Fully automated registration of first-pass myocardial perfusion MRI using independent component analysis.
Fully automated registration of first-pass myocardial perfusion MRI using independent component analysis.
复制标题
使用独立成分分析全自动配准首过心肌灌注 MRI。
DOI：
10.1007/978-3-540-73273-0_45
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Lelieveldt,BPF
中科院分区：
文献类型：
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作者：
Milles,J;vanderGeest,RJ;Jerosch-Herold,M;Reiber,JHC;Lelieveldt,BPF
提出了一种新的心脏灌注MRI配准方法。所提出的方法成功地纠正呼吸运动，没有任何手动交互，使用独立分量分析提取生理相关的功能，连同他们的时间强度行为。基于伊卡的结果来计算模仿感兴趣数据中的强度变化的时变参考图像，并将其用于计算由每帧的呼吸引起的位移。该方法的定性和定量验证使用46个临床质量，短轴，灌注MR数据集，每个数据集包括100个图像。验证实验显示平均LV运动从1.26±0.87像素减少到0.64±0.46像素。配准后的时间-强度曲线也有所改善，配准后的数据与人工金标准的平均误差从2.65±7.89%降低到0.87±3.88%。我们的结论是，这种全自动ICA为基础的方法显示出良好的准确性，鲁棒性和计算速度，足以在临床环境中使用。
This paper presents a novel method for registration of cardiac perfusion MRI. The presented method successfully corrects for breathing motion without any manual interaction using Independent Component Analysis to extract physiologically relevant features together with their time-intensity behavior. A time-varying reference image mimicking intensity changes in the data of interest is computed based on the results of ICA, and used to compute the displacement caused by breathing for each frame. Qualitative and quantitative validation of the method is carried out using 46 clinical quality, short-axis, perfusion MR datasets comprising 100 images each. Validation experiments showed a reduction of the average LV motion from 1.26±0.87 to 0.64±0.46 pixels. Time-intensity curves are also improved after registration with an average error reduced from 2.65±7.89% to 0.87±3.88% between registered data and manual gold standard. We conclude that this fully automatic ICA-based method shows an excellent accuracy, robustness and computation speed, adequate for use in a clinical environment.