Chang W, Henkin R E, Hale D J, Hall D
Semin Nucl Med. 1980 Jan;10(1):39-53. doi: 10.1016/s0001-2998(80)80028-6.
The ability to accurately and reproducibly measure left ventricular performance offers significant clinical advantages in patient management. Specifically, data on wall motion of the left ventricle, the characteristics and shape of the left ventricular volume curve, and measurement of ejection fraction are the general parameters of interest evaluated. These parameters may be measured with either first-pass studies or gated equilibrium blood pool images. Either method is relatively simple, economical, and presents little risk to the patient. Over the last several years both methods have undergone considerable study, and relatively standardized techniques for the two methods exist at present. Both techniques require moderate to extensive data processing. In general, a region of interest (ROI) must be defined before further quantitative analysis is possible. There are at present multiple approaches to the establishment of an ROI for the left ventricle. The major differences between these approaches is in the algorithms used to generate the boundary of the ROI or "the edge". In order for the computer to recognize the edge of the left ventricle, objective and reproducible edge-detection processes are needed. It is the purpose of this paper to review computerized edge-detection algorithms as they apply particularly to the noisy and blurry images obtained in nuclear medicine studies.
准确且可重复地测量左心室功能的能力在患者管理中具有显著的临床优势。具体而言，左心室壁运动的数据、左心室容积曲线的特征和形状以及射血分数的测量是所评估的一般关注参数。这些参数可以通过首次通过研究或门控平衡血池图像来测量。这两种方法都相对简单、经济，且对患者风险较小。在过去几年中，这两种方法都经过了大量研究，目前存在相对标准化的技术。这两种技术都需要适度到广泛的数据处理。一般来说，在进行进一步的定量分析之前，必须定义感兴趣区域（ROI）。目前有多种建立左心室ROI的方法。这些方法之间的主要区别在于用于生成ROI边界或“边缘”的算法。为了使计算机识别左心室的边缘，需要客观且可重复的边缘检测过程。本文的目的是回顾计算机化边缘检测算法，因为它们特别适用于核医学研究中获得的噪声和模糊图像。
