基于MRI和血流动力学仿真的主动脉缩窄计算机辅助诊断系统-《中国医学物理学杂志》

文章信息/Info

Title:: Computer-aided diagnosis system for aortic coarctation based on MRI and hemodynamic simulation

作者:: 史宇兵1; 2; Israel Valverde3; Patricia V. Lawford2; D. Rodney Hose2; 1.陕西中医药大学整合医学研究院，陕西咸阳 712046； 2. Medical Physics Group, Department of Cardiovascular Science, Faculty of Medicine, Dentistry and Health, University of Sheffield, Sheffield, UK 3. Division of Imaging Sciences and Biomedical Engineering, Kings College London, The Rayne Institute, St. Thomas Hospital, London, UK

Author(s):: SHI Yubing1; 2; Israel Valverde3; Patricia V. Lawford2; D. Rodney Hose2; 1. Institute of Integrative Medicine, Shaanxi University of Chinese Medicine, Xianyang 712046, China 2. Medical Physics Group, Department of Cardiovascular Science, Faculty of Medicine, Dentistry and Health, University of Sheffield, Sheffield, UK 3. Division of Imaging Sciences and Biomedical Engineering, Kings College London, The Rayne Institute, St. Thomas Hospital, London, UK

Keywords:: Keywords: cardiocerebral vascular disease computer-aided diagnosis system magnetic resonance imaging hemodynamic calculation computer simulation

摘要:: 目前用于心脑血管系统疾病的计算机辅助诊断系统比较少，本研究基于参加欧盟FP7 euHeart项目所做的工作，以对主动脉缩窄的处理为例，介绍一个基于核磁共振成像（MRI）和血流动力学计算的心脑血管疾病计算机辅助诊断系统，对系统的结构、功能和工作流程进行详细描述。该系统以患者病变血管的MRI图像和在颈动脉和股动脉处测得的血压波形为输入信息，通过图像处理模块、生理数据处理模块、计算网格生成模块以及流体动力学计算模块对这些数据进行分析处理，输出跨缩窄段的压差以辅助评估对患者进行干预手术的必要性，并输出病变血管段的流速分布、剪切应力分布等血流状态信息用于估算血流异常对血管内皮以及血细胞造成潜在机械损伤的程度。以一个典型病例的处理结果为例，对所开发的系统进行初步测试，结果表明所开发的系统运行结果准确可靠。本系统对于开发其他心脑血管疾病计算机辅助诊断系统具有借鉴意义。

Abstract:: Abstract: Currently there is few computer-aided diagnosis system for the cardiocerebral vascular diseases. Based on the research works conducted in the EU FP7 euHeart project and taking the diagnosis of aortic coarctation as an example, a computer-aided diagnosis system for the cardiocerebral vascular diseases based on MRI and hemodynamic calculation is presented, and the structure, function and the workflow of the system are explained in details. The system uses the MRI images of the diseased vessel and the pressure waveforms measured at carotid and femoral arteries as the input, analyzes these data using the image processing module, the physiological data processing module, the computational grid generation module and the hemodynamic calculation module, and outputs the pressure gradient across the coarctation to aid the assessment of necessity for interventional surgery, besides revealing the hemodynamic characteristics including the velocity and shear stress distributions to assist the prediction of potential for mechanical damages to the endothelium and blood cells accompanying the abnormal blood flow in the diseased vessel. The developed system is preliminarily validated using a typical case as the example, and the results show that the system give accurate and reliable predictions of the disease condition. The developed system can serve as a reference in building other computer-aided diagnosis systems for the cardiocerebral vascular diseases.