Robust Orthogonal-View 2-D/3-D Rigid Registration for Minimally Invasive Surgery

Micromachines

卷:12

Zhou An¹  Jian Hu¹  Honghai Ma¹  Haojian Lu²  Rong Xiong²  Lilu Liu²  Yue Wang²  Chunlin Zhou² 

[1] Department of Thoracic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China;

[2] State Key Laboratory of Industrial Control and Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China;

关键词: 2-D/3-D registration;    rigid;    multi-view;    reconstruction;    deep learning;   

DOI  :  10.3390/mi12070844

来源: DOAJ

【 摘 要 】

Intra-operative target pose estimation is fundamental in minimally invasive surgery (MIS) to guiding surgical robots. This task can be fulfilled by the 2-D/3-D rigid registration, which aligns the anatomical structures between intra-operative 2-D fluoroscopy and the pre-operative 3-D computed tomography (CT) with annotated target information. Although this technique has been researched for decades, it is still challenging to achieve accuracy, robustness and efficiency simultaneously. In this paper, a novel orthogonal-view 2-D/3-D rigid registration framework is proposed which combines the dense reconstruction based on deep learning and the GPU-accelerated 3-D/3-D rigid registration. First, we employ the X2CT-GAN to reconstruct a target CT from two orthogonal fluoroscopy images. After that, the generated target CT and pre-operative CT are input into the 3-D/3-D rigid registration part, which potentially needs a few iterations to converge the global optima. For further efficiency improvement, we make the 3-D/3-D registration algorithm parallel and apply a GPU to accelerate this part. For evaluation, a novel tool is employed to preprocess the public head CT dataset CQ500 and a CT-DRR dataset is presented as the benchmark. The proposed method achieves 1.65 ± 1.41 mm in mean target registration error(mTRE), 20% in the gross failure rate(GFR) and 1.8 s in running time. Our method outperforms the state-of-the-art methods in most test cases. It is promising to apply the proposed method in localization and nano manipulation of micro surgical robot for highly precise MIS.

【 授权许可】

Unknown