Brain Tissue Pulsations: modelling and machine learning methods for the detection of raised intracranial pressure in adult intensive care.

目的：本博士项目旨在建立检测颅内压升高（ICP）的标准，基于对脑组织脉动的无创超声测量，用于自动检测获得性脑损伤患者的颅内压升高。研究计划大脑是由充满血液的柔软神经组织组成的，被脑脊液（CSF）包围，并包裹在头骨内。脑组织的脉动动力学是由测量区域的神经解剖学、脑组织的顺应性以及脑脊液、静脉和动脉血流的动力学决定的（图1(a)）。博士学位的第一年将包括回顾现有的脑血流动力学模型，并熟悉建模软件（Matlab）。在第二年，我们将探索将脑血流与脑组织搏动耦合的流体机械成分。在最初的两年里，在物理建模任务的同时，我们还将继续用机器学习方法探索大脑的脉搏特征，这是之前开始的在第三年，建模和机器学习的结果，将用于构建一个分类器来检测升高的ICP。在Stuart Smith教授的监督下，该分类器的性能将通过诺丁汉QMC头部创伤患者的侵入性ICP测量进行验证，Stuart Smith教授将为该研究提供临床输入。这个博士机会将在莱斯特大学的本科生和最后一年的项目学生中广泛宣传，有前途的学生将亲自接触，以确定承诺的高质量候选人。

Aims: This PhD project aims to develop criteria for the detection of raised Intracranial Pressure (ICP), based on non-invasive ultrasound measurements of brain tissue pulsations, for automated detection of raised ICP in patients with acquired brain injury.Research Plan The brain is formed of soft neural tissue that is perfused with blood, surrounded by cerebrospinal fluid (CSF), and encased within the skull. The pulsating dynamics of brain tissue is determined by the neuroanatomy of the region in which the measurements take place, the compliance of brain tissue, and the dynamics of CSF, venous and arterial blood flow (Fig. 1(a)). The first year of the PhD will involve a review of existing models of cerebral flows dynamics, and familiarisation with the modelling software (Matlab). In the second year, we will explore the hydro-mechanical components coupling cerebral flows to brain tissue pulsatility. Over the first two years, in parallel with the physical modelling task, we will also continue the exploration of the brain pulsatility features with a machine learning approach, which was initiated previously.2,3 In the third year, modelling and machine learning results, will be used to construct a classifier to detect raised ICP. The performance of the classifier will be validated against invasive ICP measurements from head trauma patients at Nottingham QMC, under the supervision of Professor Stuart Smith, who will provide clinical input to the study.This PhD opportunity will be widely advertised amongst undergraduate and final year project students at the University of Leicester, and promising students will be personally approached to identify committed high quality candidates.