Blind estimation of the arterial input function in MRI to model fluid transport in tissues and tumours

使用对比增强MRI测量组织血流（灌注）和相关液体传输参数在临床上是一种有价值的技术，但在不知道动脉血中造影剂浓度（动脉输入函数，AIF）的情况下，难以量化这些参数。当无法直接测量AIF时，已经描述了许多方法来估计AIF。其中一种方法称为盲估计，其中仅根据一系列组织“输出”来估计输入。然而，迄今为止的大多数研究都缺乏参考数据（即，已知的AIF）用于验证目的。我们在利兹有两个大型参考数据集以及大量临床数据，可用于评估盲态估计工具。在这个项目中，学生将开发这样一个工具，使用我们的参考数据测试其有效性，然后评估其在其他数据集中的应用，以估计血流和相关的流体传输参数。这些参数将被用来评估早期反应的治疗和预测结果的一系列临床应用的基础上，以前的CDT学生的工作。

Measurement of tissue blood flow (perfusion) and related fluid transport parameters using contrast-enhanced MRI is a valuable technique in the clinic, but these parameters are difficult to quantify without knowing the concentration of the contrast agent in the arterial blood (the arterial input function, AIF). There have been many approaches described to estimate the AIF when it is not possible to measure it directly. One such approach is called blind estimation where the input is estimated from a series of tissue "outputs" only. However, most studies to date suffer from a lack of reference data (i.e., a known AIF) for validation purposes. We have two large reference data sets in Leeds as well as lots of clinical data which could be used to assess a blind estimation tool. In this project the student will develop such a tool, test its validity using our reference data then assess its application in other data sets to estimate blood flow and related fluid transport parameters. These parameters will be used to assess early response to treatment and predict outcomes in a range of clinical applications building upon the work of a previous CDT student.