Arndt S, Swayze V, Cizadlo T, O'Leary D, Cohen G, Yuh W T, Ehrhardt J C, Andreasen N C
Department of Preventive Medicine and Environmental Health, University of Iowa Hospitals and Clinics, Iowa City 52242, USA.
Neuroimage. 1994 Jun;1(3):191-8. doi: 10.1006/nimg.1994.1004.
Developments in imaging technology have made three-dimensional visualization of internal brain structures possible with excellent resolution. Since improved visualization implies improved measurement, these advances hold promise to more accurately measure the volumes of internal structures. As new technologies and techniques emerge, evaluating the relative benefits of measurement methods becomes necessary. We compared and evaluated two methods of estimating volumes from images of brain structures. One method counted pixels within a region of interest, while the other method tessellated the surface between tracings on adjacent slices. Our study assessed both measurement error for true phantom volumes and method disparity for in vivo structures in a randomly selected sample of subjects (n = 100). For our comparisons, we focused on the temporal lobe, ventricular system, and hippocampus. Bias, independence of measurement errors and maximal discrimination of individual differences are properties that are relevant to validating and evaluating measurements of cerebral structure. Pixel counting proved to be the more robust of the two methods, being less sensitive to nuisance-interactions between size of object, shape, and slice thickness. Clinical and research applications of imaging techniques may have distinctive but overlapping needs when evaluating and validating new developments in imaging.
成像技术的发展已使以高分辨率对脑内部结构进行三维可视化成为可能。由于可视化的改善意味着测量的改进，这些进展有望更准确地测量内部结构的体积。随着新技术和技术的出现，评估测量方法的相对优势变得必要。我们比较并评估了两种从脑结构图像估计体积的方法。一种方法是计算感兴趣区域内的像素，而另一种方法是对相邻切片上的描边之间的表面进行三角剖分。我们的研究评估了在随机选择的受试者样本（n = 100）中，真实模型体积的测量误差和体内结构的方法差异。为了进行比较，我们重点关注颞叶、脑室系统和海马体。偏差、测量误差的独立性以及个体差异的最大区分度是与验证和评估脑结构测量相关的特性。事实证明，在这两种方法中，像素计数更为稳健，对物体大小、形状和切片厚度之间的干扰相互作用不太敏感。在评估和验证成像领域的新进展时，成像技术的临床和研究应用可能有独特但重叠的需求。
