Machine Learning for Automated Heart Strain and Motion from DENSE

PHD项目的目的：密集MRI提供准确且可重现的心肌应变，但目前的处理依赖于大量的人工输入。该项目将利用现有数据和最先进的机器学习算法来自动处理和分割密集数据，输出心脏几何形状以及像素化位移和应变。项目描述/背景：机器学习和人工智能方法的最新进展使得在医学成像检查中定量分析心脏性能的新工具的开发成为可能。然而，对患者的应用需要在特定疾病病例的临床扫描上进行实施和评估。该项目将开发分析高密度心脏MRI检查的新方法。得到的软件工具将用于皇家布朗普顿医院进行的临床研究。DENSE是无创量化心肌应变和运动的最准确和最好的分辨率方法[1，2]。然而，目前的图像需要复杂且耗时的离线后处理来估计临床上重要的应变参数。特别是，必须确定(分割)心肌的边界并且展开相位信号(由于混叠)。在存在噪声和伪影的情况下，这是困难的，并且需要手动纠正错误。这种处理的采集后性质还意味着，核磁共振技术人员在很大程度上是在“盲目”操作，几乎没有应变数据和结果的质量迹象，这可以用来指导后续的数据采集。最近，机器学习和人工智能方法的进步在心脏MRI数据的自动评估方面显示出了希望[3，4]。该项目将利用机器学习和人工智能的最新进展来自动分析密集数据，包括分割和相位展开，以提供心脏所有区域的准确像素级应变信息。培训将利用Imperial和/或KCL的高性能计算集群。经过训练的模型将被整合到在线图像重建工具中，提供扫描仪上心肌位移和应变的即时测量。

Aim of the PhD Project:DENSE MRI provides accurate and reproducible myocardial strain, but processing currently relies on extensive manual input.This project will utilize existing data and state-of-the-art machine-learning algorithms to automatically process and segment DENSE data, outputting cardiac geometry as well as pixelwise displacement and strain.Project Description / Background:Recent advances in machine learning and artificial intelligence methods have enabled the development of new tools for the quantitative analysis of cardiac performance in medical imaging examinations. However, applications to patients require implementation and evaluation on clinical scans in specific disease cases. This project will develop new methods for the analysis of DENSE cardiac MRI exams. The resulting software tools will be used in clinical studies performed at the Royal Brompton Hospital.DENSE is the most accurate and best resolution method for non-invasive quantification of strain and motion of heart muscle [1,2]. However, the images currently require complex and time-consuming off-line post-processing to estimate the clinically important strain parameters. In particular, the borders of the heart muscle must be determined (segmentation) and the phase signal unwrapped (due to aliasing). In the presence of noise and artefacts this is difficult and errors need to be corrected manually. The post-acquisition nature of this processing also means that MRI technologists are operating "blind" to a large extent, with little indication of the quality of the strain data and results, which could be used to guide subsequent data acquisition. Recently, advances in machine learning and AI methods have shown promise in automatic evaluation of cardiac MRI data [3, 4]. These do not require manual interaction and can considerably speed up the evaluation process.This project will leverage recent advances in machine learning and artificial intelligence to automatically analyse DENSE data, including segmentation and phase unwrapping, to provide accurate pixel-wise strain information in all regions of the heart. Training will make use of the high performance computing clusters at Imperial and/or KCL. The trained models will be incorporated into online image reconstruction tools, providing immediate measures of myocardial displacement and strain at the scanner.