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

脑组织脉动：用于检测成人重症监护中颅内压升高的建模和机器学习方法。

• 批准号: 2435538
• 金额: --
• 依托单位: University of Leicester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2435538
• 关键词: Brain; Tissue; Pulsations; modelling; machine

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.

目的：本博士项目的目的是开发颅内压（ICP）升高的检测标准，基于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。研究计划大脑由灌注血液的软神经组织组成，被脑脊液（CSF）包围，并包裹在颅骨内。脑组织的脉动动力学由进行测量的区域的神经解剖学、脑组织的顺应性以及CSF、静脉和动脉血流的动力学确定（图1（a））。博士学位的第一年将涉及现有的脑血流动力学模型的审查，并熟悉建模软件（Matlab）。在第二年，我们将探讨耦合脑血流脑组织脉动的液压机械组件。在头两年，与物理建模任务并行，我们还将继续探索大脑脉动特征与机器学习方法，这是以前开始的。2，3在第三年，建模和机器学习结果，将用于构建一个分类器，以检测升高的ICP。分类器的性能将验证对侵入性颅内压测量从头部创伤患者在诺丁汉QMC，斯图尔特·史密斯教授的监督下，谁将提供临床输入的study.This博士学位的机会将广泛宣传本科和最后一年的项目学生在莱斯特大学，有前途的学生将亲自接触，以确定承诺的高品质的候选人。

项目成果

Are portable ankle brachial pressure index measurement devices suitable for hypertension screening?

• DOI: 10.1371/journal.pone.0283281
• 发表时间: 2023
• 期刊: PLOS ONE
• 影响因子: 3.7
• 作者: Janus, Justyna;Nicholls, Jennifer K.;Pallett, Edward;Bown, Matthew;Chung, Emma M. L.
• 通讯作者: Chung, Emma M. L.

Emerging Detection Techniques for Large Vessel Occlusion Stroke: A Scoping Review.

• DOI: 10.3389/fneur.2021.780324
• 发表时间: 2021
• 期刊: Frontiers in neurology
• 影响因子: 3.4
• 作者: Nicholls JK;Ince J;Minhas JS;Chung EML
• 通讯作者: Chung EML