BOLUS PREDICTION USING LINEAR SYSTEM THEORY

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 “使用线性系统理论进行团注预测”项目是一项改进对比增强磁共振血管造影术的尝试。应确定患者特异性和现实的注射方案，从而在血管系统中产生所需的增强轮廓。输入响应函数（IRF）的表征是通过对测试团注进行去卷积来完成的，从而增强了血管系统的性能。该IRF用于前向方法，该方法考虑了生理和技术限制（最大注射流速、再循环）。使用现代优化方法，可以在不超过9秒（平均值）内完成注入曲线的计算。这些注射曲线导致动脉系统中的增强曲线，其在最小范数意义上匹配期望的增强曲线。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The project 'Bolus Prediction Using Linear System Theory' is an attempt to improve contrast-enhanced magnetic resonance angiography. Patient specific and realistic injection protocols, which result in a desired enhancement profile in the vascular system, should be identified. A characterization of the input response function (IRF) is done by de-convoluting the test bolus injection with the resulting enhancement in the in the vascular system. This IRF is used in a forward approach, which takes physiological and technical constraints (maximum injection flow speed, recirculation) into account. With modern optimization methods, computation of the injection profile can be done in no more than nine seconds (averaged value). Those injection profiles led to an enhancement profile in the arterial system that matches the desired enhancement profile in a minimum norm sense.