Department of Orthopaedics, University Medical Center Utrecht, Utrecht, The Netherlands.
Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
Phys Med Biol. 2021 Aug 31;66(17). doi: 10.1088/1361-6560/ac1769.
To develop a method that enables computed tomography (CT) to magnetic resonance (MR) image registration of complex deformations typically encountered in rotating joints such as the knee joint.We propose a workflow, denoted quaternion interpolated registration (QIR), consisting of three steps, which makes use of prior knowledge of tissue properties to initialise deformable registration. In the first step, the rigid skeletal components were individually registered. Next, the deformation of soft tissue was estimated using a dual quaternion-based interpolation method. In the final step, the registration was fine-tuned with a rigidity-constrained deformable registration step. The method was applied to paired, unregistered CT and MR images of the knee of 92 patients. It was compared to registration using B-Splines (BS) and B-Splines with a rigidity penalty (BSRP). Registration accuracy was evaluated using mutual information, and by calculating Dice similarity coefficient (DSC), mean absolute surface distance (MASD) and 95th percentile Hausdorff distance (HD95) on bone, and DSC on water and fat dominated tissue. To evaluate the rigidity of bone in the registration, the Jacobian determinant (JD) was calculated.QIR achieved improved results with 0.93, 0.76 mm and 1.88 mm on the DSC, MASD and HD95 metrics on bone, compared to 0.87, 1.40 mm and 4.99 mm for method and 0.87, 1.40 mm and 3.56 mm for the BSRP method. The average DSC of water and fat was 0.77 and 0.86 for the QIR, 0.75 and 0.84 for BS and 0.74 and 0.84 for BSRP. Comparison of the median JD and median interquartile (IQR) ranges of the JD indicated that the QIR (1.00 median, 0.03 IQR) resulted in higher rigidity in the rigid skeletal tissues compared to the BS (0.98 median, 0.19 IQR) and BSRP (1.00 median, 0.05 IQR) methods.This study showed that QIR could improve the outcome of complex registration problems, encountered in joints involving rigid and non-rigid bodies such as occur in the knee, as compared to a conventional registration approach.
为了开发一种能够将 CT 图像与磁共振(MR)图像进行配准的方法,以适应旋转关节(如膝关节)中常见的复杂变形。我们提出了一种工作流程,称为四元数插值配准(QIR),它由三个步骤组成,利用组织属性的先验知识来初始化变形配准。在第一步中,单独对刚性骨骼成分进行配准。接下来,使用双四元数插值方法估计软组织的变形。在最后一步中,使用刚性约束的变形配准步骤对配准进行微调。该方法应用于 92 例膝关节的配对、未注册的 CT 和 MR 图像。将其与使用 B 样条(BS)和带刚性惩罚的 B 样条(BSRP)的配准进行了比较。使用互信息评估配准精度,并通过计算骨的 Dice 相似系数(DSC)、平均绝对表面距离(MASD)和 95%Hausdorff 距离(HD95),以及水和脂肪为主的组织的 DSC 来评估配准的准确性。为了评估配准中骨骼的刚性,计算了雅可比行列式(JD)。与方法相比,QIR 在骨的 DSC、MASD 和 HD95 指标上分别达到了 0.93、0.76mm 和 1.88mm,而方法和 BSRP 方法分别为 0.87、1.40mm 和 4.99mm。水和脂肪的平均 DSC 分别为 QIR 的 0.77 和 0.86,BS 的 0.75 和 0.84,以及 BSRP 的 0.74 和 0.84。JD 的中位数和四分位距(IQR)范围的中位数比较表明,与 BS(中位数 0.98,IQR 0.19)和 BSRP(中位数 1.00,IQR 0.05)方法相比,QIR(中位数 1.00,IQR 0.03)在刚性骨骼组织中产生了更高的刚性。本研究表明,与传统的配准方法相比,QIR 可以改善膝关节等包含刚性和非刚性体的关节中复杂配准问题的结果。