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

利用磁共振造影增强技术测量组织血流（灌注）和相关的流体输送参数在临床上是一项有价值的技术，但如果不知道动脉血液中造影剂的浓度（动脉输入功能，AIF），这些参数很难量化。当无法直接测量AIF时，已经描述了许多方法来估计AIF。其中一种方法被称为盲估计，即只从一系列组织“输出”中估计输入。然而，迄今为止的大多数研究都缺乏用于验证目的的参考数据（即已知的AIF）。我们在利兹有两个大的参考数据集，以及大量的临床数据，可以用来评估一个盲估计工具。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，以估计血液流量和相关的流体运输参数。这些参数将用于评估治疗的早期反应，并在一系列临床应用中预测结果，这些应用建立在以前CDT学生的工作基础上。

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.