A review on personalized pediatric dosimetry applications using advanced computational tools

Pediatric patient-specific dosimetry of ionizing radiation is of great scientific and social interest. Children provide a higher relative cancer-risk from exposure to ionizing radiation compared to adults. The proposed study reviews the recent techniques applied in pediatric imaging and therapy applications for dosimetry purposes. Modern medicine makes use of advance computational tools for the personalization of internal and external dosimetry, especially in the sensitive group of children. Several groups of pediatric computational models have been developed which are combined with Monte Carlo simulations, Machine Learning techniques, and Image Processing algorithms for accurate dosimetry assessment. More specifically, this study reviews the dosimetry applications in pediatric diagnostic procedures, including computed tomography and nuclear medicine applications. Right afterwards, the most recent applications in therapeutic brachytherapy protocols are presented, which is a rather sensitive procedure in pediatrics. Finally, modern tools for dosimetry optimization are discussed, reviewing the most indicative applications with: a) Monte Carlo simulations for pediatric dosimetry assessment b) pediatric computational models, widely used in medical applications and c) Machine Learning techniques that provide an alternative method for estimating individualized absorbed doses.