4-D Tomographic Representation of Coronary Arteries from One Rotational X-Ray Sequence

Blondel, Christophe; Malandain, Grégoire; Vaillant, Régis; Devernay, Frédéric; Ayache, Nicholas

doi:10.1007/978-3-540-39899-8_52

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…The intraoperative simulated angiograms are automatically converted into binary masks 36 using the multiscale segmentation algorithm described by Koller et al 37 and Sato et al 38 Koller's filter searches for elongated structures of arbitrary shape, creating a sharp maximum at the centerline of the vessels. The multiscale nature of this filter makes it robust to different vessel sizes and also allows the detection of the vessel widths.…”

Section: Angiogram Segmentationmentioning

confidence: 99%

2D‐3D registration of coronary angiograms for cardiac procedure planning and guidance

et al. 2005

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We present a completely automated 2D-3D registration technique that accurately maps a patient-specific heart model, created from preoperative images, to the patient's orientation in the operating room. This mapping is based on the registration of preoperatively acquired 3D vascular data with intraoperatively acquired angiograms. Registration using both single and dual-plane angiograms is explored using simulated but realistic datasets that were created from clinical images. Heart deformations and cardiac phase mismatches are taken into account in our validation using a digital 4D human heart model. In an ideal situation where the pre- and intraoperative images were acquired at identical time points within the cardiac cycle, the single-plane and the dual-plane registrations resulted in 3D root-mean-square (rms) errors of 1.60 +/- 0.21 and 0.53 +/- 0.08 mm, respectively. When a 10% timing offset was added between the pre- and the intraoperative acquisitions, the single-plane registration approach resulted in inaccurate registrations in the out-of-plane axis, whereas the dual-plane registration exhibited a 98% success rate with a 3D rms error of 1.33 +/- 0.28 mm. When all potential sources of error were included, namely, the anatomical background, timing offset, and typical errors in the vascular tree reconstruction, the dual-plane registration performed at 94% with an accuracy of 2.19 +/- 0.77 mm.

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Section: Angiogram Segmentationmentioning

confidence: 99%

2D‐3D registration of coronary angiograms for cardiac procedure planning and guidance

et al. 2005

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“…in the M n matrices. Indeed, as demonstrated in [28], the global matching criterion reaches its minimum for optimal calibration parameters. We thus consider optimizing the global matching criterion with respect to the cameras translation in their acquisition plane, i.e.…”

Section: Multi-ocular Matchingmentioning

confidence: 95%

Reconstruction of coronary arteries from a single rotational X-ray projection sequence

Blondel

Malandain

Vaillant³

et al. 2006

IEEE Trans. Med. Imaging

136

159

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Abstract-Cardiovascular diseases remain the primary cause of death in developed countries. In most cases, exploration of possibly underlying coronary artery pathologies is performed using X-ray coronary angiography. Current clinical routine in coronary angiography is directly conducted in 2-D projection images from several static viewing angles. However, for diagnosis and treatment purposes, coronary artery reconstruction is highly suitable. The purpose of this study is to provide physicians with a 3-D model of coronary arteries, e.g. for absolute threedimensional measures for lesion assessment, instead of direct projective measures deduced from the images, which are highly dependent on the viewing angle. In this article, we propose a novel method to reconstruct coronary arteries from one single rotational X-ray projection sequence. As a side result, we also obtain an estimation of the coronary artery motion. Our method consists of 3 main consecutive steps: (1) 3-D reconstruction of coronary artery centerlines, including respiratory motion compensation, (2) coronary artery 4-D motion computation, and (3) 3-D tomographic reconstruction of coronary arteries, involving compensation for respiratory and cardiac motions. We present some experiments on clinical datasets, and the feasibility of a true 3-D Quantitative Coronary Analysis is demonstrated.

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“…Recently, [6] proposed a graphbased non-rigid deformation model showing good results despite the important number of parameters and the heavy computational costs. Other attempts at motion compensation in the field of 3D reconstruction require multiple views and generally propose heavily parameterized deformation models [7][8][9]. Finally, the methods in the third category put their attention on the optimization strategy [10,11], arguing that the ill-posedness of 3D/2D registration calls for the use of robust optimization techniques allowing multiple hypothesis to coexist during the search, such as techniques based on Monte-Carlo sampling.…”

Section: Related Workmentioning

confidence: 99%

Stochastic 3D Motion Compensation of Coronary Arteries from Monoplane Angiograms

Hadida

Desrosiers

Duong

2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Image-based navigation during percutaneous coronary interventions is highly challenging since it involves estimating the 3D motion of a complex topology using 2D angiographic views. A static coronary tree segmented in a pre-operative CT-scan can be overlaid on top of the angiographic frames to outline the coronary vessels, but this overlay does not account for coronary motion, which has to be mentally compensated by the cardiologist. In this paper, we propose a new approach to the motion estimation problem, where the temporal evolution of the coronary deformation over the cardiac cycle is modeled as a stochastic process. The sequence of angiographic frames is interpreted as a probabilistic evidence of the succession of unknown deformation states, which can be optimized using particle filtering. Iterative and non-rigid registration is performed in a projective manner, and relies on a feature-based similarity measure. Experiments show promising results in terms of registration accuracy, learning capability and computation time.

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4-D Tomographic Representation of Coronary Arteries from One Rotational X-Ray Sequence

Cited by 5 publications

References 11 publications

2D‐3D registration of coronary angiograms for cardiac procedure planning and guidance

2D‐3D registration of coronary angiograms for cardiac procedure planning and guidance

Reconstruction of coronary arteries from a single rotational X-ray projection sequence

Stochastic 3D Motion Compensation of Coronary Arteries from Monoplane Angiograms

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