骨科创伤手术中骨折复位的多体登记

R. Han, A. Uneri, P. Wu, R. Vijayan, P. Vagdargi, M. Ketcha, N. Sheth, S. Vogt, G. Kleinszig, G. Osgood, J. Siewerdsen
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引用次数: 3

摘要

目的。骨折复位是骨科骨盆创伤手术中一个具有挑战性的部分,如果复位不能准确地恢复自然形态,则导致长期预后不良。手动术前计划是为了获得靶骨的靶转化,即使对急诊护理和透视引导手术的快速工作流程中的专家来说,这一过程也是具有挑战性和耗时的。我们报告了一种使用新的基于图像的配准框架进行骨折复位计划的方法。方法。设计了一个目标函数,将术前通过图形切割方法分割的多体骨碎片同时注册到具有体间碰撞约束的骨盆统计形状模型(SSM)。一种交替优化策略在碎片对齐和SSM自适应之间切换,以解决骨折复位规划中的碎片转换问题。该方法在骨盆图谱上进行了一项留一研究,其中40名成员在左无名骨中模拟了两体和三体骨折,位移范围为0 - 20毫米,0°-15°。结果。实验表明,该配准方法在两体和三体骨折情况下都是可行的。分割后的Dice系数中位数为0.94(0.01四分位间距[IQR]),均方根误差(RMSE)为2.93 mm (0.56 mm IQR)。在两体骨折病例中,骨折复位计划的平移误差为3.8 mm (1.6 mm IQR),旋转误差为2.9°(1.8°IQR)。结论。该方法在5mm范围内实现了精确的骨折复位规划,并有望在未来推广到更复杂的骨折病例。该算法提供了一种从标准工作流程中获得的术前CT图像进行规划的新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-body registration for fracture reduction in orthopaedic trauma surgery
Purpose. Fracture reduction is a challenging part of orthopaedic pelvic trauma procedures, resulting in poor long-term prognosis if reduction does not accurately restore natural morphology. Manual preoperative planning is performed to obtain target transformations of target bones – a process that is challenging and time-consuming even to experts within the rapid workflow of emergent care and fluoroscopically guided surgery. We report a method for fracture reduction planning using a novel image-based registration framework. Method. An objective function is designed to simultaneously register multi-body bone fragments that are preoperatively segmented via a graph-cut method to a pelvic statistical shape model (SSM) with inter-body collision constraints. An alternating optimization strategy switches between fragments alignment and SSM adaptation to solve for the fragment transformations for fracture reduction planning. The method was examined in a leave-one-out study performed over a pelvic atlas with 40 members with two-body and three-body fractures simulated in the left innominate bone with displacements ranging 0–20 mm and 0°–15°. Result. Experiments showed the feasibility of the registration method in both two-body and three-body fracture cases. The segmentations achieved Dice coefficient of median 0.94 (0.01 interquartile range [IQR]) and root mean square error (RMSE) of 2.93 mm (0.56 mm IQR). In two-body fracture cases, fracture reduction planning yielded 3.8 mm (1.6 mm IQR) translational and 2.9° (1.8° IQR) rotational error. Conclusion. The method demonstrated accurate fracture reduction planning within 5 mm and shows promise for future generalization to more complicated fracture cases. The algorithm provides a novel means of planning from preoperative CT images that are already acquired in standard workflow.
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