Symmetry, outliers, and geodesics in coronary artery centerline reconstruction from rotational angiography

Unberath, Mathias; Taubmann, Oliver; Hell, Michaela M.; Achenbach, Stephan; Maier, Andreas

doi:10.1002/mp.12512

Cited by 10 publications

(6 citation statements)

References 40 publications

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“…One of the main advantages of our proposed reconstruction method is that it can be applied when only two image projections are available. Most state-of-the-art 3D reconstruction methods, such as [24] and [25], only work when at least three image projections are available, which is often infeasible in clinical practice (mostly for RCA). Although the method of [26] is applicable on two projections, it is suggested to be used on multiple angiographic projections.…”

Section: Discussionmentioning

confidence: 99%

“…This could result in falsely reconstructed vessel segments and discontinuities along centerlines. More recently, Unberath et al [25] applied the same approach of [24], combined with graph cuts. After merging N(N −1) reconstructions from N projections, the outliers were removed based on reprojection errors on additional reference projections.…”

Section: Introductionmentioning

confidence: 99%

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Point-Cloud Method for Automated 3D Coronary Tree Reconstruction From Multiple Non-Simultaneous Angiographic Projections

Banerjee

Galassi

Zacur

et al. 2020

IEEE Trans. Med. Imaging

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X-ray angiography is the most commonly used imaging modality for the detection of coronary stenoses due to its high spatial and temporal resolution of lumen contour and its utility to guide coronary interventions in real time. However, the high inter-and intra-observer variability in interpreting the geometry of 3D vascular structure based on multiple 2D image projections is a limitation in the accurate determination of lesion severity. This could be addressed by the 3D reconstruction of the coronary arterial (CA) tree. The automated reconstruction of 3D CA tree from 2D projections is challenging due to the existence of several imaging artifacts, such as vessel overlap, foreshortening, and most importantly respiratory and cardiac motion. Along with these artifacts, the acquisition geometry introduces the possibility of generating false vessel segments in the reconstruction. Our approach aims to reduce the motion artifacts in angiographic projections by developing a new method for rigid and non-rigid motion correction. A novel point-cloud based approach is subsequently introduced for reconstruction of 3D vessel centerlines by iteratively minimizing the reconstruction error. The performance of the proposed 3D reconstruction is evaluated using angiographic projections from 45 patients, producing average Manuscript

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Point-Cloud Method for Automated 3D Coronary Tree Reconstruction From Multiple Non-Simultaneous Angiographic Projections

Banerjee

Galassi

Zacur

et al. 2020

IEEE Trans. Med. Imaging

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“…We extract centerlines as minimal cost paths from potential end nodes to the start nodes, in this case the coronary ostia that are extracted according to Section 3.5 [36]. As the end nodes for the cheapest path computation are not known beforehand, we first perform a front propagation step [32] in the Dijkstra sense [37] that has similarities to fast marching algorithms as described in [38].…”

Section: Centerline Extractionmentioning

confidence: 99%

Automated Curved and Multiplanar Reformation for Screening of the Proximal Coronary Arteries in MR Angiography

et al. 2018

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Congenital anomalies of the coronary ostia can lead to sudden death. A screening solution would be useful to prevent adverse outcomes for the affected individuals. To be considered for integration into clinical routine, such a procedure must meet strict constraints in terms of invasiveness, time and user interaction. Imaging must be fast and seamlessly integrable into the clinical process. Non-contrast enhanced coronary magnetic resonance angiography (MRA) is well suited for this. Furthermore, planar reformations proved effective to reduce the acquired volumetric datasets to 2D images. These usually require time consuming user interaction, though. To fulfill the aforementioned challenges, we present a fully automated solution for imaging and reformatting of the proximal coronary arteries which enables rapid screening of these. The proposed pipeline consists of: (I) highly accelerated single breath-hold MRA data acquisition, (II) coronary ostia detection and vessel centerline extraction, and (III) curved planar reformation of the proximal coronary arteries, as well as multiplanar reformation of the coronary ostia. The procedure proved robust and effective in ten volunteer data sets. Imaging of the proximal coronary arteries took 24 ± 5 s and was successful within one breath-hold for all patients. The extracted centerlines achieve an overlap of 0.76 ± 0.18 compared to the reference standard and the average distance of the centerline points from the spherical surface for reformation was 1.1 ± 0.51 mm. The promising results encourage further experiments on patient data, particularly in coronary ostia anomaly screening.

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“…Irrespective of their categorization as symbolic or tomographic, most currently known coronary artery reconstruction algorithms from rotational angiography rely on projection domain vessel segmentation or centerline extraction algorithms to either perform background suppression or obtain sparse data. Much work has considered automatic vessel segmentation [7][8][9] both in an analytic, model-based but also in a machine learning context. While results are promising when a static imaging geometry can be assumed (e. g. as in traditional angiography), satisfactory segmentation quality cannot yet be reliably achieved in rotational angiography because of substantial changes in vessel visibility in successive views due to overlap with high contrast structures, such as the spine.…”

Section: Scope and Specific Goalsmentioning

confidence: 99%

CoronARe: A Coronary Artery Reconstruction Challenge

Çimen

Unberath

Frangi

et al. 2017

Lecture Notes in Computer Science

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CoronARe ranks state-of-the-art methods in symbolic and tomographic coronary artery reconstruction from interventional C-arm rotational angiography. Specifically, we benchmark the performance of the methods using accurately pre-processed data, and study the effects of imperfect pre-processing conditions (segmentation and background subtraction errors). In this first iteration of the challenge, evaluation is performed in a controlled environment using digital phantom images, where accurate 3D ground truth is known.

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Symmetry, outliers, and geodesics in coronary artery centerline reconstruction from rotational angiography

Abstract: The application of the proposed extensions yielded superior reconstruction quality in all cases and effectively removed erroneously reconstructed points. Future work will investigate possibilities to integrate parts of the proposed extensions directly into reconstruction.

Cited by 10 publications

References 40 publications

Point-Cloud Method for Automated 3D Coronary Tree Reconstruction From Multiple Non-Simultaneous Angiographic Projections

Point-Cloud Method for Automated 3D Coronary Tree Reconstruction From Multiple Non-Simultaneous Angiographic Projections

Automated Curved and Multiplanar Reformation for Screening of the Proximal Coronary Arteries in MR Angiography

CoronARe: A Coronary Artery Reconstruction Challenge

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