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Research of Heart Reconstruction Approach Based on 4D-CT Datasets

Author: WangXingJia
Tutor: FengHuanQing
School: University of Science and Technology of China
Course: Biomedical Engineering
Keywords: MSCT 4DCT Cardiac Segmentation Levelset ICLS Model IFLW Model Statistical Shape Models Cardiac Function Analysis
CLC: TP391.41
Type: PhD thesis
Year: 2011
Downloads: 65
Quote: 1
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Abstract

Cardiovascular diseases (CVDs) characterized by acute onset and great hidden nature, with the highest incidence and mortality rate, are the number one cause of death all over the world. The early quantitative diagnosis and accurate evaluation of CVDS have an important clinical significance. The occurence of Multi-Spiral CT (MSCT) detector based on multiple-detector technique, has significantly promoted imaging speed and scanning accuracy, which has been widely used in cardiac examination and diagnosis, the evaluation of coronary heart disease. Associated The Appearance of the ECG-synchronization scanning technique, the 4D cardiac CT imaging datasets included the temporal Information would be obtained from MSCT, which provide an effective means of clinical observation for dynamic characteristics of heart.As these new technology have been promoting the development of the medical image processing and analysis, the crucial task ahead of us is how to extract the heart morphology, function and cardiac Pathological Information from the massive imaging data, in order to make full use of the high-end imaging equipment in the field of CVDs early quantitative prevention and diagnosis . Hence the heart extraction and function analysis based on MSCT have become a hot issue in the medical image processing area in recent years.This paper based on 3D cardiac datasets from 64-slices MSCT and 4D cardiac datasets (3D+t) from 256-slices MSCT, have explored the automatic and semi-automatic cardiac segmentation without training sample set, the cardiac multi -phases segmentation with training sample set, and buildup of the 4D segmentation model of left ventricle(LV). Based on the segmentation model, this paper analyzes the cardiac global static parameters and dynamic parameters preliminarily. The main research work and contributions of this dissertation can be summarized as follows:1. According to the characteristic of cardiac MSCT datasets, with the analysis of difficulties of heart segmentation for MSCT imaging, the design of the integral segmentation framework based on multi-phase cardiac datasets have been accomplished, which mainly include pre-processing operation such as cardiac axis’s transformation for the datasets, intensity transformation, image denosing and so on, and automatic localization algorithm, automatic and semi-automatic LV segmentation algorithm, etc. the construction of segmentation models will lay a solid foundation for the further heart parameter extraction and function analysis .2. With the deep analysis of the edge- and region-based levelset model, according to the drawbacks of edge leakage and sensitivity to initial position, we proposed unsupervised improved coupled levelset (ICLS) automatic LV segmentation algorithm without training datasets,by combination of cardiac anatomy and MSCT slice characteristic. On the basis of the automatic location algorithm to determine the initial contour position of the level set curve accurately, through the improvement of edge detection function and coupled level set function with LV shape, a complete heart segmentation ICLS model is built. The model could inhibit edge leakage, and avoid the local minimum during the curve evolution, and by this model an ideal epicardium and endocardium could be obtained automatically.3. With regard to the LV segmentation of cardiac slices with edge blur and lack, ICLS model relies heavily on the termination conditions of the evolution and parameters of the level set model. This paper proposes a manual-interaction IFLW (Improved Fuzzy connectedness-based Live-Wire) semi-automatic LV segmentation model. By generating the seed points automatically, a new Fuzzy connectedness function incorporating myocardium position information is introduced to represent the region fuzzy features of the slices with edge blur and loss. The fuzzy connectedness of myocardium is then embedded into the joint cost matrix in polar coordinates, and with the improved Livewire algorithm (IFLW), the heart extraction from datasets is achieved semi-automatically. Compared with traditional Livewire algorithm, IFLW model requires much fewer feature points and the segmentation results are close to the accuracy of manual segmentation. IFLW model reduces extra manual intervention while increasing the robustness and accuracy of the LV segmentation algorithm.4. To reduce the manual intervention level and make full use of the advantages of IFLW accurate segmentation model, an improved shape statistical models segmentation algorithm based on IFLW model to segment the sample sets is proposed. On the basis of the segmentation results of the chosen slices by IFLW, The shape model and local intensity model are constructed. Through the new localization algorithm of the initial shape model and improved shape model search algorithm, the 3D segmentation of MSCT datasets based on improved multi-scale shape statistical model is proposed. In addition, by the Demons registration algorithm based on mutual Information in the cardiac multi-phase 4D MSCT datasets, the deformation field from the slice of the segmented 3D datasets to the corresponding slice of the 3D dataset to be segmented could be obtained, which is used to deform the segmented results of the segmented 3D datasets, to achieve the segmentation result of the 3D dataset. By the means of the non-rigid registration and shape statistical models, the whole MSCT 4D data set segmentation results are obtained.5. On the basis of the above work, a preliminary function analysis for cardiac static and dynamic parameters was carried out for the further research.Finally, special thanks to the State of the National Natural Science Foundation of China (60771007) and Anhui Natural Science Foundation of China (2006KJ097A) for funding this work.

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