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A method of motion tracking during CT for motion correction

机译:CT期间用于运动校正的运动跟踪方法

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Patient motion is a significant problem in pediatric PET/CT, and motion correction techniques could potentially remove the need for anaesthesia or sedation that are normally required for very young patients. Effective methods exist for motion correction of neurological PET images, and although there is a scarcity of equivalent methods for CT, a potential method of correcting for rigid head motion during the CT scan has recently been proposed. In this study, we describe a motion tracking method for CT using an optical motion tracking system. Since pose is reported in tracker coordinates, and motion correction requires motion in CT scanner coordinates, a calibration is required to determine the transformation needed to convert between the coordinate systems. We describe such a calibration method, and evaluate it by acquiring two CT scans of a Hoffman 3D brain phantom, with a tracker target attached, in different poses. The applied motion between two scans was calculated from the change in target pose measured by the tracker, and converted to scanner coordinates. This motion was applied to one of the reconstructed image volumes, which was then compared with the other image volume. The mean registration error of the two volumes was estimated from landmark analysis to be less than 0.3 mm on average in all directions, which agreed well with a calculated maximum uncertainty of 0.7 mm. Errors of this magnitude could be acceptable if the CT scans were only used for attenuation correction, but may need to be further reduced for motion correction applications. We anticipate that this will be achievable with improvements to our technique, and intend in future work to use motion data to attempt motion correction in spiral CT studies.
机译:在小儿PET / CT中,患者的运动是一个重大问题,运动校正技术可以潜在地消除非常年轻的患者通常需要的麻醉或镇静作用。存在用于神经学PET图像的运动校正的有效方法,并且尽管缺乏等效的CT方法,但是最近已经提出了在CT扫描期间校正刚性头部运动的潜在方法。在这项研究中,我们描述了使用光学运动跟踪系统的CT运动跟踪方法。由于在跟踪器坐标中报告了姿态,并且运动校正需要在CT扫描仪坐标中进行运动,因此需要进行校准以确定在坐标系之间转换所需的变换。我们描述了这种校准方法,并通过获取霍夫曼3D脑模型的两个CT扫描(带有跟踪器目标)以不同的姿势进行评估,从而对其进行了评估。根据跟踪器测量的目标姿态变化计算两次扫描之间的施加运动,并将其转换为扫描仪坐标。将此运动应用于一个重建的图像体积,然后将其与另一个图像体积进行比较。根据地标分析,两个体积的平均配准误差在各个方向上平均小于0.3 mm,这与计算得出的0.7 mm的最大不确定度非常吻合。如果仅将CT扫描用于衰减校正,则这种大小的误差是可以接受的,但对于运动校正应用,可能需要进一步减小。我们预计这将通过改进我们的技术来实现,并打算在未来的工作中使用运动数据尝试进行螺旋CT研究中的运动校正。

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