with deep learning reconstruction for diagnosis of craniosynostosis.

Materials and methods

All children who underwent initial craniofacial CT for suspected craniosynostosis between September 2021 and September 2022 were included in the study. The ultra-low-dose craniofacial CT protocol using 70 kVp, model-based iterative reconstruction and deep learning reconstruction techniques was compared with a routine-dose craniofacial CT protocol. Quantitative analysis of the signal-to-noise ratio and noise was performed. The 3-dimensional (D) volume-rendered images were independently evaluated by two radiologists with regard to surface coarseness, step-off artifacts and overall image quality on a 5-point scale. Sutural patency was assessed for each of six sutures. Radiation dose was compared between the two protocols.

Results

Among 29 patients (15 routine-dose CT and 14 ultra-low-dose CT), 23 patients had craniosynostosis. The 3-D volume-rendered images of ultra-low-dose CT without deep learning showed decreased image quality compared to routine-dose CT. The 3-D volume-rendered images of ultra-low-dose CT with deep learning reconstruction showed higher noise level, higher surface coarseness but decreased step-off artifacts, comparable signal-to-noise ratio and overall similar image quality compared to the routine-dose CT images. Diagnostic performance for detecting craniosynostosis at the suture level showed no significant difference between ultra-low-dose CT without deep learning reconstruction, ultra-low-dose CT with deep learning reconstruction and routine-dose CT. The estimated effective radiation dose for the ultra-low-dose CT was 0.05 mSv (range, 0.03–0.06 mSv), a 95% reduction in dose over the routine-dose CT at 1.15 mSv (range, 0.54–1.74 mSv). This radiation dose is comparable to 4-view skull radiography (0.05–0.1 mSv) and lower than previously reported effective dose for craniosynostosis protocols (0.08–3.36 mSv).

Conclusion

In this pilot study, an ultra-low-dose CT protocol using radiation doses at a level similar to skull radiographs showed preserved diagnostic performance for craniosynostosis, but decreased image quality compared to the routine-dose CT protocol. However, by combining the ultra-low-dose CT protocol with deep learning reconstruction, image quality was improved to a level comparable to the routine-dose CT protocol, without sacrificing diagnostic performance for craniosynostosis.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors thank Chang Won Kim for his technical support.

Funding

This research 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: HI20C2092).

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Authors and Affiliations

  1. Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea

    Youngwook Lyoo, Young Hun Choi, Seul Bi Lee, Seunghyun Lee, Yeon Jin Cho & Jung-Eun Cheon

  2. Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea

    Youngwook Lyoo, Young Hun Choi, Seul Bi Lee, Seunghyun Lee & Jung-Eun Cheon

  3. Department of Radiology, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea

    Su-Mi Shin

  4. Department of Pediatric Neurosurgery, Seoul National University Hospital, Seoul, Republic of Korea

    Ji Hoon Phi & Seung Ki Kim

  5. Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea

    Jung-Eun Cheon

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Contributions

Y.H.C. designed and directed the project. Y.L., Y.H.C., S.L. and Y.J.C. interpreted the images. Y.L. analyzed the results and wrote the manuscript with supervision from Y.H.C. All authors provided critical feedback and helped shape the research, analysis and manuscript. All authors approved the final version of the manuscript. 

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Correspondence to Young Hun Choi.

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Lyoo, Y., Choi, Y.H., Lee, S.B. et al. Ultra-low-dose computed tomography with deep learning reconstruction for craniosynostosis at radiation doses comparable to skull radiographs: a pilot study. Pediatr Radiol 53, 2260–2268 (2023). https://doi.org/10.1007/s00247-023-05717-3

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