Patriarche Julia Willamena, Erickson Bradley James
Department of Radiology, Mayo Clinic and Foundation, Rochester, MN 55905, USA.
J Digit Imaging. 2007 Sep;20(3):203-22. doi: 10.1007/s10278-006-1038-1.
The goal of this study was to create an algorithm which would quantitatively compare serial magnetic resonance imaging studies of brain-tumor patients. A novel algorithm and a standard classify-subtract algorithm were constructed. The ability of both algorithms to detect and characterize changes was compared using a series of digital phantoms. The novel algorithm achieved a mean sensitivity of 0.87 (compared with 0.59 for classify-subtract) and a mean specificity of 0.98 (compared with 0.92 for classify-subtract) with regard to identification of voxels as changing or unchanging and classification of voxels into types of change. The novel algorithm achieved perfect specificity in seven of the nine experiments. The novel algorithm was additionally applied to a short series of clinical cases, where it was shown to identify visually subtle changes. Automated change detection and characterization could facilitate objective review and understanding of serial magnetic resonance imaging studies in brain-tumor patients.
本研究的目的是创建一种算法，用于对脑肿瘤患者的系列磁共振成像研究进行定量比较。构建了一种新型算法和一种标准的分类减法算法。使用一系列数字模型比较了两种算法检测和表征变化的能力。在确定体素是否发生变化以及将体素分类为变化类型方面，新型算法的平均灵敏度达到0.87（分类减法算法为0.59），平均特异性达到0.98（分类减法算法为0.92）。在九个实验中的七个实验中，新型算法实现了完美的特异性。新型算法还被应用于一小系列临床病例，结果表明它能够识别视觉上细微的变化。自动变化检测和表征有助于对脑肿瘤患者的系列磁共振成像研究进行客观的评估和理解。
