The Vascular Modeling Toolkit: A Python Library for the Analysis of Tubular Structures in Medical Images

Izzo, Richard L.; Steinman, David A.; Manini, Simone; Antiga, Luca

doi:10.21105/joss.00745

Cited by 82 publications

(37 citation statements)

References 4 publications

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“…Three‐dimensional carotid geometries were reconstructed from 3D‐FLASH MRI images. Segmentation was performed using a level‐set technique (http://www.vmtk.org, 54,55 ) with a colliding fronts initialization. The surface mesh of the lumen boundary was extracted as the zero‐level isosurface through the marching cubes algorithm 56 .…”

Section: Methods For Processing Imaging Datamentioning

confidence: 99%

A surrogate model for plaque modeling in carotids based on Robin conditions calibrated by cine MRI data

Pozzi

Domanin

Forzenigo

et al. 2021

Numer Methods Biomed Eng

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We propose a surrogate model for the fluid–structure interaction (FSI) problem for the study of blood dynamics in carotid arteries in presence of plaque. This is based on the integration of a numerical model with subject‐specific data and clinical imaging. We propose to model the plaque as part of the tissues surrounding the vessel wall through the application of an elastic support boundary condition. In order to characterize the plaque and other surrounding tissues, such as the close‐by jugular vein, the elastic parameters of the boundary condition were spatially differentiated and their values were estimated by minimizing the discrepancies between computed vessel displacements and reference values obtained from CINE Magnetic Resonance Imaging data. We applied the model to three subjects with a degree of stenosis greater than 70%. We found that accounting for both plaque and jugular vein in the estimation of the elastic parameters increases the accuracy. In particular, in all patients, mismatches between computed and in vivo measured wall displacements were one to two orders of magnitude lower than the spatial resolution of the original MRI data. These results confirmed the validity of the proposed surrogate plaque model. We also compared fluid‐dynamics results with those obtained in a fixed wall setting and in a full FSI model, used as gold standard, highlighting the better accordance of our results in comparison to the rigid ones.

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Section: Methods For Processing Imaging Datamentioning

confidence: 99%

A surrogate model for plaque modeling in carotids based on Robin conditions calibrated by cine MRI data

Pozzi

Domanin

Forzenigo

et al. 2021

Numer Methods Biomed Eng

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“…The ground truth maximal diameter change was measured by first extracting the aortic centerline of the fixed image then sampling the centerline at points every 0.5mm. The maximum diameter of each cross-section (orthogonal to the centerline) was then computed by the open-source Vascular Modeling Toolkit (VMTK, www.vmtk.org) 12 . We denote the results as two one-dimensional arrays d V f ixed and d V smoving , with each having the length equal to the number of point samples on the center-line.…”

Section: Iic2 Validation Of Quantitative Measurement Robustnessmentioning

confidence: 99%

Validation of a robust method for quantification of three‐dimensional growth of the thoracic aorta using deformable image registration

et al. 2022

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Purpose: Accurate assessment of thoracic aortic aneurysm (TAA) growth is important for appropriate clinical management. Maximal aortic diameter is the primary metric that is used to assess growth, but it suffers from substantial measurement variability. A recently proposed technique, termed Vascular Deformation Mapping (VDM), is able to quantify three-dimensional aortic growth using clinical computed tomography angiography (CTA) data using an approach based on deformable image registration (DIR). However, the accuracy and robustness of VDM remains undefined given the lack of a ground truth from clinical CTA data, and furthermore the performance of VDM relative to standard manual diameter measurements is unknown.Methods: To evaluate the performance of the VDM pipeline for quantifying aortic growth we developed a novel and systematic evaluation process to generate 31 unique synthetic CTA growth phantoms with variable degrees and locations of aortic wall deformation. Aortic deformation was quantified using two metrics: Area Ratio (AR), defined as the ratio of surface area in triangular mesh elements, and the magnitude of deformation in the normal direction (DiN) relative to the aortic surface. Using these phantoms, we further investigated the effects on VDM's measurement accuracy resulting from factors that influence quality of clinical CTA data such as respiratory translations, slice thickness and image noise. Lastly, we compare the measurement error of VDM TAA growth assessments against two expert raters performing standard diameter measurements of synthetic phantom images.Results: Across our population of 31 synthetic growth phantoms, the median absolute error was 0.048 (IQR: 0.077-0.037) for AR and 0.138mm (IQR: 0.227-0.107mm) for DiN. Median relative error was 1.9% for AR and < 6.4% for DiN at the highest tested noise level (CNR = 2.66). Error in VDM output increased with slice thickness, i with highest median relative error of 1.4% for AR and 6.3% for DiN at slice thickness of 2.0 mm. Respiratory motion of the aorta resulted in maximal absolute error of 3% AR and 0.6 mm in DiN, but bulk translations in aortic position had a very small effect on measured AR and DiN values (relative errors < 1%). VDM-derived measurements of magnitude and location of maximal diameter change demonstrated significantly high accuracy and lower variability compared to two expert manual raters (p < 0.03 across all comparisons).Conclusions: VDM yields accurate, three-dimensional assessment of aortic growth in TAA patients and is robust to factors such as image noise, respiration-induced translations and differences in patient position. Further, VDM significantly outperformed two expert manual raters in assessing the magnitude and location of aortic growth despite optimized experimental measurement conditions. These results support validation of the VDM technique for accurate quantification of aortic growth in patients and highlight important several advantages over current measurement techniques.

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“…It has a plug-in for 3D Slicer software. It mainly uses its vtkvmtkPolyDataCenterline function to obtain the inner ear centerline model (20). The centerline model of the inner ear is converted to a series of points (Figure 1).…”

Section: Obtaining the Centerline Of Semicircular Canalmentioning

confidence: 99%

Measurement of Human Semicircular Canal Spatial Attitude

Lin

Zheng

et al. 2021

Front. Neurol.

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Located deep in the temporal bone, the semicircular canal is a subtle structure that requires a spatial coordinate system for measurement and observation. In this study, 55 semicircular canal and eyeball models were obtained by segmentation of MRI data. The spatial coordinate system was established by taking the top of the common crus and the bottom of the eyeball as the horizontal plane. First, the plane equation was established according to the centerline of the semicircular canals. Then, according to the parameters of the plane equation, the plane normal vectors were obtained. Finally, the average unit normal vector of each semicircular canal plane was obtained by calculating the average value of the vectors. The standard normal vectors of the and left posterior semicircular canal, superior semicircular canal and lateral semicircular canal were [−0.651, 0.702, 0.287], [0.749, 0.577, 0.324], [−0.017, −0.299, 0.954], [0.660, 0.702, 0.266], [−0.739, 0.588, 0.329], [0.025, −0.279, 0.960]. The different angles for the different ways of calculating the standard normal vectors of the right and left posterior semicircular canal, superior semicircular canal and lateral semicircular canal were 0.011, 0.028, 0.008, 0.011, 0.024, and 0.006 degrees. The technology for measuring the semicircular canal spatial attitudes in this study are reliable, and the measurement results can guide vestibular function examinations and help with guiding the diagnosis and treatment of BPPV.

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The Vascular Modeling Toolkit: A Python Library for the Analysis of Tubular Structures in Medical Images

Cited by 82 publications

References 4 publications

A surrogate model for plaque modeling in carotids based on Robin conditions calibrated by cine MRI data

A surrogate model for plaque modeling in carotids based on Robin conditions calibrated by cine MRI data

Validation of a robust method for quantification of three‐dimensional growth of the thoracic aorta using deformable image registration

Measurement of Human Semicircular Canal Spatial Attitude

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