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Motion Compensation in Extremity Cone-Beam CT Using a Penalized Image Sharpness Criterion

机译:使用惩罚图像清晰度准则的四肢锥束CT运动补偿

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摘要

Cone-beam CT (CBCT) for musculoskeletal imaging would benefit from a method to reduce the effects of involuntary patient motion. In particular, the continuing improvement in spatial resolution of CBCT may enable tasks such as quantitative assessment of bone microarchitecture (0.1 mm – 0.2 mm detail size), where even subtle, sub-mm motion blur might be detrimental. We propose a purely image based motion compensation method that requires no fiducials, tracking hardware or prior images. A statistical optimization algorithm (CMA-ES) is used to estimate a motion trajectory that optimizes an objective function consisting of an image sharpness criterion augmented by a regularization term that encourages smooth motion trajectories. The objective function is evaluated using a volume of interest (VOI, e.g. a single bone and surrounding area) where the motion can be assumed to be rigid. More complex motions can be addressed by using multiple VOIs. Gradient variance was found to be a suitable sharpness metric for this application. The performance of the compensation algorithm was evaluated in simulated and experimental CBCT data, and in a clinical dataset. Motion-induced artifacts and blurring were significantly reduced across a broad range of motion amplitudes, from 0.5 mm to 10 mm. Structure Similarity Index (SSIM) against a static volume was used in the simulation studies to quantify the performance of the motion compensation. In studies with translational motion, the SSIM improved from 0.86 before compensation to 0.97 after compensation for 0.5 mm motion, from 0.8 to 0.94 for 2 mm motion and from 0.52 to 0.87 for 10 mm motion (~70% increase). Similar reduction of artifacts was observed in a benchtop experiment with controlled translational motion of an anthropomorphic hand phantom, where SSIM (against a reconstruction of a static phantom) improved from 0.3 to 0.8 for 10 mm motion. Application to a clinical dataset of a lower extremity showed dramatic reduction of streaks and improvement in delineation of tissue boundaries and trabecular structures throughout the whole volume. The proposed method will support new applications of extremity CBCT in areas where patient motion may not be sufficiently managed by immobilization, such as imaging under load and quantitative assessment of subchondral bone architecture.
机译:用于减少肌肉骨骼成像的锥形束CT(CBCT)将受益于减少患者非自愿运动的方法。特别是,CBCT空间分辨率的不断提高可能使诸如骨微结构(0.1毫米至0.2毫米细节尺寸)的定量评估之类的任务得以实现,而即使是微妙的,亚毫米级的运动模糊也可能有害。我们提出了一种完全基于图像的运动补偿方法,该方法不需要基准,跟踪硬件或先前的图像。统计优化算法(CMA-ES)用于估算运动轨迹,该运动轨迹可优化目标函数,该目标函数由图像清晰度标准(由鼓励平滑运动轨迹的正则项扩展)组成。使用感兴趣的体积(VOI,例如单个骨骼和周围区域)评估目标函数,在该体积中可以假定运动是刚性的。可以通过使用多个VOI解决更复杂的运动。发现渐变方差是此应用程序的合适清晰度指标。在模拟和实验CBCT数据以及临床数据集中评估了补偿算法的性能。在从0.5毫米到10毫米的宽范围的运动幅度范围内,运动引起的伪影和模糊现象均得到显着降低。在模拟研究中使用了针对静态体积的结构相似性指数(SSIM)来量化运动补偿的性能。在平移运动的研究中,SSIM从0.5毫米运动的补偿前的0.86提高到0.5毫米运动补偿后的0.97,2 mm运动从0.82提高到0.94,10 mm运动从0.52到0.87(增加了70%)。在模拟人体模型的受控平移运动的台式实验中观察到了类似的伪影减少,其中对于10 mm运动,SSIM(针对静态模型的重建)从0.3改进为0.8。在下肢的临床数据集上的应用显示,横纹显着减少,并且在整个体积中组织边界和小梁结构的轮廓得到了改善。所提出的方法将支持四肢CBCT在无法通过固定来充分控制患者运动的区域中的新应用,例如负重成像和软骨下骨结构的定量评估。

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