TH-AB-207A-06: The Use of Realistic Phantoms to Predict CT Dose to Pediatric Patients




To predict pediatric patient dose from diagnostic CT scans using Monte Carlo simulation of realistic reference phantoms of various ages, weights, and heights.


A series of deformable pediatric reference phantoms using Non-Uniform Rational B-Splines (NURBS) was developed for a large range of ages, percentiles, and reference anatomy. Individual bones were modeled using age-dependent factors, and red marrow was modeled as functions of age and spatial distribution based on Cristy1. Organ and effective doses for the phantom series were calculated using Monte Carlo simulation of chest, abdominopelvic, and chest-abdomen-pelvis CT exams. Non-linear regression was performed to determine the relationship between dose-length-product (DLP)-normalized organ and effective doses and phantom diameter. Patient-specific voxel computational phantoms were also created by manual segmentation of previously acquired CT images for 40 pediatric patients (0.7 to 17 years). Organ and effective doses were determined by Monte Carlo simulation of these patient-specific phantoms. Each patient was matched to the closest pediatric reference phantom based primarily on age and diameter for all major organs within the torso.


A total of 80 NURBS phantoms were created ranging from newborn to 15 years with height/weight percentiles from 10 to 90%. Organ and effective dose normalized by DLP correlated strongly with exponentially decreasing average phantom diameter (R2 > 0.95 for most organs). A similar relationship was determined for the patient-specific voxel phantoms. Differences between patient-phantom matched organ-dose values ranged from 0.37 to 2.39 mGy (2.87% to 22.1%).


Dose estimation using NURBS-based pediatric reference phantoms offers the ability to predict patient dose before and after CT examinations, and physicians and scientists can use this information in their analysis of dose prescriptions for particular subjects and study types. This may lead to practices that minimize radiation dose while still achieving high quality images and, ultimately, improved patient care.

NIH/NCI 1 R01 CA155400-01A1