Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Department of Radiology, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen 518035, China.
Biomed Res Int. 2020 Jul 10;2020:5615371. doi: 10.1155/2020/5615371. eCollection 2020.
To align multimodal images is important for information fusion, clinical diagnosis, treatment planning, and delivery, while few methods have been dedicated to matching computerized tomography (CT) and magnetic resonance (MR) images of lumbar spine. This study proposes a coarse-to-fine registration framework to address this issue. Firstly, a pair of CT-MR images are rigidly aligned for global positioning. Then, a bending energy term is penalized into the normalized mutual information for the local deformation of soft tissues. In the end, the framework is validated on 40 pairs of CT-MR images from our in-house collection and 15 image pairs from the SpineWeb database. Experimental results show high overlapping ratio (in-house collection, vertebrae 0.97 ± 0.02, blood vessel 0.88 ± 0.07; SpineWeb, vertebrae 0.95 ± 0.03, blood vessel 0.93 ± 0.10) and low target registration error (in-house collection, ≤2.00 ± 0.62 mm; SpineWeb, ≤2.37 ± 0.76 mm) are achieved. The proposed framework concerns both the incompressibility of bone structures and the nonrigid deformation of soft tissues. It enables accurate CT-MR registration of lumbar spine images and facilitates image fusion, spine disease diagnosis, and interventional treatment delivery.
将多模态图像对齐对于信息融合、临床诊断、治疗计划和实施非常重要，然而，很少有方法专门用于匹配计算机断层扫描（CT）和磁共振（MR）腰椎图像。本研究提出了一种粗到精的配准框架来解决这个问题。首先，将一对 CT-MR 图像进行刚性配准以进行全局定位。然后，将弯曲能量项惩罚到归一化互信息中，以对软组织的局部变形进行惩罚。最后，该框架在我们内部收集的 40 对 CT-MR 图像和 SpineWeb 数据库中的 15 对图像上进行了验证。实验结果表明，重叠率高（内部收集，椎体 0.97 ± 0.02，血管 0.88 ± 0.07；SpineWeb，椎体 0.95 ± 0.03，血管 0.93 ± 0.10），目标配准误差低（内部收集，≤2.00 ± 0.62 mm；SpineWeb，≤2.37 ± 0.76 mm）。所提出的框架既考虑了骨结构的不可压缩性，也考虑了软组织的非刚性变形。它能够实现腰椎图像的准确 CT-MR 配准，并有助于图像融合、脊柱疾病诊断和介入治疗实施。
