Nuclear and Radiological Engineering, University of Florida, Gainesville, Florida 32611, USA.
Med Phys. 2011 Feb;38(2):1008-17. doi: 10.1118/1.3544353.
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.
研究在透视引导介入过程中，通过患者-体模匹配来确定器官剂量的优势和局限性。
在这项研究中，我们对 27 个代表不同体型和性别的 CT 数据集进行了轮廓绘制并转换为特定于患者的计算模型。每个模型都基于身高和体重与从 UF 混合患者相关系列中选择的计算体模进行匹配。为了研究体模类型对患者器官剂量的影响，我们使用蒙特卡罗方法模拟了两种心脏投影（PA/左侧）和两种腹部投影（RAO/LPO）。然后，我们为每个特定于患者的和患者相关的体模以及参考样式化和参考混合体模计算了器官剂量转换系数。随后，我们分析了患者特异性与剂量估计准确性之间的任何相关性。通过使用特定于患者的剂量转换系数作为参考来计算绝对百分比差异来量化准确性。
研究表明，对于估计重患者的剂量，患者-体模匹配最为有益。在这些情况下，与使用参考样式化体模相比，腹部投影的剂量估计改善幅度在 50%到 120%之间，对于所有投影，与参考混合体模相比改善幅度在 20%到 60%之间。对于较轻的个体，患者-体模匹配明显优于使用参考样式化体模，但在某些视野和投影下，并不明显优于使用参考混合体模。
研究结果表明，当患者与体模匹配时，存在两种误差源：由于器官大小和位置的差异而导致的解剖学误差，以及由于总软组织衰减差异而导致的误差。对于小患者，软组织衰减的差异很小，被内在的解剖学差异所超越。对于大患者，软组织衰减的差异可能很大。在这些情况下，患者-体模匹配效果最佳，因为软组织衰减的差异得到了缓解。随着肥胖率的上升，超重患者在接受医学成像的所有患者中所占比例将继续增加。因此，拥有更好地代表这一人群的体模是对以前方法的重大改进。针对本研究，UFHADM 和 UFHADF 患者相关系列中将增加代表更重体重百分位数的额外体模。
