Reaungamornrat S, De Silva T, Uneri A, Wolinsky J-P, Khanna A J, Kleinszig G, Vogt S, Prince J L, Siewerdsen J H
Department of Computer Science, Johns Hopkins University, Baltimore MD.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD.
Proc SPIE Int Soc Opt Eng. 2016 Feb 27;9786. doi: 10.1117/12.2208621. Epub 2016 Mar 18.
Localization of target anatomy and critical structures defined in preoperative MR images can be achieved by means of multi-modality deformable registration to intraoperative CT. We propose a symmetric diffeomorphic deformable registration algorithm incorporating a modality independent neighborhood descriptor (MIND) and a robust Huber metric for MR-to-CT registration.
The method, called MIND Demons, solves for the deformation field between two images by optimizing an energy functional that incorporates both the forward and inverse deformations, smoothness on the velocity fields and the diffeomorphisms, a modality-insensitive similarity function suitable to multi-modality images, and constraints on geodesics in Lagrangian coordinates. Direct optimization (without relying on an exponential map of stationary velocity fields used in conventional diffeomorphic Demons) is carried out using a Gauss-Newton method for fast convergence. Registration performance and sensitivity to registration parameters were analyzed in simulation, in phantom experiments, and clinical studies emulating application in image-guided spine surgery, and results were compared to conventional mutual information (MI) free-form deformation (FFD), local MI (LMI) FFD, and normalized MI (NMI) Demons.
The method yielded sub-voxel invertibility (0.006 mm) and nonsingular spatial Jacobians with capability to preserve local orientation and topology. It demonstrated improved registration accuracy in comparison to the reference methods, with mean target registration error (TRE) of 1.5 mm compared to 10.9, 2.3, and 4.6 mm for MI FFD, LMI FFD, and NMI Demons methods, respectively. Validation in clinical studies demonstrated realistic deformation with sub-voxel TRE in cases of cervical, thoracic, and lumbar spine.
A modality-independent deformable registration method has been developed to estimate a viscoelastic diffeomorphic map between preoperative MR and intraoperative CT. The method yields registration accuracy suitable to application in image-guided spine surgery across a broad range of anatomical sites and modes of deformation.
通过多模态可变形配准到术中CT，可实现术前MR图像中定义的目标解剖结构和关键结构的定位。我们提出了一种对称的微分同胚可变形配准算法，该算法结合了模态无关邻域描述符（MIND）和用于MR到CT配准的鲁棒Huber度量。
该方法称为MIND Demons，通过优化一个能量泛函来求解两个图像之间的变形场，该能量泛函包含正向和反向变形、速度场和平滑度的平滑性、适用于多模态图像的模态不敏感相似性函数以及拉格朗日坐标中的测地线约束。使用高斯-牛顿法进行直接优化（不依赖于传统微分同胚Demons中使用的固定速度场的指数映射）以实现快速收敛。在模拟、体模实验以及模拟图像引导脊柱手术应用的临床研究中分析了配准性能和对配准参数的敏感性，并将结果与传统互信息（MI）自由形式变形（FFD）、局部MI（LMI）FFD和归一化MI（NMI）Demons进行了比较。
该方法产生了亚体素可逆性（0.006毫米）和非奇异空间雅可比行列式，能够保留局部方向和拓扑结构。与参考方法相比，它显示出更高的配准精度，平均目标配准误差（TRE）为1.5毫米，而MI FFD、LMI FFD和NMI Demons方法分别为10.9、2.3和4.6毫米。临床研究中的验证表明，在颈椎、胸椎和腰椎病例中，变形逼真且TRE为亚体素。
已开发出一种模态无关的可变形配准方法，用于估计术前MR和术中CT之间的粘弹性微分同胚映射。该方法产生的配准精度适用于广泛的解剖部位和变形模式的图像引导脊柱手术应用。
