Radiation dose reduction in pediatric computed tomography (CT) using deep convolutional neural network denoising.

Aim: We evaluated the quality of noncontrast chest computed tomography (CT) for pediatric patients at two dose levels with and without denoising using a deep convolutional neural network (CNN).

Materials And Methods: Forty children underwent noncontrast chest CTs for "chronic cough" using a routine dose (RD) protocol. Images were reconstructed using iterative reconstruction (IR). A validated noise insertion method was used to simulate 20% dose (TD) data for each case. A deep CNN model was trained and validated on 10 cases and then applied to the remaining 30 cases. Three certificate of qualification (CAQ)-certified pediatric radiologists evaluated 30 cases under 4 conditions: (1) RD + IR; (2) RD + CNN; (3) TD + IR; and (4) TD + CNN. Likert scales were used to score subjective image quality (1-5, 5 = excellent) and subjective noise artifact (1-4, 4 = no noise). Images were reviewed for specific findings.

Results: For the 30 patients evaluated (14 female, mean age: 10.8 years, range: 0.17-17), the mean effective dose was 0.46 ± 0.21 mSv for the original RD exam, with an effective dose of 0.09 mSv for the TD exam. Both RD + CNN (3.6 ± 1.1, p < 0.001) and TD + CNN (3.4 ± 0.9, p = 0.023) had higher image quality than RD + IR (3.1 ± 0.9). Both RD + CNN (3.2 ± 0.9, p-value = <0.001) and TD + CNN (2.9 ± 0.6, p-value = 0.001) showed significantly lower subjective noise artifact scores than RD + IR (2.7 ± 0.7). There was excellent intrareader (RD + IR-RD + CNN: mean κ = 0.96, RD + IR-TD + CNN = 0.96, RD + IR-TD + IR = 0.98) and moderate inter-reader reliability (RD + IR: mean κ = 0.55, RD + CNN = 0.50, TD + CNN = 0.54, TD + IR = 0.57) on all 4 image reconstructions.

Conclusion: CNN denoising outperforms IR as a means of radiation dose reduction in pediatric CT.