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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.


Euclidean distance , longitudinal distance  and orthogonal distance  between the real tip and the registered one (in mm) with catheter smoothing, 3 different simulations (Table 2), optimal settings and 3 mm sampling
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Fig18: Euclidean distance , longitudinal distance and orthogonal distance between the real tip and the registered one (in mm) with catheter smoothing, 3 different simulations (Table 2), optimal settings and 3 mm sampling

Mentions: Figure 17 shows the distance between the real tip in the simulated catheter (without smoothing) and the tip after registration. Figure 18 shows the results with catheter smoothing. Without catheter smoothing, for the brute force optimizer, the median of the Euclidean distance is below 1 mm whereas for Powell the distance is below 3 mm. With catheter smoothing, the registered tip is less accurate and less robust with both Powell and brute force optimization. The longitudinal and orthogonal distance are similar with slight, moderate or large transformation. Overall, the longitudinal distance is slightly more robust than the orthogonal.Fig. 17


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)

Euclidean distance , longitudinal distance  and orthogonal distance  between the real tip and the registered one (in mm) with catheter smoothing, 3 different simulations (Table 2), optimal settings and 3 mm sampling
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig18: Euclidean distance , longitudinal distance and orthogonal distance between the real tip and the registered one (in mm) with catheter smoothing, 3 different simulations (Table 2), optimal settings and 3 mm sampling
Mentions: Figure 17 shows the distance between the real tip in the simulated catheter (without smoothing) and the tip after registration. Figure 18 shows the results with catheter smoothing. Without catheter smoothing, for the brute force optimizer, the median of the Euclidean distance is below 1 mm whereas for Powell the distance is below 3 mm. With catheter smoothing, the registered tip is less accurate and less robust with both Powell and brute force optimization. The longitudinal and orthogonal distance are similar with slight, moderate or large transformation. Overall, the longitudinal distance is slightly more robust than the orthogonal.Fig. 17

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.