首页> 外文期刊>Ultrasound in Medicine and Biology >A NOVEL ELASTOGRAPHIC FRAME QUALITY INDICATOR AND ITS USE IN AUTOMATIC REPRESENTATIVE-FRAME SELECTION FROM A CINE LOOP
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A NOVEL ELASTOGRAPHIC FRAME QUALITY INDICATOR AND ITS USE IN AUTOMATIC REPRESENTATIVE-FRAME SELECTION FROM A CINE LOOP

机译:一种新的弹性框架质量指示器及其在CINE循环中的自动代表框架选择

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This study was aimed at developing a method for automatically selecting a few representative frames from several hundred axial-shear strain elastogram frames typically obtained during freehand compression elastography of the breast in vivo. This may also alleviate some inter-observer variations that arise at least partly because of differences in selection of representative frames from a cine loop for evaluation and feature extraction. In addition to the correlation coefficient and frame-average axial strain that have been previously used as quality indicators for axial strain elastograms, we incorporated the angle of compression, which has unique effects on axial-shear strain elastogram interpretation. These identified quality factors were computed for every frame in the elastographic cine loop. The algorithm identifies the section having N contiguous frames (N = 10) that possess the highest cumulative quality scores from the cine loop as the one containing representative frames. Data for total of 40 biopsy-proven malignant or benign breast lesions in vivo were part of this study. The performance of the automated algorithm was evaluated by comparing its selection against that by trained radiologists. The observer-identified frame that consisted of a sonogram, axial strain elastogram and axial-shear strain elastogram was compared with the respective images in the frames of the algorithm-identified section using cross-correlation as a similarity measure. It was observed that there was, on average (similar to standard deviation), 82.2% (similar to 2.2%), 83.4% (similar to 3.8%) and 78.4% (similar to 3.6%) correlation between corresponding images of the observer-selected and algorithm-selected frames, respectively. The results indicate that the automatic frame selection method described heremay provide an objective way to select a representative frame while saving time for the radiologist. Furthermore, the frame quality metric described and used here can be displayed in real time as feedback to guide elastographic data acquisition and for training purposes. (E-mail: akthittai@iitm.ac.in) (C) 2016 World Federation for Ultrasound in Medicine & Biology.
机译:该研究旨在开发一种用于自动选择来自乳房的手法压缩弹性显影期间的几百轴剪切应变弹性图框架的少数代表框架的方法。这也可以减轻一些观察者间变异,其至少部分地出现,这是由于来自Cine环路的代表帧的选择差异进行评估和特征提取。除了先前用作轴向应变弹性图的质量指示剂的相关系数和框架平均轴向应变之外,我们纳入了压缩角度,这对轴向剪切应变弹性图解释具有独特的效果。这些识别的质量因子被计算为弹性剪切环路循环中的每个帧。该算法识别具有N连续帧(n = 10)的部分,该部分具有与Cine Loop的最高累积质量分数为包含代表帧的最高累积质量分数。体内共有40个活检的恶性肿瘤或良性乳腺病变的数据是本研究的一部分。通过将其选择与培训的放射科学家的选择进行比较来评估自动算法的性能。将由互相关部分的帧识别部分的帧中的各个图像与超相关作为相似度量进行比较了由超声图,轴向应变弹性图和轴剪应变弹性图组成的观察者识别的帧。观察到,平均(类似于标准偏差),82.2%(类似于2.2%),83.4%(类似于3.8%)和观察者的相应图像之间的相应图像与78.4%(类似于3.6%)相关性 - 分别选择和算法选择帧。结果表明,自动帧选择方法描述了Heremay提供了一种客观的方式来选择代表性框架,同时节省放射科学家的时间。此外,这里描述和使用的帧质量度量可以实时显示为反馈以指导弹性图数据采集和用于训练目的。 (电子邮件:akthittai@iitm.ac.in)(c)2016 2016年中国超声联联合会在医学与生物学中。

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