An Analysis Of Intelligent Noise Reduction Processing Applied To Pediatric Digital Radiographs

By Jonah Ice and Selena Yao

Explore the groundbreaking intersection of ionizing radiation, pediatric digital radiography dosing, and artificial intelligence (AI) through the transformative potential of Canon Intelligent Noise Reduction (Canon INR) deep learning neural network (DLNN) and its application in clinical practice. With digital projection radiography being a significant part of medical imaging, it is crucial to balance image quality and ionizing radiation exposure.

This white paper examines the impact of Canon INR on pediatric digital radiography dosing protocols and demonstrates its potential to mitigate risks associated with ionizing radiation. The study was conducted at Dayton Children’s Hospital South Campus and evaluated over a thousand radiographs across various anatomical regions. The results indicate that Canon INR enhances image quality and reduces radiation exposure substantially, which is crucial for pediatric patients known for their heightened radiosensitivity. The findings of this study suggest a paradigm shift in standard dosage guidelines that could have profound implications for pediatric healthcare.