Quantitative 4D Echocardiography using Fusion Techniques and Spatio-temporal Analysis

4D超声心动图(或实时3D超声心动图)是容量心脏成像领域的一个新成员，正受到临床的极大关注，因为它可能取代更昂贵的技术(MRI)的需要，并为体积心脏成像提供了新的应用领域，例如在介入治疗中。该研究项目在EPSRC GR/S72801的基础上，涉及定量4D超声心动图，特别是开发从4D超声心动图估计心脏健康的全球和区域功能测量的定量方法。该项目的新颖性建立在两个主要思想上。融合4D超声心动图：一种最初在EPSRC GR/S72801中研究的想法，即融合来自两次或更多4D采集的信息，以获得比单一采集更完整的心脏结构/功能表示。在之前的工作中，我们开发了融合方法，并展示了它导致了卓越的3D图像。在这个新的项目中，我们计划研究自动定量分析(分割)，我们假设这些将优于融合的4D超声心动图。我们还将研究组合对齐(配准)和分割的技术，这是朝着使定量融合4D超声心动图完全自动化并因此在临床实践中有用的方向发展的自然下一步。2.静息和负荷超声心动图联合序列分析：负荷超声心动图是临床上广泛使用的一种检查方法，在心脏静止时和负荷后进行成像。负荷超声心动图定量图像分析的工作有限，虽然在临床诊断中使用的是静息和负荷序列之间的相对差异，但对两者的处理是分开进行的。该项目这一部分的新想法是使用前应激序列和后应激序列来定义从训练序列中学习的健康/异常功能的模型(数学上为隐马尔可夫模型)。我们对体积数据执行此操作。在这个项目中，我们将建立这样的模型，并将它们应用于疾病的初始分类，然后作为分割的约束。这两个发展都将促进临床4D超声心动图，以及更广泛的3D心脏成像。开发的工具还可能在非心脏应用中找到应用，例如产科和妇科(评估子宫健康)，或肿瘤学(肿瘤对治疗的反应)。

4D echocardiography (or real-time 3D echocardiography) is a recent addition to the volumetric cardiac imaging field which is receiving significant clinical interest as it may replace the need for more expensive techniques (MRI) and also suggest new areas of application for volumetric cardiac imaging e.g. in interventions. This research project, building on the succces of EPSRC GR/S72801, concerns quantitative 4D echocardiography, and in particular developing quantitative methods for estimating global and regional functional measures of the health of the heart from 4D echocardiography The novelty in the project is built on two principal ideas.1. Fused 4D echocardiography: an idea, initially investigated in EPSRC GR/S72801, of fusing information from 2 or more 4D acquisitions to get an anatomically more complete representation of the structure/function of a heart than can be obtained from a single acquisition. In prior work we developed methodology for fusion and showed it led to superior 3D images. In this new project we plan to investigate automated quantitative analysis (segmentation) which we hypothese will be superior on fused 4D echocardiography. We will also investigate techniques for combined alignment (registration) and segmentation which is a natural next step to investigate towards making quantitative fused 4D echocardiography fully automated and hence useful in clinical practice. 2. Analysis of combined rest and stress echocardiographic sequences: Stress echocardiography is a widely used test in clinical practice where imaging is done when the heart is at rest and then after it has been stressed. The limited work on quantitative image analysis of stress echocardiography has treated rest and stress sequences separately even though it is the relative difference between the two that is used in clinical diagnosis. The novel idea in this part of the project is to use both pre and post stress sequences to define a model of healthy/abnormal function (mathematically a Hidden Markov Model) which is learned from training sequences. We do this on volumetric data. In the project we will build such models and apply them to initially classify disease and then as a constraint in segmentation.Both developments will advance clinical 4D echocardiography, and 3D cardiac imaging more generally. The developed tools may also find application in non-cardiac applications, such as in obstetrics and gynaecology (assessing the health of the uterus), or oncology (tumour response to treatment).