Faculty of Engineering and IT, University of Sydney, Sydney, Australia. Faculty of Health Sciences and Brain and Mind Centre, University of Sydney, Australia.
Phys Med Biol. 2018 May 17;63(10):105018. doi: 10.1088/1361-6560/aabd48.
Motion-compensated brain imaging can dramatically reduce the artifacts and quantitative degradation associated with voluntary and involuntary subject head motion during positron emission tomography (PET), single photon emission computed tomography (SPECT) and computed tomography (CT). However, motion-compensated imaging protocols are not in widespread clinical use for these modalities. A key reason for this seems to be the lack of a practical motion tracking technology that allows for smooth and reliable integration of motion-compensated imaging protocols in the clinical setting. We seek to address this problem by investigating the feasibility of a highly versatile optical motion tracking method for PET, SPECT and CT geometries. The method requires no attached markers, relying exclusively on the detection and matching of distinctive facial features. We studied the accuracy of this method in 16 volunteers in a mock imaging scenario by comparing the estimated motion with an accurate marker-based method used in applications such as image guided surgery. A range of techniques to optimize performance of the method were also studied. Our results show that the markerless motion tracking method is highly accurate (<2 mm discrepancy against a benchmarking system) on an ethnically diverse range of subjects and, moreover, exhibits lower jitter and estimation of motion over a greater range than some marker-based methods. Our optimization tests indicate that the basic pose estimation algorithm is very robust but generally benefits from rudimentary background masking. Further marginal gains in accuracy can be achieved by accounting for non-rigid motion of features. Efficiency gains can be achieved by capping the number of features used for pose estimation provided that these features adequately sample the range of head motion encountered in the study. These proof-of-principle data suggest that markerless motion tracking is amenable to motion-compensated brain imaging and holds good promise for a practical implementation in clinical PET, SPECT and CT systems.
运动补偿脑成像技术可以显著减少正电子发射断层扫描（PET）、单光子发射计算机断层扫描（SPECT）和计算机断层扫描（CT）中与受试者头部自愿和非自愿运动相关的伪影和定量衰减。然而，运动补偿成像方案并未广泛应用于这些模态。造成这种情况的一个关键原因似乎是缺乏实用的运动跟踪技术，无法在临床环境中平稳可靠地整合运动补偿成像方案。我们通过研究一种适用于 PET、SPECT 和 CT 几何形状的高度通用的光学运动跟踪方法来解决这个问题。该方法不需要附加标记，仅依赖于独特面部特征的检测和匹配。我们通过将估计的运动与在图像引导手术等应用中使用的精确基于标记的方法进行比较，研究了该方法在 16 名模拟成像场景中的志愿者中的可行性。还研究了各种优化方法性能的技术。我们的结果表明，无标记运动跟踪方法在种族多样化的受试者中具有高度准确性（与基准系统相比差异<2mm），而且与一些基于标记的方法相比，它的抖动和运动估计范围更大。我们的优化测试表明，基本姿势估计算法非常稳健，但通常受益于基本的背景屏蔽。通过考虑特征的非刚性运动，可以实现精度的边际提高。通过限制用于姿势估计的特征数量可以提高效率，前提是这些特征充分采样了研究中遇到的头部运动范围。这些初步数据表明，无标记运动跟踪适用于运动补偿脑成像，并为在临床 PET、SPECT 和 CT 系统中实现实用化提供了良好的前景。
