Heilig-Hartziekenhuis Roeselare-Menen, Belgium.
Br J Radiol. 2012 Dec;85(1020):e1212-8. doi: 10.1259/bjr/83724929.
To develop a multicompartment model of only essential human body components that predicts the contrast medium concentration vs time curve in a chosen compartment after an intravenous injection. Also to show that the model can be used to time adequately contrast-enhanced CT series.
A system of linked time delay instead of ordinary differential equations described the model and was solved with a Matlab program (Matlab v. 6.5; The Mathworks, Inc., Natick, MA). All the injection and physiological parameters were modified to cope with normal or pathological situations. In vivo time-concentration curves from the literature were recalculated to validate the model.
The recalculated contrast medium time-concentration curves and parameters are given. The results of the statistical analysis of the study findings are expressed as the median prediction error and the median absolute prediction error values for both the time delay and ordinary differential equation systems; these are situated well below the generally accepted maximum 20% limit.
The presented program correctly predicts the time-concentration curve of an intravenous contrast medium injection and, consequently, allows an individually tailored approach of CT examinations with optimised use of the injected contrast medium volume, as long as time delay instead of ordinary differential equations are used.
The presented program offers good preliminary knowledge of the time-contrast medium concentration curve after any intravenous injection, allowing adequate timing of a CT examination, required by the short scan time of present-day scanners. The injected volume of contrast medium can be tailored to the individual patient with no more contrast medium than is strictly needed.
开发一个仅包含人体基本成分的多腔室模型,以预测静脉注射后所选腔室中的对比剂浓度随时间的变化曲线。同时,展示该模型可用于充分定时对比增强 CT 系列。
用一个包含时间延迟的系统来描述模型,而不是用普通微分方程来描述。该系统使用 Matlab 程序(Matlab v. 6.5;The Mathworks, Inc.,Natick,MA)进行求解。所有的注射和生理参数都进行了修改,以适应正常或病理情况。从文献中重新计算了体内时间浓度曲线,以验证模型。
给出了重新计算的对比剂时间浓度曲线和参数。对研究结果的统计分析结果表示为时间延迟和常微分方程组的中位数预测误差和中位数绝对预测误差值;这些值都远低于普遍接受的最大 20%限制。
该程序正确地预测了静脉注射对比剂的时间浓度曲线,因此,只要使用时间延迟而不是普通微分方程,就可以根据个体患者的情况,采用优化的对比剂注射量,实现 CT 检查的个体化方法。
该程序提供了关于任何静脉注射后时间对比剂浓度曲线的初步良好知识,允许充分定时 CT 检查,以适应目前扫描仪的短扫描时间。可以根据个体患者的情况调整对比剂的注射量,而不需要更多的对比剂。