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使用 T2 加权和动态对比增强 T1 加权 MRI 对前列腺外周区病变进行计算机辅助分析。

Computer-assisted analysis of peripheral zone prostate lesions using T2-weighted and dynamic contrast enhanced T1-weighted MRI.

机构信息

Department of Radiology, Radboud University Medical Centre, Nijmegen, 6525GA, The Netherlands.

出版信息

Phys Med Biol. 2010 Mar 21;55(6):1719-34. doi: 10.1088/0031-9155/55/6/012. Epub 2010 Mar 2.

Abstract

In this study, computer-assisted analysis of prostate lesions was researched by combining information from two different magnetic resonance (MR) modalities: T2-weighted (T2-w) and dynamic contrast-enhanced (DCE) T1-w images. Two issues arise when incorporating T2-w images in a computer-aided diagnosis (CADx) system: T2-w values are position as well as sequence dependent and images can be misaligned due to patient movement during the acquisition. A method was developed that computes T2 estimates from a T2-w and proton density value and a known sequence model. A mutual information registration strategy was implemented to correct for patient movement. Global motion is modelled by an affine transformation, while local motion is described by a volume preserving non-rigid deformation based on B-splines. The additional value to the discriminating performance of a DCE T1-w-based CADx system was evaluated using bootstrapped ROC analysis. T2 estimates were successfully computed in 29 patients. T2 values were extracted and added to the CADx system from 39 malignant, 19 benign and 29 normal annotated regions. T2 values alone achieved a diagnostic accuracy of 0.85 (0.77-0.92) and showed a significantly improved discriminating performance of 0.89 (0.81-0.95), when combined with DCE T1-w features. In conclusion, the study demonstrated a simple T2 estimation method that has a diagnostic performance such that it complements a DCE T1-w-based CADx system in discriminating malignant lesions from normal and benign regions. Additionally, the T2 estimate is beneficial to visual inspection due to the removed coil profile and fixed window and level settings.

摘要

在这项研究中,通过结合两种不同的磁共振(MR)模态的信息:T2 加权(T2-w)和动态对比增强(DCE)T1-w 图像,研究了前列腺病变的计算机辅助分析。将 T2-w 图像纳入计算机辅助诊断(CADx)系统时会出现两个问题:T2-w 值既依赖于位置又依赖于序列,并且由于患者在采集过程中的运动,图像可能会错位。开发了一种从 T2-w 和质子密度值以及已知序列模型计算 T2 估计值的方法。实施了互信息配准策略来纠正患者运动。全局运动由仿射变换建模,而局部运动由基于 B 样条的体积保持非刚性变形描述。使用 bootstrap ROC 分析评估了 DCE T1-w 为基础的 CADx 系统的判别性能的附加价值。在 29 名患者中成功计算了 T2 估计值。从 39 个恶性、19 个良性和 29 个正常注释区域中提取并添加了 T2 值到 CADx 系统。T2 值本身的诊断准确性为 0.85(0.77-0.92),当与 DCE T1-w 特征结合使用时,显示出显著改善的判别性能为 0.89(0.81-0.95)。总之,该研究表明,一种简单的 T2 估计方法具有诊断性能,可以补充基于 DCE T1-w 的 CADx 系统,用于区分恶性病变与正常和良性区域。此外,由于去除了线圈轮廓和固定的窗口和水平设置,T2 估计值有助于视觉检查。

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