Fully Automatic Registration and Segmentation of First-Pass Myocardial Perfusion MR Image Sequences
Fully Automatic Registration and Segmentation of First-Pass Myocardial Perfusion MR Image Sequences
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DOI：
10.1016/j.acra.2010.06.015
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发表时间：
2010-11-01
影响因子：
4.8
通讯作者：
Lelieveldt, Boudewijn P. F.
中科院分区：
文献类型：
--
作者：
Gupta, Vikas;Hendriks, Emile A.;Lelieveldt, Boudewijn P. F.
基本原理和目标：从首过心肌灌注磁共振图像导出诊断相关参数涉及大量图像中心肌的繁琐且耗时的手动分割。为了减少手动交互和加快灌注分析，我们提出了一种自动配准和分割方法的推导灌注连接parameters.Materials和Methods：一个完整的自动化完成了第一次注册未对齐的图像使用基于独立成分分析的方法，然后使用注册的数据自动分割的心肌与积极的外观models。我们使用了18个灌注研究（100每项研究的图像）进行验证，其中自动获得的（AO）轮廓进行了比较与专家绘制的轮廓的基础上的点到曲线的错误，骰子指数，和相对灌注upslope在myocardial.Results：目测检查显示成功分割15的18项研究。AO轮廓与专家绘制的轮廓的比较分别产生2.23 ± 0.53 mm和0.91 ± 0.02的点-曲线误差和Dice指数。手动和自动获得的相对上坡参数之间的平均差异无统计学意义（P = .37）。此外，每层的分析时间从20分钟（手动）减少到1.5分钟（自动），结论：我们提出了一种自动方法，显着减少了分析首过心脏磁共振灌注图像所需的时间。当AO轮廓与专家绘制的轮廓进行比较时，所提出的方法的鲁棒性和准确性通过灌注参数的高空间对应性和统计学上不显著的差异来证明。
Rationale and Objectives: Derivation of diagnostically relevant parameters from first-pass myocardial perfusion magnetic resonance images involves the tedious and time-consuming manual segmentation of the myocardium in a large number of images. To reduce the manual interaction and expedite the perfusion analysis, we propose an automatic registration and segmentation method for the derivation of perfusion linked parameters.Materials and Methods: A complete automation was accomplished by first registering misaligned images using a method based on independent component analysis, and then using the registered data to automatically segment the myocardium with active appearance models. We used 18 perfusion studies (100 images per study) for validation in which the automatically obtained (AO) contours were compared with expert drawn contours on the basis of point-to-curve error, Dice index, and relative perfusion upslope in the myocardium.Results: Visual inspection revealed successful segmentation in 15 out of 18 studies. Comparison of the AO contours with expert drawn contours yielded 2.23 0.53 mm and 0.91 +/- 0.02 as point-to-curve error and Dice index, respectively. The average difference between manually and automatically obtained relative upslope parameters was found to be statistically insignificant (P = .37). Moreover, the analysis time per slice was reduced from 20 minutes (manual) to 1.5 minutes (automatic),Conclusion: We proposed an automatic method that significantly reduced the time required for analysis of first-pass cardiac magnetic resonance perfusion images. The robustness and accuracy of the proposed method were demonstrated by the high spatial correspondence and statistically insignificant difference in perfusion parameters, when AO contours were compared with expert drawn contours.