Principal Component Analysis of Dynamic Contrast Enhanced MRI in Human Prostate Cancer
Principal Component Analysis of Dynamic Contrast Enhanced MRI in Human Prostate Cancer
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DOI：
10.1097/rli.0b013e3181d0a02f
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发表时间：
2010-04-01
影响因子：
6.7
通讯作者：
Degani, Hadassa
中科院分区：
文献类型：
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作者：
Eyal, Erez;Bloch, B. Nicolas;Degani, Hadassa
目的：开发和评估一种基于主成分分析（PCA）的快速、客观和标准化的前列腺动态对比增强 MRI 图像处理方法。材料和方法：该研究得到了机构内部审查委员会的批准；获得签署的知情同意书。在根治性前列腺切除术之前，对 21 名活检证实患有癌症的患者进行了 3 特斯拉的前列腺 MRI 扫描。在 10.5 分钟内以高空间分辨率采集了 7 个 3 维梯度回波数据集，其中 2 个为钆喷酸二葡胺注射前和 5 个为注射后 (0.1 mmol/kg)。采用动态强度尺度（IS）和增强尺度（ES）数据集的PCA以及3时间点（3TP）方法的分析，使用后一种方法来调整PCA特征向量。结果：7个IS数据集和6个ES数据集的PCA产生了相应的特征向量和特征值。第一 IS 特征向量捕获了信号方差的主要部分，这是由于不均匀的表面线圈接收轮廓引起的预对比和第一对比后之间的信号变化。接下来的 2 个 IS 特征向量和 2 个主要 ES 特征向量捕获由于组织对比度增强而产生的信号变化，而其余特征向量捕获噪声变化。这些特征向量通过旋转进行调整，以达到与根据 3TP 方法定义的洗入和洗出动力学参数一致。 IS 和 ES 特征向量以及旋转角度在患者之间具有高度可重复性，能够计算通用旋转特征向量基，用于快速、客观地计算新病例的诊断相关投影系数图。我们发现，对于先验选择的前列腺癌患者，IS-2nd 特征向量的投影系数为检测活检证实的癌症提供了比 ES-2nd 旋转和非旋转特征向量的投影系数更高的准确度（94% 灵敏度、67% 特异性、80% ppv 和 89% npv）。结论：PCA 调整为与 生理参数选择主要特征向量，不受不均匀射频场接收轮廓和噪声分量的影响。该特征向量的投影系数图提供了一种快速、客观且标准化的前列腺癌可视化方法。
Objectives: To develop and evaluate a fast, objective and standardized method for image processing of dynamic contrast enhanced MRI of the prostate based on principal component analysis (PCA).Materials and Methods: The study was approved by the institutional internal review board; signed informed consent was obtained. MRI of the prostate at 3 Tesla was performed in 21 patients with biopsy proven cancers before radical prostatectomy. Seven 3-dimensional gradient echo datesets, 2 pre and 5 post-gadopentetate dimeglumine injection (0.1 mmol/kg), were acquired within 10.5 minutes at high spatial resolution. PCA of dynamic intensity-scaled (IS) and enhancement-scaled (ES) datasets and analysis by the 3-time points (3TP) method were applied using the latter method for adjusting the PCA eigenvectors.Results: PCA of 7 IS datasets and 6 ES datasets yielded their corresponding eigenvectors and eigenvalues. The first IS-eigenvector captured the major part of the signal variance because of a signal change between the precontrast and the first postcontrast arising from the inhomogeneous surface coil reception profile. The next 2 IS-eigenvectors and the 2 dominant ES-eigenvectors captured signal changes because of tissue contrast-enhancement, whereas the remaining eigenvectors captured noise changes. These eigenvectors were adjusted by rotation to reach congruence with the wash-in and wash-out kinetic parameters defined according to the 3TP method. The IS and ES-eigenvectors and rotation angles were highly reproducible across patients enabling the calculation of a general rotated eigenvector base that served to rapidly and objectively calculate diagnostically relevant projection coefficient maps for new cases. We found for the a priori selected prostate cancer patients that the projection coefficients of the IS-2nd eigenvector provided a higher accuracy for detecting biopsy proven cancers (94% sensitivity, 67% specificity, 80% ppv, and 89% npv) than the projection coefficients of the ES-2nd rotated and non rotated eigenvectors.Conclusions: PCA adjusted to correlate with physiological parameters selects a dominant eigenvector, free of the inhomogeneous radio-frequency field reception-profile and noise-components. Projection coefficient maps of this eigenvector provide a fast, objective, and standardized means for visualizing prostate cancer.