一种用于分析动态对比增强磁共振成像（DSCE）图像并应用于肿瘤分级的新方法。

A novel method for analyzing DSCE-images with an application to tumor grading.

作者信息

Slotboom Johannes, Schaer Ralph, Ozdoba Christoph, Reinert Michael, Vajtai Istvan, El-Koussy Marwan, Kiefer Claus, Zbinden Martin, Schroth Gerhard, Wiest Roland

机构信息

Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland.

出版信息

Invest Radiol. 2008 Dec;43(12):843-53. doi: 10.1097/RLI.0b013e3181893605.

DOI:10.1097/RLI.0b013e3181893605

PMID:19002056

Abstract

OBJECTIVES

(a) The development of a novel analysis method, named Dynamic pixel intensity Histogram Analysis (DHA) allowing for pixel intensity-histogram-model-parameter fitting of arbitrary-shaped regions defined in dynamic-susceptibility-contrast-enhanced (DSCE) difference MR-image time-series, and (b) its prospective application and evaluation for glioma grading.

MATERIALS AND METHODS

For each difference-image, pixel intensity histograms of arbitrary-shaped ROIs were computed and fitted using the Levenberg-Marquardt algorithm. Time-dependent histogram center-position- and width-parameters are computed during bolus-passage. The method was applied to 25 patients with low and high grade gliomas.

RESULTS

During bolus outflow-time, histogram-center-position-parameter and histogram-width-parameter reach highest significance levels and discriminate gliomas of different grades. The histogram center-position-parameter discriminated grade-II from grade-III, grade-II from grade-IV but not grade-III from grade-IV. The observed histogram width-parameters discriminated grade-II from grade-III (P < 0.00022), grade-II from grade-IV (P <8.3 10), and grade-III from grade-IV (P < 0.00063).

CONCLUSIONS

DHA is a easy-to-use method for glioma grading; the histogram width parameter is best indicator for histologic grade.

摘要

目的

(a) 开发一种名为动态像素强度直方图分析（DHA）的新型分析方法，该方法能够对动态对比增强（DSCE）差异磁共振成像时间序列中定义的任意形状区域进行像素强度直方图模型参数拟合；(b) 对其在胶质瘤分级中的前瞻性应用和评估。

材料与方法

对于每个差异图像，使用Levenberg-Marquardt算法计算并拟合任意形状感兴趣区域（ROI）的像素强度直方图。在团注通过期间计算随时间变化的直方图中心位置和宽度参数。该方法应用于25例低级别和高级别胶质瘤患者。

结果

在团注流出时间内，直方图中心位置参数和直方图宽度参数达到最高显著水平，并能区分不同级别的胶质瘤。直方图中心位置参数能区分二级与三级、二级与四级胶质瘤，但不能区分三级与四级胶质瘤。观察到的直方图宽度参数能区分二级与三级（P < 0.00022）、二级与四级（P < 8.3×10）以及三级与四级（P < 0.00063）胶质瘤。