Sisniega A, Stayman J W, Cao Q, Yorkston J, Siewerdsen J H, Zbijewski W
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA.
Carestream Health, Rochester, NY USA.
Proc SPIE Int Soc Opt Eng. 2016 Feb 27;9783. doi: 10.1117/12.2217243. Epub 2016 Mar 22.
Cone-beam CT (CBCT) of the extremities provides high spatial resolution, but its quantitative accuracy may be challenged by involuntary sub-mm patient motion that cannot be eliminated with simple means of external immobilization. We investigate a two-step iterative motion compensation based on a multi-component metric of image sharpness.
Motion is considered with respect to locally rigid motion within a particular region of interest, and the method supports application to multiple locally rigid regions. Motion is estimated by maximizing a cost function with three components: a gradient metric encouraging image sharpness, an entropy term that favors high contrast and penalizes streaks, and a penalty term encouraging smooth motion. Motion compensation involved initial coarse estimation of gross motion followed by estimation of fine-scale displacements using high resolution reconstructions. The method was evaluated in simulations with synthetic motion (1-4 mm) applied to a wrist volume obtained on a CMOS-based CBCT testbench. Structural similarity index (SSIM) quantified the agreement between motion-compensated and static data. The algorithm was also tested on a motion contaminated patient scan from dedicated extremities CBCT.
Excellent correction was achieved for the investigated range of displacements, indicated by good visual agreement with the static data. 10-15% improvement in SSIM was attained for 2-4 mm motions. The compensation was robust against increasing motion (4% decrease in SSIM across the investigated range, compared to 14% with no compensation). Consistent performance was achieved across a range of noise levels. Significant mitigation of artifacts was shown in patient data.
The results indicate feasibility of image-based motion correction in extremities CBCT without the need for a priori motion models, external trackers, or fiducials.
四肢锥形束CT（CBCT）提供了高空间分辨率，但其定量准确性可能会受到患者亚毫米级非自主运动的挑战，而这种运动无法通过简单的外部固定方法消除。我们研究了一种基于图像清晰度多分量度量的两步迭代运动补偿方法。
考虑特定感兴趣区域内的局部刚体运动，该方法支持应用于多个局部刚体区域。通过最大化一个具有三个分量的成本函数来估计运动：一个鼓励图像清晰度的梯度度量、一个有利于高对比度并惩罚条纹的熵项，以及一个鼓励平滑运动的惩罚项。运动补偿包括对总体运动的初始粗略估计，然后使用高分辨率重建估计精细尺度位移。该方法在模拟中进行了评估，将合成运动（1 - 4毫米）应用于在基于CMOS的CBCT测试平台上获得的腕部容积数据。结构相似性指数（SSIM）量化了运动补偿后数据与静态数据之间的一致性。该算法还在来自专用四肢CBCT的受运动污染的患者扫描数据上进行了测试。
在所研究的位移范围内实现了出色的校正，与静态数据在视觉上具有良好的一致性。对于2 - 4毫米的运动，SSIM提高了10 - 15%。该补偿对于增加的运动具有鲁棒性（在所研究的范围内，SSIM下降4%，而无补偿时为14%）。在一系列噪声水平下都实现了一致的性能。在患者数据中显示出伪影得到了显著减轻。
结果表明在四肢CBCT中基于图像的运动校正具有可行性，无需先验运动模型、外部跟踪器或基准标记。
