基于99mTc-MAG3肾图的肾脏感兴趣区完全自动定义：在正常肾脏患者和疑似肾梗阻患者中的验证

Totally automatic definition of renal regions of interest from 99mTc-MAG3 renograms: validation in patients with normal kidneys and in patients with suspected renal obstruction.

作者信息

Garcia Ernest V, Folks Russell, Pak Samuel, Taylor Andrew

机构信息

Department of Radiology, Emory University School of Medicine, Atlanta, Georgia, USA.

出版信息

Nucl Med Commun. 2010 May;31(5):366-74. doi: 10.1097/MNM.0b013e3283362aa3.

DOI:10.1097/MNM.0b013e3283362aa3

PMID:20145580

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2884994/

Abstract

INTRODUCTION

An image-processing algorithm (AUTOROI) has been developed to totally automatically (or with manual assistance) detect whole-kidney contours and generate renal regions of interest (ROI) for the extraction of the quantitative measurements used in the interpretation of Tc-mercaptoacetyltriglycine (Tc-MAG3) renograms.

METHODS

The 18-20th min dynamic frames post-MAG3 injection were used to automatically define boxes surrounding each kidney, which were then transposed to an early composite image for interpolative and directional background subtraction. Sobel operator and unsharp masking were applied for edge enhancement, and the resulting image histograms were equalized to better define poorly functioning kidneys. AUTOROI searched radially from the center of mass to define each kidney's ROI coordinates. AUTOROI was validated using MAG3 studies from 79 patients referred for suspected obstruction (79 left, 77 right kidneys) and 19 kidney donors with normal kidney function and no obstruction. Renal ROIs were manually defined by a nuclear medicine technologist with 20+ years of experience (reference standard) and an American Board of Nuclear Medicine certified physician. AUTOROI and physician ROIs were automatically compared with the reference standard to determine the border definition error.

RESULTS

AUTOROI totally automatically detected the renal borders in 89% (172 of 194) of the kidneys from the entire group of 98 patients. The 22 kidneys missed automatically were subsequently detected with the assistance of a single manually placed fiducial point demarcating the liver/kidney boundary. These 22 kidneys were shown to be associated with markedly reduced MAG3 clearance. The mean error of AUTOROI for all 194 kidneys was 6.66+/-3.77 and 7.31+/-4.52 mm for the left and right kidney, respectively. The physician's error was 6.78+/-2.42 and 6.65+/-2.05 mm for the left and right kidney, respectively. This error difference between AUTOROI and the physician was not statistically significant.

CONCLUSION

AUTOROI provides an objective and promising approach to automated renal ROI detection.

摘要

引言

已开发出一种图像处理算法（AUTOROI），可完全自动（或在人工辅助下）检测全肾轮廓，并生成感兴趣的肾区（ROI），用于提取在解读锝-巯基乙酰三甘氨酸（Tc-MAG3）肾图时使用的定量测量值。

方法

在注射MAG3后第18 - 20分钟的动态帧用于自动定义围绕每个肾脏的框，然后将其转换到早期合成图像进行插值和定向背景减法。应用Sobel算子和非锐化掩膜进行边缘增强，并对所得图像直方图进行均衡化以更好地定义功能不良的肾脏。AUTOROI从质心径向搜索以定义每个肾脏的ROI坐标。使用来自79例疑似梗阻患者（79个左肾，77个右肾）和19例肾功能正常且无梗阻的肾脏供体的MAG3研究对AUTOROI进行验证。肾ROI由一位有20多年经验的核医学技术人员（参考标准）和一位美国核医学委员会认证的医生手动定义。将AUTOROI和医生定义的ROI与参考标准自动比较以确定边界定义误差。

结果

在98例患者的整个组中，AUTOROI在89%（194个肾脏中的172个）的肾脏中完全自动检测到肾边界。自动遗漏的22个肾脏随后在单个手动放置的用于划定肝/肾边界的基准点的辅助下被检测到。这22个肾脏显示与MAG3清除率显著降低有关。所有194个肾脏的AUTOROI平均误差，左肾为6.66±3.77毫米，右肾为7.31±4.52毫米。医生的误差，左肾为6.78±2.42毫米，右肾为6.65±2.05毫米。AUTOROI与医生之间的这种误差差异无统计学意义。