Reconstruction Of Vascular Trees Research Articles

OS085 - Complete vascular tree reconstruction in rat decellularized liver scaffolds using differential recellularization pressures

OS085 - Complete vascular tree reconstruction in rat decellularized liver scaffolds using differential recellularization pressures

Validation of parametric mesh generation for subject-specific cerebroarterial trees using modified Hausdorff distance metrics

Accurate subject-specific vascular network reconstruction is a critical task for the hemodynamic analysis of cerebroarterial circulation. Vascular skeletonization and computational mesh generation for large sections of cerebrovascular trees from magnetic resonance angiography (MRA) is an error-prone, operator-dependent, and very time-consuming task. Validation of reconstructed computational models is essential to ascertain their accuracy and precision, which directly relates to the confidence of CFD computations performed on these meshes. The aim of this study is to generate an imaging segmentation pipeline to validate and quantify the spatial accuracy of computational models of subject-specific cerebral arterial trees. We used a recently introduced parametric structured mesh (PSM) generation method to automatically reconstruct six subject-specific cerebral arterial trees containing 1364 vessels and 571 bifurcations. By automatically extracting sampling frames for all vascular segments and bifurcations, we quantify the spatial accuracy of PSM against the original MRA images. Our comprehensive study correlates lumen area, pixel-based statistical analysis, area overlap and centerline accuracy measurements. In addition, we propose a new metric, the pointwise offset surface distance metric (PSD), to quantify the spatial alignment between dimensions of reconstructed arteries and bifurcations with in-vivo data with the ability to quantify the over- and under-approximation of the reconstructed models. Accurate reconstruction of vascular trees can a practical process tool for morphological analysis of large patient data banks, such as medical record files in hospitals, or subject-specific hemodynamic simulations of the cerebral arterial circulation.

A new approach to blood flow simulation in vascular networks

A proper analysis of blood flow is contingent upon accurate modelling of the branching pattern and vascular geometry of the network of interest. It is challenging to reconstruct the entire vascular network of any organ experimentally, in particular the pulmonary vasculature, because of its very high number of vessels, complexity of the branching pattern and poor accessibility in vivo. The objective of our research is to develop an innovative approach for the reconstruction of the full pulmonary vascular tree from available morphometric data. Our method consists of the use of morphometric data on those parts of the pulmonary vascular tree that are too small to reconstruct by medical imaging methods. This method is a three-step technique that reconstructs the entire pulmonary arterial tree down to the capillary bed. Vessels greater than 2 mm are reconstructed from direct volume and surface analysis using contrast-enhanced computed tomography. Vessels smaller than 2 mm are reconstructed from available morphometric and distensibility data and rearranged by applying Murray's laws. Implementation of morphometric data to reconstruct the branching pattern and applying Murray's laws to every vessel bifurcation simultaneously leads to an accurate vascular tree reconstruction. The reconstruction algorithm generates full arterial tree topography down to the first capillary bifurcation. Geometry of each order of the vascular tree is generated separately to minimize the construction and simulation time. The node-to-node connectivity along with the diameter and length of every vessel segment is established and order numbers, according to the diameter-defined Strahler system, are assigned. In conclusion, the present model provides a morphological foundation for future analysis of blood flow in the pulmonary circulation

Vascular tree reconstruction with discrete tomography: intensity based camera correction for 3D reconstruction

This paper is concerned with the reconstruction of vascular trees from few projections using discrete tomography. However, its computational cost is high and it lacks robustness when the data are inconsistent. We improve robustness by incorporating an intensity-based camera-correction method. The proposed approach is also capable of handling small motion artifacts by modeling them as repositionings of a virtual X-ray camera. We also present a parallel implementation which substantially reduces reconstruction time. We propose a data-driven reduction of positional inconsistencies by minimizing the reconstruction residual to increase the robustness. Inspired by motion compen-sation algorithms in SPECT imaging, we combine an intensity-based 2D/3D-registration method with itera-tive reconstruction methods. Our objective is the robust vascular-tree reconstruction from positionally inconsistent data. The speed of the reconstruction is substantially increased by a volume-splitting scheme that allows parallel processing. Vascular trees in the liver can be accurately reconstructed from few positionally inconsistent projections using digitally reconstructed radiographs. We have tested the proposed method on synthetic projection data and on objects imaged with a new robotized C-arm. We measured a decrease in the average reconstruction residual of about 13% for real data compared to projection data without preprocessing. Over 4,600 reconstruction experiments were conducted to evaluate the speed-up obtained when employing the volume-splitting scheme. Reconstruction time decreased linearly with increased number of processor-cores, both for real and synthetic data. The proposed method reduces inconsistencies caused by positioning errors and small motion artifacts. No prior segmentation or detection of correspondences between projections is necessary, because all algorithms are intensity-based. As a result, the proposed method allows for robust, high-quality reconstructions, while reducing radiation dose substantially.

Four-Dimensional Vascular Tree Reconstruction Using Moving Grid Deformation

Thinned perforator flaps have been widely used in plastic surgery for greater survivability and decreased morbidity. However, quantitative analysis of three-dimensional (3D) blood flow direction and location has not been examined yet. Such information will benefit and guide the surgical thinning and dissection process. Toward this goal, this study was performed for 3D vascular tree reconstruction with the incorporation of temporal contrast-agent propagation information (three spatial dimensions plus one temporal dimension; ie, 4D). A novel computational framework by adopting a moving grid deformation method is presented. To take advantage of temporal information of the bolus propagating, a sequential segmentation procedure is proposed. Moreover, the temporal evolution of the vascular tree (4D vascular tree) is reconstructed during the procedure. Eight anterolateral thigh perforator flaps from eight cadavers were used for this study. The age range is 60-80 years old and the gender includes four males and four females. The 3D nature of the vascular structure and 4D vascular tree evolving process are showed in comparison with maximum intensity projection images. The proposed computational framework demonstrates effectiveness in the modeling of 4D vascular tree. Furthermore, it reveals the ability to detect small vessel tree structures that are beyond the limit of image resolution.

Vascular Modeling from Volumetric Diagnostic Data: A Review

Reconstruction of vascular trees from digital diagnostic images is a challenging task in the development of tools for simulation and procedural planning for clinical use. Improvements in quality and resolution of acquisition modalities are constantly increasing the fields of application of computer assisted techniques for vascular modeling and a lot of Computer Vision and Computer Graphics research groups are currently active in the field, developing methodologies, algorithms and software prototypes able to recover models of branches of human vascular system from different kinds of input images. Reconstruction methods can be extremely different according to image type, accuracy requirements and level of automation. Some technologies have been validated and are available on medical workstation, others have still to be validated in clinical environments. It is difficult, therefore, to give a complete overview of the different approach used and results obtained, this paper just presents a short review including some examples of the principal reconstruction approaches proposed for vascular reconstruction, showing also the contribution given to the field by the Medical Application Area of CRS4, where methods to recover vascular models have been implemented and used for blood flow analysis, quantitative diagnosis and surgical planning tools based on Virtual Reality. Keywords: Vessel, CT, MRI, Segmentation, 3D

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

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.

Automatic 3D vascular tree construction in CT angiography

This study presents an automatic method for 3D reconstruction of vascular trees using computed-tomography angiographic (CTA) images. The program starts with the CTA slices, performs a sequential procedure of 3D image formation, preprocessing, segmentation, thinning, skeleton pruning and tree construction. It ends with vascular trees along with quantitative data about the trees such as values of diameter, length and bifurcation angles. All the involved algorithms are presented with the emphasis given to the skeleton pruning and tree construction algorithms. The skeletons obtained using a 3D thinning algorithm may contain cycles, spurs, isolated sticks, and non-unit-width parts, which hinder tree construction. As a solution to this problem, a skeleton pruning and tree construction algorithm is proposed. At each stage of the automatic procedure, 3D rendering is provided for visual inspection of the computed results. In the final output, the constructed vascular trees are visualized by rendering the 3D trees and the 3D binary image together in a transparent display mode. The program is carried out in a fully automatic fashion, with a few default settings. Occasionally, user intervention is needed at the 3D segmentation stage to impose an appropriate threshold when the automatic 3D segmentation is obviously sub-optimal for vessel delineation. Experimental demonstrations on both coronary artery phantom and a cast of coronary artery tree of a swine animal model are provided.

The coronary vasculature and its reconstruction.

Recently, we have developed several new innovations in the morphometry of vascular trees. These innovations have been used to study the anatomy of the coronary circulation in the pig and have yielded a complete set of morphometric data on the entire coronary vasculature. Since, the innovations are applicable to any organ that has a tree-like vasculature, their utility in describing the quantitative anatomy of intraorgan vasculature is evident. These advancements in morphometry along with the automation of vascular tree reconstruction, data analysis, and hemodynamic applications should make the data base on intraorgan vasculature more abundant and more useful. The morphometric data will contribute to the Circulatory Network Physiome that will serve as an anatomical basis for the Physiome Project. This article covers several topics: (1) intraorgan vascular trees in terms of their anatomy, mechanical properties, physiological behavior, and adaptation, (2) reconstruction of the coronary vasculature, and (3) some of the shortcomings of the present morphometric data base and some proposed remedies. Finally, a discussion of the constitution of the circulatory network physiome in terms of the available morphometric data on intraorgan vasculature will be presented.

Three-dimensional reconstruction of vascular trees: experimental evaluation.

This paper is the second of two that together present a novel approach to the problem of reconstructing vascular trees from a small number of projections. Previously, we described the reconstruction algorithm and how it effectively circumvents the matching or "correspondence problem" found in most photogrammetric or computer-vision-based approaches. The algorithm is fully automatic and assumes that the imaging geometry is known, the vascular tree is a connected structure, and that its center-lines have been identified in three or more images. It employs consistency and connectivity constraints and comprises three steps: The first generates a connected structure representing the multiplicity of solutions that are consistent with the first two views; the second assigns a measure of agreement to each branch in this structure based on one or more additional projections; and the third step employs this measure to distinguish between those branches comprising the vasculature and the accompanying artifacts. This paper addresses the issue of validation via simulations and experiments. In addition to a clinical case, we examine the performance of the algorithm when applied to simulated projections of two 3-D vascular models, both representative of the complexity faced in coronary and cerebral angiography. The results in each instance are impressive and demonstrate that adequate reconstructions may be obtained with as few as three distinct views.

Three-dimensional reconstruction of vascular trees. Theory and methodology.

In this paper we examine the few-view reconstruction problem as it applies to imaging vascular trees. A fully automated reconstruction algorithm is described that circumvents the traditional "correspondence problem," using only notions of consistency and connectivity. It is assumed that the vascular tree is a connected structure and that its centerlines have been identified in three or more images. The first of three steps in the procedure involves generating a connected structure that represents the multiplicity of solutions that are consistent with any two (different) projections. The second step assigns to each branch in this structure a measure of agreement based on its relationship with one or more additional views of the vasculature. The problem then becomes one of propagating this information, via connectivity relationships and consistency checks, throughout the above structure to distinguish between the branches comprising the imaged structure and the accompanying artifacts. In this paper we present the theory and methodology of the technique, while in a companion paper we address the issue of validation via simulations and experiments. Together, these papers shed some light on why ambiguities arise and often lead to errors in the few-view reconstruction problem. Strategies to handle these errors are described and results are presented that demonstrate the ability to obtain adequate reconstructions with as few as three distinct views.

Vascular network segmentation in subtraction angiograms: a comparative study

The three-dimensional reconstruction of vascular trees from a very limited number of two-dimensional projections is an active research field. The quality of the results (resolution in terms of vessel size and geometrical location) is highly dependent on the overall distortions introduced in the data acquisition process but is also related to the reliability of feature detection. A new approach based on mathematical morphology is proposed in this paper for extracting the centrelines and edges of coronary vessels from digital subtracted angiograms. These results are compared with those from an alternative method based on vectorial tracking and a directed contour finder.