New image quality and dose reduction technique for pediatric digital radiography

Pediatric Pelvis

The development of the pelvis follows a predictable pattern of ossification that involves the maturation of bone, synchondroses, and apophyses. These growth centers appear and close at distinct times during skeletal maturity and give rise to structural changes in the pelvis that can be distinctively appreciated on various imaging modalities. Accurate interpretation of radiologic images requires knowledge of skeletal development because the varying appearance of the maturing pediatric pelvis may be mistaken for pathology. In addition, many normal features within the pelvis can be erroneously perceived as injury. This article incorporates a multimodality review of normal pelvic maturation, a discussion of developmental variants, and a description of common injuries unique to the pediatric pelvis.

Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

Radiation dose optimization is particularly important in pediatric radiology, as children are more susceptible to potential harmful effects of ionizing radiation. However, only one narrative review about artificial intelligence (AI) for dose optimization in pediatric computed tomography (CT) has been published yet. The purpose of this systematic review is to answer the question “What are the AI techniques and architectures introduced in pediatric radiology for dose optimization, their specific application areas, and performances?” Literature search with use of electronic databases was conducted on 3 June 2022. Sixteen articles that met selection criteria were included. The included studies showed deep convolutional neural network (CNN) was the most common AI technique and architecture used for dose optimization in pediatric radiology. All but three included studies evaluated AI performance in dose optimization of abdomen, chest, head, neck, and pelvis CT; CT angiography; and dual-energy CT through deep learning image reconstruction. Most studies demonstrated that AI could reduce radiation dose by 36–70% without losing diagnostic information. Despite the dominance of commercially available AI models based on deep CNN with promising outcomes, homegrown models could provide comparable performances. Future exploration of AI value for dose optimization in pediatric radiology is necessary due to small sample sizes and narrow scopes (only three modalities, CT, positron emission tomography/magnetic resonance imaging and mobile radiography, and not all examination types covered) of existing studies.