Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, United States of America.
Phys Med Biol. 2020 Jul 17;65(13):135009. doi: 10.1088/1361-6560/ab843c.
Surgical reduction of pelvic dislocation is a challenging procedure with poor long-term prognosis if reduction does not accurately restore natural morphology. The procedure often requires long fluoroscopic exposure times and trial-and-error to achieve accurate reduction. We report a method to automatically compute the target pose of dislocated bones in preoperative CT and provide 3D guidance of reduction using routine 2D fluoroscopy. A pelvic statistical shape model (SSM) and a statistical pose model (SPM) were formed from an atlas of 40 pelvic CT images. Multi-body bone segmentation was achieved by mapping the SSM to a preoperative CT via an active shape model. The target reduction pose for the dislocated bone is estimated by fitting the poses of undislocated bones to the SPM. Intraoperatively, multiple bones are registered to fluoroscopy images via 3D-2D registration to obtain 3D pose estimates from 2D images. The method was examined in three studies: (1) a simulation study of 40 CT images simulating a range of dislocation patterns; (2) a pelvic phantom study with controlled dislocation of the left innominate bone; (3) a clinical case study investigating feasibility in images acquired during pelvic reduction surgery. Experiments investigated the accuracy of registration as a function of initialization error (capture range), image quality (radiation dose and image noise), and field of view (FOV) size. The simulation study achieved target pose estimation with translational error of median 2.3 mm (1.4 mm interquartile range, IQR) and rotational error of 2.1° (1.3° IQR). 3D-2D registration yielded 0.3 mm (0.2 mm IQR) in-plane and 0.3 mm (0.2 mm IQR) out-of-plane translational error, with in-plane capture range of ±50 mm and out-of-plane capture range of ±120 mm. The phantom study demonstrated 3D-2D target registration error of 2.5 mm (1.5 mm IQR), and the method was robust over a large dose range, down to 5 [Formula: see text]Gy/frame (an order of magnitude lower than the nominal fluoroscopic dose). The clinical feasibility study demonstrated accurate registration with both preoperative and intraoperative radiographs, yielding 3.1 mm (1.0 mm IQR) projection distance error with robust performance for FOV ranging from 340 × 340 mm to 170 × 170 mm (at the image plane). The method demonstrated accurate estimation of the target reduction pose in simulation, phantom, and a clinical feasibility study for a broad range of dislocation patterns, initialization error, dose levels, and FOV size. The system provides a novel means of guidance and assessment of pelvic reduction from routinely acquired preoperative CT and intraoperative fluoroscopy. The method has the potential to reduce radiation dose by minimizing trial-and-error and to improve outcomes by guiding more accurate reduction of joint dislocations.
骨盆脱位的手术复位是一项具有挑战性的操作,如果复位不能准确恢复自然形态,其长期预后较差。该操作通常需要长时间的透视暴露时间,并通过反复试验来实现准确的复位。我们报告了一种在术前 CT 中自动计算脱位骨骼目标姿势的方法,并使用常规二维透视术提供复位的 3D 指导。通过从 40 个骨盆 CT 图像的图谱中形成骨盆统计形状模型(SSM)和统计姿势模型(SPM),对多体骨骼进行分割。通过主动形状模型将 SSM 映射到术前 CT 上来实现脱位骨骼的目标复位姿势。通过拟合未脱位骨骼的姿势来估计脱位骨骼的目标复位姿势。术中,通过 3D-2D 配准将多个骨骼配准到透视图像中,从二维图像中获得三维姿势估计。该方法在三项研究中进行了检验:(1)对 40 个 CT 图像的模拟研究,模拟了一系列脱位模式;(2)使用左骨盆骨进行骨盆幻影研究;(3)对骨盆复位手术中获取的图像进行临床案例研究,以调查可行性。实验研究了配准的准确性作为初始化误差(捕获范围)、图像质量(辐射剂量和图像噪声)和视野(FOV)大小的函数。模拟研究达到了目标姿势估计,平移误差中位数为 2.3mm(1.4mm 四分位距(IQR)),旋转误差为 2.1°(1.3° IQR)。3D-2D 配准得到了 0.3mm(0.2mm IQR)的面内和 0.3mm(0.2mm IQR)的面外平移误差,面内捕获范围为±50mm,面外捕获范围为±120mm。幻影研究表明,3D-2D 目标注册误差为 2.5mm(1.5mm IQR),该方法在较大的剂量范围内具有鲁棒性,低至 5[Formula: see text]Gy/帧(比常规透视剂量低一个数量级)。临床可行性研究表明,术前和术中射线照相均能实现准确的配准,具有稳健的性能,FOV 范围从 340×340mm 到 170×170mm(在图像平面上)。该方法在模拟、幻影和临床可行性研究中,针对广泛的脱位模式、初始化误差、剂量水平和 FOV 大小,都能准确估计目标复位姿势。该系统提供了一种从常规获取的术前 CT 和术中透视术中指导和评估骨盆复位的新方法。该方法有可能通过最小化反复试验来降低辐射剂量,并通过指导更准确的关节脱位复位来改善结果。