医学
图像质量
迭代重建
辐射剂量
算法
还原(数学)
对比度(视觉)
噪音(视频)
图像噪声
腹部计算机断层扫描
放射科
医学物理学
对比度增强
人工智能
核医学
图像(数学)
计算机科学
磁共振成像
数学
几何学
作者
Achille Mileto,Luís Guimarães,Cynthia H. McCollough,Joel G. Fletcher,Lifeng Yu
出处
期刊:Radiology
[Radiological Society of North America]
日期:2019-10-29
卷期号:293 (3): 491-503
被引量:153
标识
DOI:10.1148/radiol.2019191422
摘要
The development and widespread adoption of iterative reconstruction (IR) algorithms for CT have greatly facilitated the contemporary practice of radiation dose reduction during abdominal CT examinations. IR mitigates the increased image noise typically associated with reduced radiation dose levels, thereby maintaining subjective image quality and diagnostic confidence for a variety of clinical tasks. Mounting evidence, however, points to important limitations of this method involving radiologists' ability to perform low-contrast diagnostic tasks, such as the detection of liver metastases or pancreatic masses. Radiologists need to be aware that use of IR can result in a decline of spatial resolution for low-contrast structures and degradation of low-contrast detectability when radiation dose reductions exceed approximately 25%. This article will review the principles of IR algorithm technology, describe the various commercial implementations of IR in CT, and review published studies that have evaluated the ability of IR to preserve diagnostic performance for low-contrast diagnostic tasks. In addition, future developments in CT noise reduction techniques and methods to rigorously evaluate their diagnostic performance will be discussed.
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