ESTIMATION OF ORGAN DOSES AND EFFECTIVE DOSES BASED ON IN-PHANTOM DOSIMETRY FOR PAEDIATRIC DIAGNOSTIC CARDIAC CATHETERISATION

The present study evaluated the organ doses, effective doses and conversion factors from the dose–area product to effective dose in pediatric diagnostic cardiac catheterization performed by in-phantom dosimetry and Monte Carlo simulation. The organ and effective doses in 5-y-olds during diagnostic cardiac catheterizations were evaluated using radiophotoluminescence glass dosemeters implanted into a pediatric anthropomorphic phantom and PCXMC software. The mean effective dose was 3.8 mSv (range: 1.8–7.5 mSv). The conversion factors from the dose–area product to effective dose were 0.9 and 1.6 mSv (Gy cm2)−1 for posteroanterior and lateral fluoroscopy, respectively, and 0.9 and 1.5 mSv (Gy cm2)−1 for posteroanterior and lateral cineangiography, respectively. Effective doses evaluated using the pediatric dosimetry system agreed with those obtained using PCXMC software within 12%. The dose data and conversion factors evaluated may guide the estimation of exposure doses in children undergoing diagnostic cardiac catheterization.

Patient Specific Dosimetry Phantoms Using Multichannel LDDMM of the Whole Body

This paper describes an automated procedure for creating detailed patient-specific pediatric dosimetry phantoms from a small set of segmented organs in a child's CT scan. The algorithm involves full body mappings from adult template to pediatric images using multichannel large deformation diffeomorphic metric mapping (MC-LDDMM). The parallel implementation and performance of MC-LDDMM for this application is studied here for a sample of 4 pediatric patients, and from 1 to 24 processors. 93.84% of computation time is parallelized, and the efficiency of parallelization remains high until more than 8 processors are used. The performance of the algorithm was validated on a set of 24 male and 18 female pediatric patients. It was found to be accurate typically to within 1-2 voxels (2–4 mm) and robust across this large and variable data set.