Automated segmentation of blood-flow regions in large thoracic arteries using 3D-cine PC-MRI measurements

Pelt, Roy van; Nguyen, Huy H.; Romeny, Bart M. ter Haar; Vilanova, Anna

doi:10.1007/s11548-011-0642-9

Cited by 25 publications

(23 citation statements)

References 9 publications

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“…Among automated segmentation techniques that have been published elsewhere, the active surface model method by van Pelt (6), while fast, accurate, and topologically stable, is affected by limited spatial resolution and can therefore not segment smaller arteries which are the target of cerebral 4D flow MRI scans. Although pulsatility likely changes luminal diameters in the ICA, such differences in the common carotid artery are on the order of 1 mm in healthy young volunteers (16).…”

Section: Discussionmentioning

confidence: 99%

“…Thus active surface modelling would not be needed for these small luminal changes intracranially throughout the cardiac cycle. Additionally, the initial isosurface used in van Pelt (6) was generated with a varying manually-defined threshold on their version of the tMIP. These manual interactions potentially introduce user-dependence and are not part of our automated segmentation scheme.…”

Section: Discussionmentioning

confidence: 99%

“…In vessels with a tortuous path, such as in the siphon of the internal carotid artery (ICA siphon ), segmentations and measurements become increasingly user-dependent and time-consuming. Therefore, automated segmentation methods are preferred for robust and consistent measurements, but in the context of 4D flow MRI, have only been presented in large and relatively straight intrathoracic arteries (6,7). …”

Section: Introductionmentioning

confidence: 99%

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Fast 4D flow MRI intracranial segmentation and quantification in tortuous arteries

Schrauben

Wåhlin

Ambarki

et al. 2015

Magnetic Resonance Imaging

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Purpose 4D flow MRI enables blood flow measurement in all major cerebral arteries with one scan. Clinical translational hurdles are time demanding post-processing and user-dependence induced variability during analysis. To overcome these shortcomings, a centerline processing scheme (CPS) for semi-automated segmentation and quantification is described, validated, and implemented in carotid siphons of healthy subjects. Materials and Methods A CPS for 4D flow data was developed to automatically separate cerebral artery trees. Flow parameters were quantified at planes along the centerline oriented perpendicular to the vessel path. At 3T, validation against 2D phase-contrast (PC) MR and 4D flow manual processing was performed on an intracranial flow phantom for constant flow, while pulsatile flow validation was performed in the internal carotid artery (ICA) of ten healthy volunteers. The CPS and 4D manual processing times were measured and compared. Flow and area measurements were also demonstrated along the length of the ICA siphon. Results Phantom measurements for area and flow were highly correlated between the CPS and 2D measurements (area: R = 0.95, flow: R = 0.94), while in-vivo waveforms were highly correlated (R = 0.93). Processing time was reduced by a factor of 4.6 compared with manual processing. Whole ICA measurements revealed significantly decreased area in the most distal segment of the carotid siphon (p = 0.0017), with flow unchanged (p = 0.84). Conclusion This study exhibits fast semi-automated analysis of intracranial 4D flow MRI. Internal consistency was shown through flow conservation along the tortuous ICA siphon, which is typically a difficult to assess.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fast 4D flow MRI intracranial segmentation and quantification in tortuous arteries

Schrauben

Wåhlin

Ambarki

et al. 2015

Magnetic Resonance Imaging

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“…The proposed 3D flow displacement analysis does just this by using a semi-automatic, 3D segmentation (Amira software, 5 minutes per patient) to generate quantitative measurements of abnormal aortic flow. Fully automatic segmentation of 4D PC-MRI is anticipated in the near future [20]. …”

Section: Discussionmentioning

confidence: 99%

Extended 3D approach for quantification of abnormal ascending aortic flow

Sigovan¹,

Dyverfeldt²,

Wrenn³

et al. 2015

Magnetic Resonance Imaging

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Background Flow displacement quantifies eccentric flow, a potential risk factor for aneurysms in the ascending aorta, but only at a single anatomic location. The aim of this study is to extend flow displacement analysis to 3D in patients with aortic and aortic valve pathologies. Methods 43 individuals were studied with 4DFlow MRI in 6 groups: healthy, tricuspid aortic valve (TAV) with aortic stenosis (AS) but no dilatation, TAV with dilatation but no AS, and TAV with both AS and dilatation, BAV without AS or dilatation, BAV without AS but with dilation. The protocol was approved by our institutional review board, and informed consent was obtained. Flow displacement was calculated for multiple planes along the ascending aorta, and 2D and 3D analyses were compared. Results Good correlation was found between 2D flow displacement and both maximum and average 3D values (r>0.8). Healthy controls had significantly lower flow displacement values with all approaches (p<0.05). The highest flow displacement was seen with stenotic TAV and aortic dilation (0.24±0.02 with maximum flow displacement). The 2D approach underestimated the maximum flow displacement by more than 20% in 13 out of 36 patients (36%). Conclusions The extended 3D flow displacement analysis offers a more comprehensive quantitative evaluation of abnormal systolic flow in the ascending aorta than 2D analysis. Differences between patient subgroups are better demonstrated, and maximum flow displacement is more reliable assessed.

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“…The emerging trend in many applications, such as in biomedical systems (Dubini et al ., 2011; Steinman et al ., 2003; Antiga et al ., 2008), micro-flows in porous media (Lin et al ., 2010; Al-Omari and Masad, 2004; Yue et al ., 2003) or in materials modeling (Baer, 2002; Ghosh, 2004) is to reconstruct the geometries to be modeled from image data (acquired from in vivo patient data or in situ geological sampling, etc.). Imaging modalities can be varied, spanning optical imaging (Tytell and Lauder, 2004), ultrasound (Boukerroui et al ., 2003; Hovda et al ., 2008; Kao et al ., 1997), MRI (van Pelt et al ., 2012; Makowski et al ., 2002), and X-ray CT (Cnudde et al ., 2006; Vignoles, 2001). Figures 1 and 2 illustrate several examples of image/video data that can be employed to compute fluid/solid dynamics.…”

Section: Introductionmentioning

confidence: 99%

Techniques to derive geometries for image-based Eulerian computations

Dillard

Buchholz

Vigmostad

et al. 2014

Engineering Computations

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Purpose The performance of three frequently used level set-based segmentation methods is examined for the purpose of defining features and boundary conditions for image-based Eulerian fluid and solid mechanics models. The focus of the evaluation is to identify an approach that produces the best geometric representation from a computational fluid/solid modeling point of view. In particular, extraction of geometries from a wide variety of imaging modalities and noise intensities, to supply to an immersed boundary approach, is targeted. Design/methodology/approach Two- and three-dimensional images, acquired from optical, X-ray CT, and ultrasound imaging modalities, are segmented with active contours, k-means, and adaptive clustering methods. Segmentation contours are converted to level sets and smoothed as necessary for use in fluid/solid simulations. Results produced by the three approaches are compared visually and with contrast ratio, signal-to-noise ratio, and contrast-to-noise ratio measures. Findings While the active contours method possesses built-in smoothing and regularization and produces continuous contours, the clustering methods (k-means and adaptive clustering) produce discrete (pixelated) contours that require smoothing using speckle-reducing anisotropic diffusion (SRAD). Thus, for images with high contrast and low to moderate noise, active contours are generally preferable. However, adaptive clustering is found to be far superior to the other two methods for images possessing high levels of noise and global intensity variations, due to its more sophisticated use of local pixel/voxel intensity statistics. Originality/value It is often difficult to know a priori which segmentation will perform best for a given image type, particularly when geometric modeling is the ultimate goal. This work offers insight to the algorithm selection process, as well as outlining a practical framework for generating useful geometric surfaces in an Eulerian setting.

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Automated segmentation of blood-flow regions in large thoracic arteries using 3D-cine PC-MRI measurements

Cited by 25 publications

References 9 publications

Fast 4D flow MRI intracranial segmentation and quantification in tortuous arteries

Fast 4D flow MRI intracranial segmentation and quantification in tortuous arteries

Extended 3D approach for quantification of abnormal ascending aortic flow

Techniques to derive geometries for image-based Eulerian computations

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