Computational biophysical modelling for the optimisation of cardiac magnetic resonance perfusion imaging protocols

心肌梗塞(MI)是一种严重的疾病，当血液流向心脏的一部分受到限制或停止时，会对心肌造成损害。对心脏造成的损伤可能包括心肌细胞的纤维化和坏死，超过一定时间可能会影响心脏有效泵血的能力，最终导致部分(但不是全部)患者心力衰竭。因此，有必要了解导致这种损害的过程，并因此了解观察到的不同临床结果。这个项目将使用数学建模和计算模拟在心肌梗死期间发生的各种细胞过程，目的是提供一个度量或计算工具的原理证明，该工具将在治疗疑似心肌梗死患者时为临床医生提供诊断支持。建议使用细胞过程的常微分方程式模型，并在适当的情况下使用心脏力学的偏微分方程式模型，该模型将由布里斯托尔大学转化生物医学研究中心的合作者从一系列猪实验中收集的数据进行参数化。这些数据将包括血液生物标记物和定量成像数据，由于数据收集将在MI期间和随后的时期进行，数据将提供足够的手段来验证这些模型的预测能力。该项目的一个更广泛的目标是创建最初可以使用更大的数据集进行验证的模型，并最终使用临床数据进行测试。

Myocardial infarction (MI) is a severe condition that occurs when blood flow is restricted or stopped to a part of the heart, causing damage to the heart muscle. Resulting injury to the heart can include fibrosis and necrosis of myocytes, which overtime can affect the ability of the heart to pump efficiently, ultimately leading to heart failure in some, but not all, patients. Thus, there is a need to understand the processes that are responsible for causing this damage, and to consequently understand the differing clinical outcomes that are observed. This then provides an opportunity for deriving patient-specific treatments that will limit these negative outcomes.This project will employ mathematical modelling and computational simulation of the various cellular processes that occur during MI, with the aims of providing a proof of principle of a metric or computational tool that will provide diagnostic support to clinicians when treating patients with suspected MI.It is proposed to use ordinary differential equation models of the cellular processes, and where appropriate partial differential equation models of cardiac mechanics, which will be parameterised by data collected from a series of porcine experiments performed by collaborators at the Translational Biomedical Research Centre, University of Bristol. This data will comprise both blood biomarker and quantitative imaging data, and as the data collection will occur both during the MI and the subsequent period, the data will provide ample means to validate these models' predictive capabilities. A broader aim of this project is to create models that can be validated using larger data sets initially, and ultimately be taken forward to test using clinical data.