Deep learning-based reconstructions can reduce pediatric CT radiation doses while preserving, or even improving, image quality at lower tube voltages, researchers recently shared in the American Journal of Roentgenology. 

The current standard for noise reduction on CT scans is iterative reconstruction (IR). Two commonly utilized techniques of IR are hybrid IR (HIR) and model-based IR (MBIR), both of which are used to achieve the same goal of noise reduction. However, both techniques have drawbacks. 

“Both HIR and MBIR have limited ability to preserve noise texture, spatial resolution, and low-contrast object detectability at considerably reduced doses,” Yasunori Nagayama, MD, from the department of diagnostic radiology and graduate school of medical sciences at Kumamoto University, and co-authors explained. “MBIR also has long reconstruction times and results in an altered image appearance (specifically, an unnatural, blurred, pixelated appearance), hampering clinical utility relative to HIR.” 

Previous studies have touted the benefits of deep learning-based reconstruction (DLR) for dose reduction, but limited research is available on its value in the context of pediatric CT or in low-tube voltage scanning. 

To better understand how DLR algorithms could improve radiation doses and image quality in a pediatric setting, researchers compared DLR, HIR and MBIR techniques on the CT scans of 65 pediatric patients (6-years-old and younger). Scans were obtained using lower-dose (80 kVp) and standard-dose (120 kVp) protocols and size-specific dose estimates (SSDE) were calculated for both.