A tree-topology preserving pairing for 3D/2D registration

Benseghir, Thomas; Malandain, Grégoire; Vaillant, Régis

doi:10.1007/s11548-015-1207-0

Cited by 22 publications

(22 citation statements)

References 20 publications

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“…A and X-ray images x 2D Bj . 14 In our survey of 2D-3D registration methods over the past 4 years, 11 papers focused on spine surgery, [15][16][17][18][19][20][21][22][23] five on vascular intervention, [24][25][26][27][28] and six on total knee arthroplasty (TKA). Although knees are the target region in both TKA and ACL reconstruction, the requirements of the two surgeries were different: femoral prothesis rotational alignment is an important criterion for TKA, while the accuracy of bone tunnel placement is the most important criterion for ACL reconstruction.…”

Section: Tbetween Ct Images X 3dmentioning

confidence: 99%

“…According to the intrinsic nature of registration, 2D‐3D registration methods could be classified into feature‐based, intensity‐based, and gradient‐based registration. Geometrical entities such as isolated points or point sets, forming a curve, contour, or surface serve as the basis of the feature‐based 3D/2D registration method. Most registration methods in vascular intervention are based on isolated points or point sets.…”

Section: Introductionmentioning

confidence: 99%

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Intensity‐based 2D‐3D registration for an ACL reconstruction navigation system

Guo

Yang

et al. 2019

Robotics Computer Surgery

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To improve the positioning accuracy of tunnels for anterior cruciate ligament (ACL) reconstruction, we proposed an intensity‐based 2D‐3D registration method for an ACL reconstruction navigation system. Methods for digitally reconstructed radiograph (DRR) generation, similarity measurement, and optimization are crucial to 2D‐3D registration. We evaluated the accuracy, success rate, and processing time of different methods: (a) ray‐casting and splating were compared for DRR generation; (b) normalized mutual information (NMI), Mattes mutual information (MMI), and Spearman's rank correlation coefficient (SRC) were assessed for similarity between registrations; and (c) gradient descent (GD) and downhill simplex (DS) were compared for optimization. The combination of splating, SRC, and GD provided the best composite performance and was applied in an augmented reality (AR) ACL reconstruction navigation system. The accuracy of the navigation system could fulfill the clinical needs of ACL reconstruction, with an end pose error of 2.50 mm and an angle error of 2.74°.

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Section: Tbetween Ct Images X 3dmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intensity‐based 2D‐3D registration for an ACL reconstruction navigation system

Guo

Yang

et al. 2019

Robotics Computer Surgery

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“…This step refers to building the correspondences between the centerline of each tip candidate C extracted from navigation sequence and the corresponding centerlines of the vessels X extracted from reference sequence by taking into account ECG information. We adopt the curve pairing algorithm of [2] to perform this task. It is required to define a curve-to-curve distance to compare the two sets of curves mentioned above.…”

Section: Feature Pairs Extractionmentioning

confidence: 99%

VOIDD: Automatic Vessel-of-Intervention Dynamic Detection in PCI Procedures

Bacchuwar

Cousty

Vaillant

et al. 2017

Lecture Notes in Computer Science

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In this article, we present the work towards improving the overall workflow of the Percutaneous Coronary Interventions (PCI) procedures by capacitating the imaging instruments to precisely monitor the steps of the procedure. In the long term, such capabilities can be used to optimize the image acquisition to reduce the amount of dose or contrast media employed during the procedure. We present the automatic VOIDD algorithm to detect the vessel of intervention which is going to be treated during the procedure by combining information from the vessel image with contrast agent injection and images acquired during guidewire tip navigation. Due to the robust guidewire tip segmentation method, this algorithm is also able to automatically detect the sequence corresponding to guidewire navigation. We present an evaluation methodology which characterizes the correctness of the guide wire tip detection and correct identification of the vessel navigated during the procedure. On a dataset of 2213 images from 8 sequences of 4 patients, VOIDD identifies vesselof-intervention with accuracy in the range of 88% or above and absence of tip with accuracy in range of 98% or above depending on the test case.

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“…The points on vascular structures and the connections among points determine the nodes and edges in the graph, respectively. Holistic vascular registration is implemented by graph matching [26][27][28], which requires the topological structure of the 2D vasculature. However, the ideal vascular topology is difficult to obtain using automatic centerline extraction algorithm.…”

Section: Introductionmentioning

confidence: 99%

Greedy Soft Matching for Vascular Tracking of Coronary Angiographic Image Sequences

Fang

Zhu

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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Purpose: Vascular tracking of angiographic image sequences is one of the most clinically important tasks in the diagnostic assessment and interventional guidance of cardiac disease due to providing dynamic structural information for precise motion analysis and 3D+t reconstruction. However, this task can be challenging to accomplish because of unsatisfactory angiography image quality and complex vascular structures. Thus, this study converted vascular tracking into branch matching and proposed a new greedy graph search-based method for it.Methods: Each vascular branch was separated from the vasculature and was tracked independently.Then, all branches were combined using topology optimization, thereby resulting in complete vasculature tracking. An intensity-based image registration method was applied to determine the tracking range, and the deformation field between two consecutive frames was calculated. The vascular branch was described using a vascular centerline extraction method with multi-probability fusion-based topology optimization. We introduced an undirected acyclic graph establishment technique. A greedy search method was proposed to acquire all possible paths in the graph that might match the tracked vascular branch. The final tracking result was selected by branch matching using dynamic time warping with a DAISY descriptor.Results: For single branch dataset SBD, the proposed method was evaluated on 12 angiographic image sequences with 77 angiograms of contrast agent-filled vessels. The average precision, sensitivity and F1 score of the tracking result of all angiograms were 0.90, 0.89 and 0.89, respectively. The average F1 score of the tracking results of the first, middle and last frames in all sequences were 0.91, 0.91 and 0.86, respectively. In the vessel tree dataset VTD, the proposed method was validated on 9 angiographic image sequences with 58 angiograms of contrast agentfilled vessels. The average precision, sensitivity and F1 score of the tracking result of all angiograms were 0.89, 0.87 and 0.88, respectively. The average F1 score of the tracking results of the first, middle and last frames in all sequences were 0.94, 0.88 and 0.82, respectively. Compared with five other state-of-the-art methods, the proposed method accurately tracked the vasculature from angiographic image sequences and the results were insignificantly affected by the tracking span in both datasets. Conclusions:The solution to the problem reflected both the spatial and textural information between successive frames. Thus, the proposed method is robust and highly effective in vascular tracking of angiographic image sequences and the approach provided a universal solution to address the problem of filamentary structure tracking.

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A tree-topology preserving pairing for 3D/2D registration

Abstract: The proposed method exhibits good results in terms of both pairing and alignment as well as low sensitivity to rotations to be compensated (up to 30°).

Cited by 22 publications

References 20 publications

Intensity‐based 2D‐3D registration for an ACL reconstruction navigation system

Intensity‐based 2D‐3D registration for an ACL reconstruction navigation system

VOIDD: Automatic Vessel-of-Intervention Dynamic Detection in PCI Procedures

Greedy Soft Matching for Vascular Tracking of Coronary Angiographic Image Sequences

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