Cao Xiaohuan, Gao Yaozong, Yang Jianhua, Wu Guorong, Shen Dinggang
School of Automation, Northwestern Polytechnical University, Xi'an, China.
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Med Image Comput Comput Assist Interv. 2016 Oct;9902:1-9. doi: 10.1007/978-3-319-46726-9_1. Epub 2016 Oct 2.
Computed tomography (CT) is widely used for dose planning in the radiotherapy of prostate cancer. However, CT has low tissue contrast, thus making manual contouring difficult. In contrast, magnetic resonance (MR) image provides high tissue contrast and is thus ideal for manual contouring. If MR image can be registered to CT image of the same patient, the contouring accuracy of CT could be substantially improved, which could eventually lead to high treatment efficacy. In this paper, we propose a learning-based approach for multimodal image registration. First, to fill the appearance gap between modalities, a structured random forest with auto-context model is learnt to synthesize MRI from CT and vice versa. Then, MRI-to-CT registration is steered in a dual manner of registering images with same appearances, i.e., (1) registering the synthesized CT with CT, and (2) also registering MRI with the synthesized MRI. Next, a dual-core deformation fusion framework is developed to iteratively and effectively combine these two registration results. Experiments on pelvic CT and MR images have shown the improved registration performance by our proposed method, compared with the existing non-learning based registration methods.
计算机断层扫描（CT）在前列腺癌放射治疗的剂量规划中被广泛应用。然而，CT的组织对比度较低，因此手动勾勒轮廓较为困难。相比之下，磁共振（MR）图像具有较高的组织对比度，因此非常适合手动勾勒轮廓。如果能将MR图像与同一患者的CT图像配准，CT的轮廓勾勒精度将得到显著提高，最终可能带来更高的治疗效果。在本文中，我们提出一种基于学习的多模态图像配准方法。首先，为了填补模态之间的外观差异，学习一个带有自动上下文模型的结构化随机森林，用于从CT合成MRI，反之亦然。然后，以一种对具有相同外观的图像进行配准的双重方式来引导MRI到CT的配准，即：（1）将合成的CT与CT进行配准，以及（2）也将MRI与合成的MRI进行配准。接下来，开发了一个双核变形融合框架，以迭代且有效地结合这两个配准结果。盆腔CT和MR图像实验表明，与现有的非基于学习的配准方法相比，我们提出的方法具有更好的配准性能。
