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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.


Closest corresponding points distance between paired vessels
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Fig11: Closest corresponding points distance between paired vessels

Mentions: As we do not have a ground truth for the registration, we used the vasculature visible on the angiographies as a reference. Thus, for validating, we compared how well the projected 3DRA matches the arteries visible in the projection images. To this end, we projected the 3DRA vasculature on the 2D image with the registered and we computed the closest corresponding points distance between the projected 3D vasculature and the 2D vasculature. The most relevant region for the roadmapping is the area close to the catheter tip; we therefore only evaluated in a circular region (3 cm radius) around the catheter tip. Before computing the distance, we manually labelled the vessels such that distances are computed between corresponding vessels (Fig. 10). To prevent bias in this assignment, the manual annotation was done without registration, thus only using the initial projection of the 3DRA. To aid in the annotation, the observer could manually register 3DRA and 2D vasculature by changing translations and rotations of the 3DRA. Vessels that cannot be manually adequately linked were not used in the evaluation. Using the labelled corresponding vessels, we computed the closest corresponding points distance for each pair of vessels (excluding distances to endpoints). The distance was computed both from the 2D angio-vessel and from its registered projected 3D vessel pair (Fig. 11). Our evaluation metric for one image is the average of distances over all pairs of vessels.Fig. 10


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)

Closest corresponding points distance between paired vessels
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig11: Closest corresponding points distance between paired vessels
Mentions: As we do not have a ground truth for the registration, we used the vasculature visible on the angiographies as a reference. Thus, for validating, we compared how well the projected 3DRA matches the arteries visible in the projection images. To this end, we projected the 3DRA vasculature on the 2D image with the registered and we computed the closest corresponding points distance between the projected 3D vasculature and the 2D vasculature. The most relevant region for the roadmapping is the area close to the catheter tip; we therefore only evaluated in a circular region (3 cm radius) around the catheter tip. Before computing the distance, we manually labelled the vessels such that distances are computed between corresponding vessels (Fig. 10). To prevent bias in this assignment, the manual annotation was done without registration, thus only using the initial projection of the 3DRA. To aid in the annotation, the observer could manually register 3DRA and 2D vasculature by changing translations and rotations of the 3DRA. Vessels that cannot be manually adequately linked were not used in the evaluation. Using the labelled corresponding vessels, we computed the closest corresponding points distance for each pair of vessels (excluding distances to endpoints). The distance was computed both from the 2D angio-vessel and from its registered projected 3D vessel pair (Fig. 11). Our evaluation metric for one image is the average of distances over all pairs of vessels.Fig. 10

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.