Improving image quality on pediatric and neonatal radiography using AI-based compensation for image degradation.

Purpose: To evaluate the impact of an AI-based, noise reduction technique for compensation of image degradation on pediatric and neonatal chest and abdomen radiography using a visual grading analysis.

Materials and methods: Forty-six consecutive cases of pediatric and neonatal chest X-rays were identified for the quality evaluation. The images underwent AI-based noise reduction processing (Intelligent NR, Canon Inc.). All the images were randomized, and were evaluated from 1 to 4 for image quality by three board-certified radiologists in consensus. A score of "1" indicated the desired anatomy or features were not seen, "2" indicated quality between one and three, "3" indicated adequate quality, and "4" indicated higher than required image quality. A Wilcoxon signed rank test was used to assess the significant difference between images from conventional noise reduction versus those from the AI-based noise reduction.

Results: The images processed with the INR(Intelligent NR) noise reduction had a higher image quality than the conventionally processed images, with a significant difference between the two groups (p < 0.05).

Conclusion: The AI-based noise reduction technique improved the image quality of pediatric and neonatal chest and abdominal radiography significantly.