Patient Specific 4D Coronary Models from ECG-gated CTA Data for Intra-operative Dynamic Alignment of CTA with X-ray Images

Metz, Coert; Schaap, Michiel; Klein, Stefan; Neefjes, Lisan A.; Capuano, Ernesto; Schultz, Carl; Geuns, Robert Jan van; Serruys, Patrick W.; Walsum, Theo van; Niessen, Wiro J.

doi:10.1007/978-3-642-04268-3_46

Cited by 21 publications

(15 citation statements)

References 8 publications

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“…In fact, considering an average calibrated pixel resolution of 0.22 mm, the median error of our method is of about 1.9 mm. This compares very well with the 1 mm error reported in [11], especially considering that they restrict their evaluation to X-ray images acquired at systole and diastole time instants, where the coronary tree deformation is minimal compared to other cardiac cycle instants we contemplate.…”

Section: Real Datasupporting

confidence: 85%

“…Besides the inherent ambiguity of the monocular non-rigid reconstruction, the problem is further accentuated due to the presence of noise in the images and partial occlusions between different branches of the tree. This complexity has been traditionally alleviated by considering the vascular system as a rigid structure [9,11,16] and using multiple views [26,29]. To the best of our knowledge, [8] is the only approach in the medical imaging literature that considers the non-rigid nature of the problem.…”

Section: Related Workmentioning

confidence: 99%

“…This reduces the shape recovering task to a rigid 3D-to-2D registration [9,11,16]. There exist a recent attempt of addressing the non-rigidity nature of the problem, although it has only been shown effective for relatively small deformations [8].…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous correspondence and non-rigid 3D reconstruction of the coronary tree from single X-ray images

Serradell

Romero

Leta

et al. 2011

2011 International Conference on Computer Vision

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We present a novel approach to simultaneously reconstruct the 3D structure of a non-rigid coronary tree and estimate point correspondences between an input X-ray image and a reference 3D shape. At the core of our approach lies an optimization scheme that iteratively fits a generative 3D model of increasing complexity and guides the matching process. As a result, and in contrast to existing approaches that assume rigidity or quasi-rigidity of the structure, our method is able to retrieve large non-linear deformations even when the input data is corrupted by the presence of noise and partial occlusions. We extensively evaluate our approach under synthetic and real data and demonstrate a remarkable improvement compared to state-of-the-art.

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Section: Real Datasupporting

confidence: 85%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Simultaneous correspondence and non-rigid 3D reconstruction of the coronary tree from single X-ray images

Serradell

Romero

Leta

et al. 2011

2011 International Conference on Computer Vision

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“…Gouveia et al 10 Regarding the evaluation of registration, most of 3D/2D coronary registration studies in the literature used clinical data (e.g., Metz, Schaap, Klein, Neefjes, et al 2009;Metz, Schaap, Klein, Weustink, et al 2009;Metz et al 2011Metz et al , 2013Baka et al 2013), and some used both clinical and computer simulated data (Ruijters et al 2009;Rivest-Hénault et al 2012;Baka et al 2014). Turgeon et al (2005) considered computer simulated data generated from an excised human heart and Aksoy et al (2013) considered images from a phantom vessel but also clinical data.…”

Section: Discussionmentioning

confidence: 99%

Registration-by-regression of coronary CTA and X-ray angiography

Gouveia

Metz

Freire

et al. 2015

Computer Methods in Biomechanics and Biomedical Engineering: Im

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We evaluate the integration of 3D preoperative computed tomography angiography of the coronary arteries with intraoperative 2D X-ray angiographies by a recently proposed novel registration-by-regression method. The method relates image features of 2D projection images to the transformation parameters of the 3D image. We compared different sets of features and studied the influence of preprocessing the training set. For the registration evaluation, a gold standard was developed from eight X-ray angiography sequences from six different patients. The alignment quality was measured using the 3D mean target registration error (mTRE). The registration-by-regression method achieved moderate accuracy (median mTRE of 15 mm) on real images. It does therefore not provide yet a complete solution to the 3D-2D registration problem but it could be used as an initialisation method to eliminate the need for manual initialisation.

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“…A non-rigid deformation approach has recently been proposed in several studies dedicated to 3D/2D coronary artery registration. Most non-rigid approaches have proposed to incorporate cardiac motion models from 3D+t CTA [6][7][8] or a coronary deformation model by randomly perturbing a CTA-derived coronary centerline [9]. Some recently attempted non-rigid registration methods with biplane X-ray angiography allow the use of additional information from the second X-ray projection image [10].…”

Section: Introductionmentioning

confidence: 99%

Non-rigid Registration of Vascular Structures for Aligning 2D X-ray Angiography with 3D CT Angiography

Kim

Kang

Kim

2014

Advances in Visual Computing

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Abstract. The alignment of pre-operative 3D scans with intra-operative 2D images is important for providing better image guidance. Specifically, overlaying the 3D centerlines of coronary arteries on top of X-ray angiography images reduces the uncertainty inherent in 2D images used during cardiovascular interventions. Because of the dynamic cardiovascular motion from the heartbeat and respiration, a non-rigid registration approach should be applied in contrast registration of the static vascular structure. In this paper, a modified TPS-RPM method is adopted as a non-rigid registration based on a feature-based approach. The proposed method is evaluated on 12 clinical datasets to highlight the necessity of a non-rigid registration approach. IntroductionImage registration between pre-operative images and intra-operative images is a vital technology for image-guided surgery. X-ray angiography (XA) is the modality most commonly used during percutaneous coronary intervention (PCI) for visualization of vessel anatomy and catheter guidance because it is able to capture temporal changes in vessel structures during intervention. However, vessel structures are visible only for 20 seconds after a contrast medium is injected, and the projective nature of angiography makes it difficult for the physician to guide the catheter through the patient's vessel system. Similar challenges occur for other minimally invasive procedures [1,2]. To overcome these challenges, a pre-operative image modality is acquired to yield a 3D total anatomic view that can be used to reduce visual uncertainty and to assess the region of occlusion when planning the intervention. Currently, the most commonly used pre-operative imaging modality for percutaneous coronary interventions is 3D computed tomography angiography (CTA) as a complement. Therefore, image registration between CTA and XA provides surgeons with 3D depth perception and better guidance of the catheter, which leads to increased accuracy.

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Patient Specific 4D Coronary Models from ECG-gated CTA Data for Intra-operative Dynamic Alignment of CTA with X-ray Images

Cited by 21 publications

References 8 publications

Simultaneous correspondence and non-rigid 3D reconstruction of the coronary tree from single X-ray images

Simultaneous correspondence and non-rigid 3D reconstruction of the coronary tree from single X-ray images

Registration-by-regression of coronary CTA and X-ray angiography

Non-rigid Registration of Vascular Structures for Aligning 2D X-ray Angiography with 3D CT Angiography

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