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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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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title	Robust Orthogonal-View 2-D/3-D Rigid Registration for Minimally Invasive Surgery
format	Article
creator	An, Zhou Ma, Honghai Liu, Lilu Wang, Yue Lu, Haojian Zhou, Chunlin Xiong, Rong Hu, Jian
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
ispartof	Micromachines (Basel), 2021-07, Vol.12 (7), p.844
description	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.
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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.</description><identifier>ISSN: 2072-666X</identifier><identifier>EISSN: 2072-666X</identifier><identifier>DOI: 10.3390/mi12070844</identifier><identifier>PMID: 34357254</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>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</subject><ispartof>Micromachines (Basel), 2021-07, Vol.12 (7), p.844</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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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.</abstract><cop>Basel</cop><pub>MDPI AG</pub><pmid>34357254</pmid><doi>10.3390/mi12070844</doi><orcidid>https://orcid.org/0000-0002-1393-3040</orcidid><orcidid>https://orcid.org/0000-0003-2937-0702</orcidid><oa>free_for_read</oa></addata></record>