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Prognostic Implications of Mitral Annular Plane Systolic Excursion in Patients with Hypertension and a Clinical Indication for Cardiac Magnetic Resonance Imaging: A Multicenter Study.高血压患者临床有心脏磁共振成像适应证时二尖瓣环平面收缩期位移的预后意义:一项多中心研究。
JACC Cardiovasc Imaging. 2019 Sep;12(9):1769-1779. doi: 10.1016/j.jcmg.2018.10.003. Epub 2018 Nov 5.
2
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks.基于全卷积网络的自动化心血管磁共振图像分析。
J Cardiovasc Magn Reson. 2018 Sep 14;20(1):65. doi: 10.1186/s12968-018-0471-x.
3
Regional myocardial strain measurements from 4DCT in patients with normal LV function.四维 CT 测量正常左心室功能患者的局部心肌应变。
J Cardiovasc Comput Tomogr. 2018 Sep-Oct;12(5):372-378. doi: 10.1016/j.jcct.2018.05.002. Epub 2018 May 9.
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SiSSR: Simultaneous subdivision surface registration for the quantification of cardiac function from computed tomography in canines.SiSSR:用于犬类 CT 图像心功能定量分析的同时细分曲面配准。
Med Image Anal. 2018 May;46:215-228. doi: 10.1016/j.media.2018.03.009. Epub 2018 Mar 29.
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Statistical characterization of noise for spatial standardization of CT scans: Enabling comparison with multiple kernels and doses.CT 扫描空间标准化的噪声统计特性:实现与多内核和剂量的比较。
Med Image Anal. 2017 Aug;40:44-59. doi: 10.1016/j.media.2017.06.001. Epub 2017 Jun 7.
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Graph cut-based method for segmenting the left ventricle from MRI or echocardiographic images.基于图割的方法从 MRI 或超声心动图图像中分割左心室。
Comput Med Imaging Graph. 2017 Jun;58:1-12. doi: 10.1016/j.compmedimag.2017.03.004. Epub 2017 Apr 2.
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Global Longitudinal Strain by Echocardiography Predicts Long-Term Risk of Cardiovascular Morbidity and Mortality in a Low-Risk General Population: The Copenhagen City Heart Study.超声心动图测量的整体纵向应变可预测低风险普通人群心血管疾病和死亡的长期风险:哥本哈根城市心脏研究
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8
Cardiac 3D Printing and its Future Directions.心脏3D打印及其未来发展方向。
JACC Cardiovasc Imaging. 2017 Feb;10(2):171-184. doi: 10.1016/j.jcmg.2016.12.001.
9
Statistical shape modeling of the left ventricle: myocardial infarct classification challenge.左心室的统计形状建模:心肌梗死分类挑战
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10
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Nat Rev Cardiol. 2016 Dec;13(12):701-718. doi: 10.1038/nrcardio.2016.170. Epub 2016 Oct 27.

基于4D图割的动态CTA中主动脉瓣和二尖瓣位移的自动估计

Automatic estimation of aortic and mitral valve displacements in dynamic CTA with 4D graph-cuts.

作者信息

Ortuño Juan E, Vegas-Sánchez-Ferrero Gonzalo, Gómez-Valverde Juan J, Chen Marcus Y, Santos Andrés, McVeigh Elliot R, Ledesma-Carbayo María J

机构信息

Biomedical Research Networking Centre on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain; Biomedical Image Technologies Lab, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain.

Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Biomedical Image Technologies Lab, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain; Biomedical Research Networking Centre on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.

出版信息

Med Image Anal. 2020 Oct;65:101748. doi: 10.1016/j.media.2020.101748. Epub 2020 Jun 6.

DOI:10.1016/j.media.2020.101748
PMID:32711368
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7722502/
Abstract

The location of the mitral and aortic valves in dynamic cardiac imaging is useful for extracting functional derived parameters such as ejection fraction, valve excursions, and global longitudinal strain, and when performing anatomical structures tracking using slice following or valve intervention's planning. Completely automatic segmentation methods are still challenging tasks because of their fast movements and the different positions that prevent good visibility of the leaflets along the full cardiac cycle. In this article, we propose a processing pipeline to track the displacement of the aortic and mitral valve annuli from high-resolution cardiac four-dimensional computed tomographic angiography (4D-CTA). The proposed method is based on the dynamic separation of left ventricle, left atrium and aorta using statistical shape modeling and an energy minimization algorithm based on graph-cuts and has been evaluated on a set of 15 electrocardiography-gated 4D-CTAs. We report a mean agreement distance between manual annotations and our proposed method of 2.52±1.06 mm for the mitral annulus and 2.00±0.69 mm for the aortic valve annulus based on valve locations detected from manual anatomical landmarks. In addition, we show the effect of detecting the valvular planes on derived functional parameters (ejection fraction, global longitudinal strain, and excursions of the mitral and aortic valves).

摘要

在动态心脏成像中,二尖瓣和主动脉瓣的位置对于提取诸如射血分数、瓣膜偏移和整体纵向应变等功能衍生参数很有用,并且在使用切片跟踪进行解剖结构追踪或进行瓣膜干预规划时也很有用。由于心脏快速运动以及不同位置会妨碍在整个心动周期中清晰看到瓣叶,因此完全自动分割方法仍然是具有挑战性的任务。在本文中,我们提出了一种处理流程,用于从高分辨率心脏四维计算机断层血管造影(4D-CTA)中跟踪主动脉瓣和二尖瓣环的位移。所提出的方法基于使用统计形状建模对左心室、左心房和主动脉进行动态分离,以及基于图割的能量最小化算法,并已在一组15例心电图门控的4D-CTA上进行了评估。基于从手动解剖标志检测到的瓣膜位置,我们报告二尖瓣环的手动标注与我们提出的方法之间的平均一致性距离为2.52±1.06毫米,主动脉瓣环为2.00±0.69毫米。此外,我们展示了检测瓣膜平面对衍生功能参数(射血分数、整体纵向应变以及二尖瓣和主动脉瓣的偏移)的影响。