Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel.
Invest Radiol. 2010 Apr;45(4):174-81. doi: 10.1097/RLI.0b013e3181d0a02f.
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).
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.
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.
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.
基于主成分分析（PCA）开发并评估一种用于前列腺动态对比增强 MRI 图像处理的快速、客观和标准化方法。
本研究经机构内部审查委员会批准；患者签署了知情同意书。21 例接受根治性前列腺切除术的活检证实癌症患者在 3T 磁共振成像仪上进行前列腺 MRI 检查。在 10.5 分钟内以高空间分辨率获得 7 个 3 维梯度回波数据集，包括 2 个预对比和 5 个钆喷酸葡胺注射后（0.1mmol/kg）数据集。使用后者方法调整 PCA 特征向量，对动态强度标度（IS）和增强标度（ES）数据集的 PCA 进行分析，并采用 3 点（3TP）方法进行分析。
对 7 个 IS 数据集和 6 个 ES 数据集进行 PCA 处理，得到相应的特征向量和特征值。由于不均匀的表面线圈接收轮廓导致预对比与第一次对比后之间的信号变化，第一个 IS 特征向量捕获了主要的信号方差，第一个 IS 特征向量捕获了主要的信号方差。接下来的 2 个 IS 特征向量和 2 个主导的 ES 特征向量捕获了组织对比度增强引起的信号变化，而剩余的特征向量捕获了噪声变化。通过旋转这些特征向量，使其与根据 3TP 方法定义的洗脱动力学参数相吻合。特征向量和旋转角度在患者之间具有高度可重复性，从而能够计算出用于新病例的快速、客观且符合诊断要求的旋转特征向量基，以快速计算出旋转特征向量基。我们发现，对于事先选定的前列腺癌患者，IS-第二特征向量的投影系数在检测活检证实的癌症方面具有更高的准确性（敏感性 94%，特异性 67%，阳性预测值 80%，阴性预测值 89%），优于 ES-第二特征向量的旋转和非旋转投影系数。
与生理参数相关的 PCA 调整选择一个主要特征向量，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、标准化的方法，用于可视化前列腺癌。
