Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI
Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI
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DOI：
10.1118/1.2210568
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发表时间：
2006-08-01
期刊：
影响因子：
3.8
通讯作者：
Newstead, Gillian M.
中科院分区：
文献类型：
--
作者：
Chen, Weijie;Giger, Maryellen L.;Newstead, Gillian M.
乳腺动态对比增强磁共振成像（DCE-MRI）作为乳腺X线摄影和超声检查的补充方式，越来越多地用于乳腺癌的检测和诊断。虽然建立了DCE-MRI中动力学曲线的潜在诊断价值，但生成动力学曲线的方法尚未标准化。需要曲线识别的内在原因是乳腺病变中造影剂的摄取通常是不均匀的，特别是在恶性病变中。公认的是，在4D乳腺磁共振（MR）图像中手动选择感兴趣区域以生成动力学曲线是一个耗时的过程，并且具有显著的观察者间和观察者内变异性。我们研究并开发了一种基于模糊C均值（FCM）聚类的技术，用于自动识别乳腺病变的特征动力学曲线，在乳腺的DCE-MRI。动态增强MR图像采用T1加权3D扰相梯度回波序列，Gd-DTPA剂量为0.2 mmol/kg，时间分辨率为69 s。FCM聚类被应用于将分割的3D乳腺病变中的信号-时间曲线自动划分为多个类别（即，原型曲线）。选择初始增强最高的原型曲线作为病变的代表性特征动力学曲线（CKC）。然后从每个特征动力学曲线中提取四个特征，以描述病变动力学的最大对比增强、达峰时间、摄取率和洗脱率。通过受试者操作特征分析评估了在区分良性和恶性病变的任务中的动力学特征的性能。利用121个乳腺病变（77个恶性和44个良性病例）的数据库，发现FCM识别的CKCs的分类性能优于通过对整个病变进行平均获得的曲线，并且与由具有乳腺MRI经验的放射科医生从病变内绘制的区域生成的动力学曲线相似。(C)2006年美国医学物理学家协会。
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast is being used increasingly in the detection and diagnosis of breast cancer as a complementary modality to mammography and sonography. Although the potential diagnostic value of kinetic curves in DCE-MRI is established, the method for generating kinetic curves is not standardized. The inherent reason that curve identification is needed is that the uptake of contrast agent in a breast lesion is often heterogeneous, especially in malignant lesions. It is accepted that manual region of interest selection in 4D breast magnetic resonance (MR) images to generate the kinetic curve is a time-consuming process and suffers from significant inter- and intraobserver variability. We investigated and developed a fuzzy c-means (FCM) clustering-based technique for automatically identifying characteristic kinetic curves from breast lesions in DCE-MRI of the breast. Dynamic contrast-enhanced MR images were obtained using a T1-weighted 3D spoiled gradient echo sequence with Gd-DTPA dose of 0.2 mmol/kg and temporal resolution of 69 s. FCM clustering was applied to automatically partition the signal-time curves in a segmented 3D breast lesion into a number of classes (i.e., prototypic curves). The prototypic curve with the highest initial enhancement was selected as the representative characteristic kinetic curve (CKC) of the lesion. Four features were then extracted from each characteristic kinetic curve to depict the maximum contrast enhancement, time to peak, uptake rate, and washout rate of the lesion kinetics. The performance of the kinetic features in the task of distinguishing between benign and malignant lesions was assessed by receiver operating characteristic analysis. With a database of 121 breast lesions (77 malignant and 44 benign cases), the classification performance of the FCM-identified CKCs was found to be better than that from the curves obtained by averaging over the entire lesion and similar to kinetic curves generated from regions drawn within the lesion by a radiologist experienced in breast MRI. (C) 2006 American Association of Physicists in Medicine.