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Continuous roadmapping in liver TACE procedures using 2D-3D catheter-based registration.

Ambrosini P, Ruijters D, Niessen WJ, Moelker A, van Walsum T - Int J Comput Assist Radiol Surg (2015)

Bottom Line: Subsequently, the catheter is registered to this vessel, and the 3DRA is visualized based on the registration results.The first selected vessel, ranked with the shape similarity metric, is used more than 39 % in the final registration and the second more than 21 %.The median of the closest corresponding points distance between 2D angiography vessels and projected 3D vessels is 4.7-5.4 mm when using the brute force optimizer and 5.2-6.6 mm when using the Powell optimizer.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, The Netherlands, p.ambrosini@erasmusmc.nl.

ABSTRACT

Purpose: Fusion of pre/perioperative images and intra-operative images may add relevant information during image-guided procedures. In abdominal procedures, respiratory motion changes the position of organs, and thus accurate image guidance requires a continuous update of the spatial alignment of the (pre/perioperative) information with the organ position during the intervention.

Methods: In this paper, we propose a method to register in real time perioperative 3D rotational angiography images (3DRA) to intra-operative single-plane 2D fluoroscopic images for improved guidance in TACE interventions. The method uses the shape of 3D vessels extracted from the 3DRA and the 2D catheter shape extracted from fluoroscopy. First, the appropriate 3D vessel is selected from the complete vascular tree using a shape similarity metric. Subsequently, the catheter is registered to this vessel, and the 3DRA is visualized based on the registration results. The method is evaluated on simulated data and clinical data.

Results: The first selected vessel, ranked with the shape similarity metric, is used more than 39 % in the final registration and the second more than 21 %. The median of the closest corresponding points distance between 2D angiography vessels and projected 3D vessels is 4.7-5.4 mm when using the brute force optimizer and 5.2-6.6 mm when using the Powell optimizer.

Conclusion: We present a catheter-based registration method to continuously fuse a 3DRA roadmap arterial tree onto 2D fluoroscopic images with an efficient shape similarity.

No MeSH data available.


Registration metric  with the first closest distances of the two points of the catheter centerline
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Fig6: Registration metric with the first closest distances of the two points of the catheter centerline

Mentions: Given these definitions, the final registration metric of our registration is a weighted sum of these distances (Fig. 6):6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned}&M(C_{2D}, l^\mathrm{sel}, T) = D_1(l^\mathrm{sel}, T) \nonumber \\&\quad + \sum _{c_i \in [c_2, c_{n_{C}}]} W(//c_i, c_1//) \cdot D(c_i, l^\mathrm{sel}, T, p_\mathrm{prev}), \end{aligned}$$\end{document}M(C2D,lsel,T)=D1(lsel,T)+∑ci∈[c2,cnC]W(//ci,c1//)·D(ci,lsel,T,pprev),where is a weight function and is the length of the catheter between and . As the registration accuracy close to the tip is more important than at the proximal part of the catheter, we use a weight to decrease the distance values that are further from the tip. We use a Gaussian with an offset:7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} W(x) = \lambda + (1 - \lambda ) \cdot e^{-\frac{x^2}{2\sigma ^2}}, \end{aligned}$$\end{document}W(x)=λ+(1-λ)·e-x22σ2,where is a parameter to control how fast the weight decrease (Fig. 7).Fig. 6


Continuous roadmapping in liver TACE procedures using 2D-3D catheter-based registration.

Ambrosini P, Ruijters D, Niessen WJ, Moelker A, van Walsum T - Int J Comput Assist Radiol Surg (2015)

Registration metric  with the first closest distances of the two points of the catheter centerline
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4563001&req=5

Fig6: Registration metric with the first closest distances of the two points of the catheter centerline
Mentions: Given these definitions, the final registration metric of our registration is a weighted sum of these distances (Fig. 6):6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned}&M(C_{2D}, l^\mathrm{sel}, T) = D_1(l^\mathrm{sel}, T) \nonumber \\&\quad + \sum _{c_i \in [c_2, c_{n_{C}}]} W(//c_i, c_1//) \cdot D(c_i, l^\mathrm{sel}, T, p_\mathrm{prev}), \end{aligned}$$\end{document}M(C2D,lsel,T)=D1(lsel,T)+∑ci∈[c2,cnC]W(//ci,c1//)·D(ci,lsel,T,pprev),where is a weight function and is the length of the catheter between and . As the registration accuracy close to the tip is more important than at the proximal part of the catheter, we use a weight to decrease the distance values that are further from the tip. We use a Gaussian with an offset:7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} W(x) = \lambda + (1 - \lambda ) \cdot e^{-\frac{x^2}{2\sigma ^2}}, \end{aligned}$$\end{document}W(x)=λ+(1-λ)·e-x22σ2,where is a parameter to control how fast the weight decrease (Fig. 7).Fig. 6

Bottom Line: Subsequently, the catheter is registered to this vessel, and the 3DRA is visualized based on the registration results.The first selected vessel, ranked with the shape similarity metric, is used more than 39 % in the final registration and the second more than 21 %.The median of the closest corresponding points distance between 2D angiography vessels and projected 3D vessels is 4.7-5.4 mm when using the brute force optimizer and 5.2-6.6 mm when using the Powell optimizer.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, The Netherlands, p.ambrosini@erasmusmc.nl.

ABSTRACT

Purpose: Fusion of pre/perioperative images and intra-operative images may add relevant information during image-guided procedures. In abdominal procedures, respiratory motion changes the position of organs, and thus accurate image guidance requires a continuous update of the spatial alignment of the (pre/perioperative) information with the organ position during the intervention.

Methods: In this paper, we propose a method to register in real time perioperative 3D rotational angiography images (3DRA) to intra-operative single-plane 2D fluoroscopic images for improved guidance in TACE interventions. The method uses the shape of 3D vessels extracted from the 3DRA and the 2D catheter shape extracted from fluoroscopy. First, the appropriate 3D vessel is selected from the complete vascular tree using a shape similarity metric. Subsequently, the catheter is registered to this vessel, and the 3DRA is visualized based on the registration results. The method is evaluated on simulated data and clinical data.

Results: The first selected vessel, ranked with the shape similarity metric, is used more than 39 % in the final registration and the second more than 21 %. The median of the closest corresponding points distance between 2D angiography vessels and projected 3D vessels is 4.7-5.4 mm when using the brute force optimizer and 5.2-6.6 mm when using the Powell optimizer.

Conclusion: We present a catheter-based registration method to continuously fuse a 3DRA roadmap arterial tree onto 2D fluoroscopic images with an efficient shape similarity.

No MeSH data available.