Abstract
Purpose: To investigate the benefits of patient‐phantom matching for determining skin and organ dose in interventional fluoroscopy. Method and Materials: In this study, approximately 30 CT datasets representing patients of different sizes and genders were contoured and converted into patient‐specific computational models. Each model was matched, based on height and weight, to a computational phantom selected from the UF hybrid patient‐dependent series. In order to investigate organ dose, Monte Carlo methods were used to simulate several fluoroscopic projections. Organ dose conversion coefficients were calculated for each patient‐specific and patient‐dependent phantom, and also for a reference stylized and reference hybrid phantom. The coefficients were subsequently analyzed for any correlation between patient‐specificity and the accuracy of the dose estimate. In order to investigate skin dose, a dose mapping program was developed and used to calculate local skin dose for a representative interventional procedure. Dose maps were produced for each phantom type and compared based on intensity and position. Results: In terms of organ dose, patient‐phantom matching was shown most beneficial for estimating the dose to large patients. In these cases, the improvement over using a reference stylized phantom ranged from 65–115%, and for a reference hybrid phantom from 20–40%. For smaller individuals, patient‐phantom matching was clearly superior to using a reference stylized phantom, but not significantly better than using a reference hybrid phantom for certain fields and projections. For skin dose, patient‐phantom matching was shown to increase the accuracy of both the location and value of the dose estimate. Conclusions: These results indicate that patient‐phantom matching can improve the accuracy of dose estimation, specifically for large patients. With increasing obesity rates, these patients will continue to make up a large portion of individuals undergoing interventional procedures. Thus, having phantoms that better represent the over‐weight population represents a considerable improvement over previous methods.
