Patient Motion Detection and Compensation in SPECT

描述（由申请人提供）：患者运动是一个一直存在的潜在原因，可能会限制诊断成像的准确性。这个问题对于像SPECT和PET这样的成像模式尤其重要，因为这些成像模式需要患者长时间保持不动，而多模态成像则需要患者在不同模式之间保持一致。我们的假设是，将视觉跟踪系统（VTS）和成像模式本身的信息整合到迭代重建中，将产生稳健的运动校正策略。通过VTS，我们指的是一种计算系统，它处理患者表面上的反向反射标记物的立体图像，以提供独立于临床成像系统的运动信息源。用这种组合方法对心脏灌注SPECT成像进行补偿研究的患者运动类型包括刚体运动（RBM）、非刚体运动（non-RBM）、呼吸运动（RM）、心脏向上蠕动（由RM变化引起）以及连续多模态成像研究之间的运动。注意，我们以不同的方式校正身体运动和RM，因此我们区分它们作为运动估计的一部分。在这一竞争性更新中，我们建议扩展我们目前的VTS，以包括多模态成像、发射数据一致性和基于非rbm的有限元建模（FEM）校正的应用。志愿者的核磁共振成像将被用来建立外部标记运动与体内心脏运动之间的关系。MRI还将提供不同运动状态下的胸部高分辨率解剖结构，并结合测量标记运动来指导创建现实的SPECT模拟，以改进算法。最初的临床试验将在患者-志愿者中使用重复休息心脏spect研究，其中第一次休息成像研究作为判断患者有意移动的第二次研究纠正成功的标准。我们假设是否成功的最终评估将是医师-观察者ROC研究，比较接受SPECT灌注成像的患者在有和没有运动补偿的情况下冠状动脉疾病（CAD）的检测准确性，心导管置入的结果作为“金”标准。我们认为，我们提出的研究的意义不仅在于通过心脏和非心脏SPECT、PET和多模态成像的运动校正来提高诊断准确性的潜力，而且还在于在成像过程中更好地了解患者的运动。

DESCRIPTION (provided by applicant): Patient motion is an ever-present potential cause of artifacts that can limit the accuracy of diagnostic imaging. The problem is especially significant for imaging modalities such as SPECT and PET, which require the patient to remain motionless for protracted periods of time, and multimodality imaging where patient alignment between the modalities is essential. Our hypothesis is that integrating into iterative reconstruction combined information from a visual-tracking-system (VTS) and the imaging modalities themselves will result in a robust motion-correction strategy. By VTS we mean a computational system that processes stereo-images of retro-reflective markers on the patient surface to provide a source of motion information that is independent of the clinical imaging system(s). The types of patient motion for which compensation will be investigated for cardiac perfusion SPECT imaging with this combined approach are rigid-body motion (RBM), non-rigid-body motion (non-RBM), respiratory motion (RM), cardiac upward creep (which is caused by changes in RM), and motion between sequential multi-modality imaging studies. Note we correct body-motion and RM differently, thus we differentiate between them as part of motion estimation. In this competitive renewal we propose to extend our current VTS to include application with multimodality imaging, emission data-consistency, and finite-element-modeling (FEM) based correction of non-RBM. MRI of volunteers will be employed to establish the relationship between external-marker motion and the motion of the heart within the body. MRI will also provide high-resolution anatomy of the chest at different motion states in combination with measured-marker motion to guide the creation of realistic SPECT simulations for refinement of algorithms. Initial clinical testing will make use of repeat-rest cardiac-SPECT studies in patient-volunteers where the first rest imaging study serves as the standard for judging the success of correction of the second study during which the patients intentionally move. The ultimate assessment of the success of our hypothesis will be physician-observer ROC studies comparing the detection accuracy of coronary artery disease (CAD) with and without motion compensation for patients undergoing SPECT perfusion imaging with the results of cardiac catheterization serving as the "gold" standard. We believe the significance of our proposed investigations lies not only in the potential to improve diagnostic accuracy through motion-correction of both cardiac and non-cardiac SPECT, PET, and multimodality imaging but also in affording a better understanding of patient motion during imaging.