<strong>Purpose:</strong> Detailed characterization of pulmonary vascular anatomy has important applications for the diagnosis and management of a variety of vascular diseases. Prior efforts have emphasized using vessel segmentation to gather information on the number or branches, number of bifurcations, and branch length and volume, but accurate traversal of the vessel tree to identify and repair erroneous interconnections between adjacent branches and neighboring tree structures has not been carefully considered. In this study, we endeavor to develop and implement a successful approach to distinguishing and characterizing individual vascular trees from among a complex intermingling of trees. <strong>Methods:</strong> We developed strategies and parameters in which the algorithm identifies and repairs false branch inter-tree and intra-tree connections to traverse complicated vessel trees. A series of two-dimensional (2D) virtual datasets with a variety of interconnections were constructed for development, testing, and validation. To demonstrate the approach, a series of real 3D computed tomography (CT) lung datasets were obtained, including that of an anthropomorphic chest phantom; an adult human chest CT; a pediatric patient chest CT; and a micro-CT of an excised rat lung preparation. <strong>Results:</strong> Our method was correct in all 2D virtual test datasets. For each real 3D CT dataset, the resulting simulated vessel tree structures faithfully depicted the vessel tree structures that were originally extracted from the corresponding lung CT scans. <strong>Conclusion:</strong> We have developed a comprehensive strategy for traversing and labeling interconnected vascular trees and successfully implemented its application to pulmonary vessels observed using 3D CT images of the chest.
This work describes a robust and fast semi-automatic approach for Abdominal Aortic Aneurysm (AAA) centerline
detection. AAA is a vascular disease accompanied by progressive enlargement of the abdominal aorta, which leads to
rupture if left untreated, an event that accounts for the 13th leading cause of death in the U.S. The lumen centerline can
be used to provide the initial starting points for thrombus segmentation. Different from other methods, which are mostly
based on region growing and suffer from problems of leakage and heavy computational burden, we propose a novel
method based on online classification. An online version of the adaboost classifier based on steerable features is applied
to AAA MRI data sets with a rectangular box enclosing the lumen in the first slice. The classifier is updated during the
tracking process by using the testing result of the previous image as the new training data. Unlike traditional offline
versions, the online classifier can adjust parameters automatically when a leakage occurs. With the help of integral
images on the computation of haar-like features, the method can achieve nearly real time processing (about 2 seconds
per image on a standard workstation). Ten ruptured and ten unruptured AAA data sets were processed and the tortuosity
of the 20 centerlines was calculated. The correlation coefficient of the tortuosity was calculated to illustrate the
significance of the prediction with the proposed method. The mean relative accuracy is 95.68% with a standard deviation
of 0.89% when compared to a manual segmentation procedure. The correlation coefficient is 0.394.
Cardiovascular disease results from pathological biomechanical conditions and fatigue of the vessel wall. Image-based
computational modeling provides a physical and realistic insight into the patient-specific biomechanics and enables
accurate predictive simulations of development, growth and failure of cardiovascular disease. An experimental
validation is necessary for the evaluation and the clinical implementation of such computational models.
In the present study, we have implemented dynamic Computed-Tomography (4D-CT) imaging and catheter-based <i>in
vivo</i> measured pressures to numerically simulate and experimentally evaluate the biomechanics of the porcine aorta. The
computations are based on the Finite Element Method (FEM) and simulate the arterial wall response to the transient
pressure-based boundary condition. They are evaluated by comparing the numerically predicted wall deformation and
that calculated from the acquired 4D-CT data. The dynamic motion of the vessel is quantified by means of the hydraulic
diameter, analyzing sequences at 5% increments over the cardiac cycle.
Our results show that accurate biomechanical modeling is possible using FEM-based simulations. The RMS error of the
computed hydraulic diameter at five cross-sections of the aorta was 0.188, 0.252, 0.280, 0.237 and 0.204 mm, which is
equivalent to 1.7%, 2.3%, 2.7%, 2.3% and 2.0%, respectively, when expressed as a function of the time-averaged
hydraulic diameter measured from the CT images. The present investigation is a first attempt to simulate and validate
vessel deformation based on realistic morphological data and boundary conditions. An experimentally validated system
would help in evaluating individual therapies and optimal treatment strategies in the field of minimally invasive