IEEE Trans Med Imaging. 2018 Aug;37(8):1877-1886. doi: 10.1109/TMI.2018.2810778. Epub 2018 Feb 28.
We present a novel technique for real-time deformable registration of 3-D to 2.5-D transrectal ultrasound (TRUS) images for image-guided, robot-assisted laparoscopic radical prostatectomy (RALRP). For RALRP, a pre-operatively acquired 3-D TRUS image is registered to thin-volumes comprised of consecutive intra-operative 2-D TRUS images, where the optimal transformation is found using a gradient descent method based on analytical first and second order derivatives. Our method relies on an efficient algorithm for real-time extraction of arbitrary slices from a 3-D image deformed given a discrete mesh representation. We also propose and demonstrate an evaluation method that generates simulated models and images for RALRP by modeling tissue deformation through patient-specific finite-element models (FEM). We evaluated our method on in-vivo data from 11 patients collected during RALRP and focal therapy interventions. In the presence of an average landmark deformation of 3.89 and 4.62 mm, we achieved accuracies of 1.15 and 0.72 mm, respectively, on the synthetic and in-vivo data sets, with an average registration computation time of 264 ms, using MATLAB on a conventional PC. The results show that the real-time tracking of the prostate motion and deformation is feasible, enabling a real-time augmented reality-based guidance system for RALRP.].
我们提出了一种新的实时变形配准技术，用于将 3-D 到 2.5-D 经直肠超声（TRUS）图像配准到图像引导、机器人辅助腹腔镜前列腺根治性切除术（RALRP）。对于 RALRP，术前获取的 3-D TRUS 图像与连续术中 2-D TRUS 图像组成的薄层体积进行配准，其中使用基于解析一阶和二阶导数的梯度下降方法找到最佳变换。我们的方法依赖于一种从给定离散网格表示的变形 3-D 图像中实时提取任意切片的高效算法。我们还提出并演示了一种评估方法，通过对患者特定的有限元模型（FEM）进行建模来生成模拟模型和 RALRP 的图像。我们在 RALRP 和局灶性治疗干预期间从 11 名患者采集的体内数据上评估了我们的方法。在平均标记点变形 3.89 和 4.62 毫米的情况下，我们在合成数据集和体内数据集上分别达到了 1.15 和 0.72 毫米的精度，使用 MATLAB 在常规 PC 上的平均注册计算时间为 264 毫秒。结果表明，前列腺运动和变形的实时跟踪是可行的，这为 RALRP 提供了一种实时的基于增强现实的引导系统。
