Machine Learning for Automated Heart Strain and Motion from DENSE

博士项目的目的：DENSE MRI提供准确和可重复的心肌应变，但处理目前依赖于大量的手动输入。该项目将利用现有的数据和最先进的机器学习算法来自动处理和分割DENSE数据，输出心脏几何形状以及像素位移和应变。项目描述/背景：机器学习和人工智能方法的最新进展使医学成像检查中心脏性能定量分析的新工具得以开发。然而，对患者的应用需要在特定疾病病例中对临床扫描进行实施和评估。该项目将开发用于分析DENSE心脏MRI检查的新方法。由此产生的软件工具将用于在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、最佳分辨率的方法[1，2]。然而，目前的图像需要复杂和耗时的离线后处理，以估计临床上重要的应变参数。特别地，必须确定心肌的边界（分割）并且相位信号展开（由于混叠）。在存在噪声和伪影的情况下，这是困难的，并且需要手动校正错误。这种处理的采集后性质也意味着MRI技术人员在很大程度上是“盲目”操作的，几乎没有应变数据和结果的质量指示，这些应变数据和结果可用于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自动分析DENSE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应变信息。培训将利用帝国和/或KCL的高性能计算集群。经过训练的模型将被纳入在线图像重建工具，在扫描仪上提供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.