Optimizing the CT acquisition parameters to obtain diagnostic image quality at the lowest
possible radiation dose is crucial in the radiosensitive pediatric population. The image …

Please see the Editorial Comment by Aaron D. Hodes discussing this article.
BACKGROUND. Deep learning–based reconstruction (DLR) may facilitate CT radiation …

Objectives To evaluate the image quality of deep learning–based reconstruction (DLR),
model-based (MBIR), and hybrid iterative reconstruction (HIR) algorithms for lower-dose …

Background To evaluate the performance of a Deep Learning Image Reconstruction (DLIR)
algorithm in pediatric head CT for improving image quality and lesion detection with 0.625 …

Purpose To compare the image quality between a deep learning–based image
reconstruction algorithm (DLIR) and an adaptive statistical iterative reconstruction algorithm …

When evaluating a child with a potential neurologic or neurodevelopmental disorder,
identifying indications for imaging and the correct imaging modality to order can be …

ABSTRACT BACKGROUND AND PURPOSE Iterative model reconstruction (IMR) has
shown to improve computed tomography (CT) image quality compared to hybrid iterative …

The proposed algorithm of inverse problem of computed tomography (CT), using limited
views, is based on stochastic techniques, namely simulated annealing (SA). The selection of …

Purpose To evaluate the relationship between patient age and radiation doses associated
with routine pediatric head CT performed with automatic tube potential selection and tube …

The combination of low dose CT and AI performance in the pediatric population has not
been explored. Understanding this relationship is relevant for pediatric patients given the …