Precalibration Versus 2D-3D Registration for 3D Guide Wire Display in Endovascular Interventions

Baert, S.A.M.; Penney, Graeme P.; Walsum, Theo van; Niessen, Wiro J.

doi:10.1007/978-3-540-30136-3_71

Cited by 8 publications

(4 citation statements)

References 7 publications

(8 reference statements)

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“…A further advantage of machine-based registration is the fact that it can be computed in realtime. Machine based registration and image based 2D-3D registration have been compared by Baert et al 11 A method for determining the incidence based on tracking a fiducial was proposed by Jain et al 12 We, however, do not use any fiducials. We only use the information concerning the geometry state, as is provided by the C-arm system.…”

Section: Related Workmentioning

confidence: 99%

3D multimodality roadmapping in neuroangiography

Ruijters

Babić

Homan

et al. 2007

Medical Imaging 2007: Visualization and Image-Guided Procedures

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In this paper we describe a novel approach to using morphological datasets (such as CT or MR) in the minimally invasive image guidance of intra-arterial and intra-venous endovascular devices in neuroangiography interventions. Minimally invasive X-ray angiography procedures rely on the navigation of endovascular devices, such as guide wires and catheters, through human vessels, using C-arm fluoroscopy. While the bone structure may be visible, and the injection of iodine contrast medium allows to guide endovascular devices through the vasculature, the soft-tissue structures remain invisible in the fluoroscopic images. We intend to present a method for the combined visualization of morphological data, a 3D rotational angiography (3DRA) reconstruction and the live fluoroscopy data stream in a single image. The combination of the fluoroscopic image with the 3DRA vessel tree offers the advantage that endovascular devices can be located with respect to the vasculature, without additional contrast injection, while the position of the C-arm geometry can be altered freely. The additional visualization of the morphological data, adds contextual information to the position of endovascular devices. This article addresses the clinical applications, the real-time aspects of the registration algorithms and fast fused visualization of the proposed method.

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Section: Related Workmentioning

confidence: 99%

3D multimodality roadmapping in neuroangiography

Ruijters

Babić

Homan

et al. 2007

Medical Imaging 2007: Visualization and Image-Guided Procedures

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“…), provided that there was no patient motion between the acquisition of the 3DRA data and fluoroscopy data (Maintz and Viergever 1998, Kerrien et al 1998, Cañero et al 2002, Söderman et al 2005, Gorges et al 2005. This method also allows obtaining a registration, even when there are insufficient landmark structures present in the images, e.g., due to the absence of iodine contrast medium in the fluoroscopy images (Baert et al 2004). A further advantage of machine-based registration is the fact that it can be computed in real-time.…”

Section: Introductionmentioning

confidence: 99%

“…This method also allows obtaining a registration, even when there are insufficient landmark structures present in the images, e.g. due to the absence of the iodine contrast medium in the fluoroscopy images (Baert et al 2004). A further advantage of machine-based registration is the fact that it can be computed in real time.…”

Section: Introductionmentioning

confidence: 99%

Validation of 3D multimodality roadmapping in interventional neuroradiology

et al. 2011

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Three-dimensional multimodality roadmapping is entering clinical routine utilization for neuro-vascular treatment. Its purpose is to navigate intra-arterial and intra-venous endovascular devices through complex vascular anatomy by fusing pre-operative computed tomography (CT) or magnetic resonance (MR) with the live fluoroscopy image. The fused image presents the real-time position of the intra-vascular devices together with the patient's 3D vascular morphology and its soft-tissue context. This paper investigates the effectiveness, accuracy, robustness and computation times of the described methods in order to assess their suitability for the intended clinical purpose: accurate interventional navigation. The mutual information-based 3D-3D registration proved to be of sub-voxel accuracy and yielded an average registration error of 0.515 mm and the live machine-based 2D-3D registration delivered an average error of less than 0.2 mm. The capture range of the image-based 3D-3D registration was investigated to characterize its robustness, and yielded an extent of 35 mm and 25° for >80% of the datasets for registration of 3D rotational angiography (3DRA) with CT, and 15 mm and 20° for >80% of the datasets for registration of 3DRA with MR data. The image-based 3D-3D registration could be computed within 8 s, while applying the machine-based 2D-3D registration only took 1.5 µs, which makes them very suitable for interventional use.

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“…Many researchers have developed various methods for registration of tubular organs from medical images. Baert et al (2004) used an intensity based 2D-3D registration algorithm to register the pre-operative 3D Magnetic Resonance Angiogram (MRA) data to the interventional digital subtraction angiography (DSA) images. Chan et al (2004) proposed a 2D-3D vascular registration algorithm based on minimizing the sum of squared differences between the projected image and the reference DSA image.…”

Section: Introductionmentioning

confidence: 99%