Cao Xiaohuan, Yang Jianhua, Gao Yaozong, Wang Qian, Shen Dinggang
IEEE Trans Image Process. 2018 Mar 30. doi: 10.1109/TIP.2018.2820424.
Registration of pelvic CT and MRI is highly desired as it can facilitate effective fusion of two modalities for prostate cancer radiation therapy, i.e., using CT for dose planning and MRI for accurate organ delineation. However, due to the large inter-modality appearance gaps and the high shape/appearance variations of pelvic organs, the pelvic CT/MRI registration is highly challenging. In this paper, we propose a region-adaptive deformable registration method for multi-modal pelvic image registration. Specifically, to handle the large appearance gaps, we first perform both CT-to-MRI and MRI-to-CT image synthesis by multi-target regression forest (MT-RF). Then, to use the complementary anatomical information in the two modalities for steering the registration, we select key points automatically from both modalities and use them together for guiding correspondence detection in the region-adaptive fashion. That is, we mainly use CT to establish correspondences for bone regions, and use MRI to establish correspondences for soft tissue regions. The number of key points is increased gradually during the registration, to hierarchically guide the symmetric estimation of the deformation fields. Experiments for both intra-subject and inter-subject deformable registration show improved performances compared with state-of-the-art multi-modal registration methods, which demonstrate the potentials of our method to be applied for the routine prostate cancer radiation therapy.
骨盆CT和MRI的配准非常必要，因为它有助于将两种模态有效地融合用于前列腺癌放射治疗，即使用CT进行剂量规划，使用MRI进行精确的器官轮廓勾画。然而，由于模态间外观差异大以及骨盆器官的形状/外观变化高，骨盆CT/MRI配准极具挑战性。在本文中，我们提出一种用于多模态骨盆图像配准的区域自适应可变形配准方法。具体而言，为了处理大的外观差异，我们首先通过多目标回归森林（MT-RF）进行CT到MRI以及MRI到CT的图像合成。然后，为了利用两种模态中的互补解剖信息来引导配准，我们从两种模态中自动选择关键点，并以区域自适应的方式将它们一起用于指导对应关系检测。也就是说，我们主要使用CT来建立骨区域的对应关系，使用MRI来建立软组织区域的对应关系。在配准过程中关键点的数量逐渐增加，以分层引导变形场的对称估计。与现有多模态配准方法相比，针对受试者内和受试者间可变形配准的实验均显示出性能的提升，这证明了我们的方法应用于常规前列腺癌放射治疗的潜力。
