Pediatric Patient Surface Model Atlas Generation and X-Ray Skin Dose Estimation

Autor:	Siming Bayer, Andreas Maier, Markus Kowarschik, Xia Zhong, Philipp Roser, Nishant Ravikumar, Norbert Strobel, Rebecca Fahrig, Annette Birkhold, Tim Horz
Rok vydání:	2019
Předmět:	Estimation education.field_of_study medicine.medical_specialty medicine.diagnostic_test Computer science Population Point cloud Skin dose Term (time) medicine.anatomical_structure Population model Atlas (anatomy) medicine Fluoroscopy Medical physics education
Zdroj:	Informatik aktuell ISBN: 9783658253257 Bildverarbeitung für die Medizin
DOI:	10.1007/978-3-658-25326-4_27
Popis:	Fluoroscopy is used in a wide variety of examinations and procedures to diagnose or treat patients in modern pediatric medicine. Although these image guided interventions have many advantages in treating pediatric patients, understanding the deterministic and long term stochastic effects of ionizing radiation are of particular importance for this patient demographic. Therefore, quantitative estimation and visualization of radiation exposure distribution, and dose accumulation over the course of a procedure, is crucial for intra-procedure dose tracking and long term monitoring for risk assessment. Personalized pediatric models are necessary for precise determination of patient-X-ray interactions. One way to obtain such a model is to collect data from a population of pediatric patients, establish a population based generative pediatric model and use the latter for skin dose estimation. In this paper, we generate a population model for pediatric patient using data acquired by two RGB-D cameras from different views. A generative atlas was established using template registration. We evaluated the registered templates and generative atlas by computing the mean vertex error to the associated point cloud. The evaluation results show that the mean vertex error reduced from 25.2 ± 12.9 mm using an average surface model to 18.5 ± 9.4mm using specifically estimated pediatric surface model using the generated atlas. Similarly, the dose estimation error was halved from 10.6 ± 8.5% using the average surface model to 5.9 ± 9.3% using the personalized surface estimates.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::29f9c9bdd9eb8bc956320493e3e2e5aa https://doi.org/10.1007/978-3-658-25326-4_27 Zobrazit plný text záznamu