肝脏四维超声运动补偿的非刚性配准方法验证

S. Vijayan, S. Klein, E. F. Hofstad, F. Lindseth, B. Ystgaard, T. Langø
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引用次数: 26

摘要

未来使用聚焦超声(FUS)治疗腹部器官(如肝脏)肿瘤的治疗,必须结合这些器官由于重力和肠道(蠕动)引起的呼吸和漂移的运动跟踪。需要对目标(如肿瘤)进行运动跟踪,以确保准确定位超声。我们对一种用4D (3D+时间)超声进行肝脏运动跟踪的方法进行了定量验证。离线分析使用最近发布的非刚性配准算法完成,该算法专为动态成像数据的运动估计而设计。该方法以群体优化的方式注册整个4D序列,从而避免了对特定参考时间点的偏差。利用四维自由形式b样条变形模型实现了变换的时空平滑性。在我们的评估中,三名健康志愿者在几个呼吸周期内从腹部的三个不同位置和角度进行扫描(共9次4D扫描)。一个熟练的医生执行扫描和手动注释明确定义的解剖标志,以评估自动算法。四名工程师分别在所有时间框架中对这些点进行注释,其平均值作为黄金标准。将自动运动估计方法的误差与观测器间的可变性进行了比较。注册方法比观察者更好地估计肝脏运动,并且误差(所有数据集的75%百分位数)为1毫米。我们得出的结论是,该方法能够准确地跟踪肝脏在四维超声数据中的运动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Validation of a non-rigid registration method for motion compensation in 4D ultrasound of the liver
Future therapy using focused ultrasound (FUS) to treat tumors in abdominal organs, such as the liver, must incorporate motion tracking of these organs due to breathing and drift caused by gravity and intestines (peristalsis). Motion tracking of the target (e.g. tumor) is needed to ensure accurately located sonications. We have performed a quantitative validation of a methodology for motion tracking of the liver with 4D (3D+time) ultrasound. The offline analysis was done using a recently published non-rigid registration algorithm that was specifically designed for motion estimation from dynamic imaging data. The method registers the entire 4D sequence in a group-wise optimization fashion, thus avoiding a bias towards a specifically chosen reference time point. Both spatial and temporal smoothness of the transformations are enforced by using a 4D free-form B-spline deformation model. For our evaluation, three healthy volunteers were scanned over several breath cycles from three different positions and angles on the abdomen (totally nine 4D scans). A skilled physician performed the scanning and manually annotated well-defined anatomic landmarks for assessment of the automatic algorithm. Four engineers each annotated these points in all time frames, the mean of which was taken as a gold standard. The error of the automatic motion estimation method was compared with inter-observer variability. The registration method estimated liver motion better than the observers and had an error (75% percentile over all datasets) of 1 mm. We conclude that the methodology was able to accurately track the motion of the liver in the 4D ultrasound data.
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