Pediatric radiology · 2023
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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Lyoo Youngwook, Choi Young Hun, Lee Seul Bi, Lee Seunghyun, Cho Yeon Jin, Shin Su-Mi, Phi Ji Hoon, Kim Seung Ki, Cheon Jung-Eun
Аннотация
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.
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.