Zakkaroff Constantine, Biglands John D, Greenwood John P, Plein Sven, Boyle Roger D, Radjenovic Aleksandra, Magee Derek R
University of Leeds , School of Computing, Leeds LS2 9JT, United Kingdom.
University of Leeds, Division of Medical Physics, Leeds LS2 9JT, United Kingdom; University of Leeds, Multidisciplinary Cardiovascular Research Centre and Leeds Institute of Cardiovascular and Metabolic Medicine, Leeds LS2 9JT, United Kingdom.
J Med Imaging (Bellingham). 2016 Apr;3(2):024002. doi: 10.1117/1.JMI.3.2.024002. Epub 2016 May 13.
Respiratory motion is a significant obstacle to the use of quantitative perfusion in clinical practice. Increasingly complex motion correction algorithms are being developed to correct for respiratory motion. However, the impact of these improvements on the final diagnosis of ischemic heart disease has not been evaluated. The aim of this study was to compare the performance of four automated correction methods in terms of their impact on diagnostic accuracy. Three strategies for motion correction were used: (1) independent translation correction for all slices, (2) translation correction for the basal slice with transform propagation to the remaining two slices assuming identical motion in the remaining slices, and (3) rigid correction (translation and rotation) for the basal slice. There were no significant differences in diagnostic accuracy between the manual and automatic motion-corrected datasets ([Formula: see text]). The area under the curve values for manual motion correction and automatic motion correction were 0.93 and 0.92, respectively. All of the automated motion correction methods achieved a comparable diagnostic accuracy to manual correction. This suggests that the simplest automated motion correction method (method 2 with translation transform for basal location and transform propagation to the remaining slices) is a sufficiently complex motion correction method for use in quantitative myocardial perfusion.
呼吸运动是临床实践中使用定量灌注的一个重大障碍。人们正在开发越来越复杂的运动校正算法来校正呼吸运动。然而，这些改进对缺血性心脏病最终诊断的影响尚未得到评估。本研究的目的是比较四种自动校正方法在对诊断准确性影响方面的性能。使用了三种运动校正策略：(1) 对所有切片进行独立平移校正；(2) 对基底切片进行平移校正，并假设其余切片运动相同，将变换传播到其余两个切片；(3) 对基底切片进行刚性校正（平移和旋转）。手动和自动运动校正数据集之间的诊断准确性没有显著差异（[公式：见原文]）。手动运动校正和自动运动校正的曲线下面积值分别为0.93和0.92。所有自动运动校正方法都达到了与手动校正相当的诊断准确性。这表明最简单的自动运动校正方法（方法2，对基底位置进行平移变换并将变换传播到其余切片）是一种足够复杂的运动校正方法，可用于定量心肌灌注。
