Lawler Leo P, Wood Susan A, Pannu Harpreet K, Fishman Elliot K
Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, JHOC 3254, 601 North Caroline Street, Baltimore, MD 21287-0801, USA.
J Digit Imaging. 2003 Sep;16(3):251-61. doi: 10.1007/s10278-003-1654-y. Epub 2003 Dec 15.
The continued revolution in multidetector-row CT (MDCT) scanning increases the quality of lung imaging but at the cost of a greater burden of data for review and interpretation. This article discusses our preliminary experience with prototype software for lung nodule detection and characterization using MDCT data sets. We discuss the potential role of computer-assisted detection (CAD) as applied to the automatic detection of lung nodules. We also review the process of CAD, outline its potential results, and explore how it may fit into existing radiology practice. Finally, we discuss MDCT data-acquisition parameters and how they may affect the performance of CAD.
多排探测器CT（MDCT）扫描技术的持续革新提高了肺部成像质量，但代价是增加了用于回顾和解读的数据负担。本文讨论了我们使用MDCT数据集对肺部结节进行检测和特征分析的原型软件的初步经验。我们探讨了计算机辅助检测（CAD）在肺部结节自动检测中的潜在作用。我们还回顾了CAD的过程，概述了其潜在结果，并探讨了它如何融入现有的放射学实践。最后，我们讨论了MDCT数据采集参数以及它们如何影响CAD的性能。
