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

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

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 tomogr...

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Bibliographic Details
Published in:Micromachines (Basel) 2021-07, Vol.12 (7), p.844
Main Authors: An, Zhou, Ma, Honghai, Liu, Lilu, Wang, Yue, Lu, Haojian, Zhou, Chunlin, Xiong, Rong, Hu, Jian
Format: Article
Language:eng
Subjects:
2-D/3-D registration
Accuracy
Algorithms
Computed tomography
Datasets
deep learning
Efficiency
Failure rates
Fluoroscopy
Image reconstruction
Laparoscopy
Machine learning
Methods
multi-view
Optimization
reconstruction
Registration
rigid
Robotic surgery
Three dimensional imaging
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Summary: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.
ISSN:2072-666X
2072-666X