The impact of anthropometric patient‐phantom matching on organ dose: A hybrid phantom study for fluoroscopy guided interventions

Abstract

To investigate the benefits and limitations of patient-phantom matching for determining organ dose during fluoroscopy guided interventions. In this study, 27 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 computational phantoms selected from the UF hybrid patient-dependent series. In order to investigate the influence of phantom type on patient organ dose, Monte Carlo methods were used to simulate two cardiac projections (PA/left lateral) and two abdominal projections (RAO/LPO). Organ dose conversion coefficients were then 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. Accuracy was quantified by calculating an absolute percent difference using the patient-specific dose conversion coefficients as the reference. Patient-phantom matching was shown most beneficial for estimating the dose to heavy patients. In these cases, the improvement over using a reference stylized phantom ranged from approximately 50% to 120% for abdominal projections and for a reference hybrid phantom from 20% to 60% for all projections. For lighter 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. The results indicate two sources of error when patients are matched with phantoms: Anatomical error, which is inherent due to differences in organ size and location, and error attributed to differences in the total soft tissue attenuation. For small patients, differences in soft tissue attenuation are minimal and are exceeded by inherent anatomical differences. For large patients, difference in soft tissue attenuation can be large. In these cases, patient-phantom matching proves most effective as differences in soft tissue attenuation are mitigated. With increasing obesity rates, overweight patients will continue to make up a growing fraction of all patients undergoing medical imaging. Thus, having phantoms that better represent this population represents a considerable improvement over previous methods. In response to this study, additional phantoms representing heavier weight percentiles will be added to the UFHADM and UFHADF patient-dependent series.