Abstract
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 (MC) simulations, machine learning (ML) techniques, and image processing algorithms for accurate dosimetry assessment. More specifically, this paper reviews the dosimetry applications in pediatric diagnostic procedures, including computed tomography and nuclear medicine applications. Right afterward, 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: 1) MC simulations for pediatric dosimetry assessment; 2) pediatric computational models, widely used in medical applications; and 3) ML techniques that provide an alternative method for estimating individualized absorbed doses.
Highlights
P ATIENT specific dosimetry is of high interest in pediatric applications as radiation sensitivity is noteworthy higher compared to adults
The aim of this review is to address the recent developments in pediatric computational models, Monte Carlo (MC) simulations, machine learning (ML) techniques and advanced image processing algorithms for dosimetry applications
In the rest of the cases (39%), the resulting effective doses resulted from the European Association of NM (EANM) were more than 20% higher
Summary
P ATIENT specific dosimetry is of high interest in pediatric applications as radiation sensitivity is noteworthy higher compared to adults. Computer-based phantoms, with the flexibility to model an unlimited set of anatomies, have the greatest potential to estimate patient-specific organ and effective dose. Combined with accurate pediatric computational models, MC serve as reference for the accurate determination of absorbed dose, toward personalized dosimetry [22], [23]. The aim of this review is to address the recent developments in pediatric computational models, MC simulations, machine learning (ML) techniques and advanced image processing algorithms for dosimetry applications. These advanced tools are required for the personalization of dosimetry assessment in pediatric applications and will offer the clinician the possibility to assess imaging and therapeutic protocols predicting the absorbed dose per organ with accuracy
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