Automatic identification of pulmonary lobes from imaging is important in disease assessment and treatment planning. However, the lobar fissures can be difficult to detect automatically, as they are thin, usually of fuzzy appearance and incomplete on CT scans. The fissures can also be obscured by or confused with features of disease, for example the tissue abnormalities that characterise fibrosis. Traditional anatomical knowledge-based methods rely heavily on anatomic knowledge and largely ignore individual variability, which may result in failure to segment pathological lungs. In this study, we aim to overcome difficulties in identifying pulmonary fissures by using a statistical finite element shape model of lobes to guide lobar segmentation. By deforming a principle component analysis based statistical shape model onto an individual’s lung shape, we predict the likely region of fissure locations, to initialize the search region for fissures. Then, an eigenvalue of Hessian matrix analysis and a connected component eigenvector based analysis are used to determine a set of fissure-like candidate points. A smooth multi-level β-spline curve is fitted to the most fissure-like points (those with high fissure probability) and the fitted fissure plane is extrapolated to the lung boundaries. The method was tested on 20 inspiratory and expiratory CT scans, and the results show that the algorithm performs well both in healthy young subjects and older subjects with fibrosis. The method was able to estimate the fissure location in 100% of cases, whereas two comparison segmentation softwares that use anatomy-based methods were unable to segment 7/20 and 9/20 subjects, respectively.
The interaction between mechanical obstruction and outcome in pulmonary embolism (PE) is not well quantified. Therefore a simple prognostic tool that can be used quickly in the clinical setting remains elusive. Several scoring systems have been proposed to address this problem. However, they are unable to adequately capture the functional outcomes in PE so have not been adopted widely clinically. Here we present an image-based computational model that correlates very well with measures of RV dysfunction. The model extracts the geometric features of the lung, airways, blood vessels and emboli from CTPA (computed tomography pulmonary angiogram) imaging and simulates function (perfusion, ventilation and gas exchange) within these geometries. This results in subject-specific predictions of function in 9 patients with acute PE. There is a high correlation between model results and indicators of right heart dysfunction (p=0.001 in the case of the ratio between right and left ventricular volumes and p<0.03 in the case of systolic pulmonary artery pressure estimated from echocardiography). An existing scoring system that accounts only for the mechanical obstruction of capillary bed performs less well than the model (p=0.04 in the case of the ratio between right and left ventricular volumes and p=0.23 in the case of systolic pulmonary artery pressure estimated from echocardiography). This suggests that the functional impact of occlusion must be accounted to construct useful PE scoring systems.
Volumetric computed tomography (CT) imaging provides a method of acquiring a 3-Dimensional view of lung soft tissue. The data captured in these images allows several methods of assessing the state of health of the lung. This information can prove valuable in early diagnosis of conditions where lung tissue is damaged, before external symptoms are expressed. The imaging data is also necessary for modeling lung tissue mechanics. This paper presents some analysis techniques for lung soft tissue, and uses these techniques to compare healthy lungs of young and elderly subjects.
To improve the definition of the geometrical and mechanical properties of the porcine pulmonary arteries, we utilized an <i>in vivo</i> imaging-based approach to quantify the influence of static extravascular pressure change on pulmonary arterial geometry. The cross-sectional area and distance from the inlet of pulmonary arteries of two animals were measured over a range of static airway inflation pressure (7 cmH<sub>2</sub>O - 25 cmH<sub>2</sub>O, i.e. 0.69 kPa – 2.45 kPa). Vessels with diameter range of approximately 2.0 mm to 5.5 mm at airway inflation pressure of 25 cmH<sub>2</sub>O (2.45kPa) were considered. The results suggest that lung inflation stretches the vessels laterally, but has no statistically significant effect on diameter.
Advancing technology has enabled rapid improvements in imaging and image processing techniques providing
increasing amounts of structural and functional information. While these imaging modalities now offer a wealth of
information about function within the body in health and disease certain limitations remain. We believe these can
largely be addressed through a combined medical imaging - computational modeling approach. For example, imaging
may only be performed in the prone or supine postures but humans function naturally in the upright position. We have
developed an image-based computational model of coupled tissue mechanics and pulmonary blood flow to enable
predictions of pulmonary perfusion in various postures and lung volumes. Lung and vascular geometries are derived
using a combination of imaging reconstruction and computational algorithms. Solution of finite deformation equations
provides predictions of tissue deformation and internal pressure distributions within the lung parenchyma. By
embedding vascular models within the lung volume we obtain a coupled model of blood vessel deformation as a result
of changes in lung volume. A 1D form of the Navier-Stokes flow equations are solved within the vascular model to
predict perfusion. Tissue pressures calculated from the mechanics model are incorporated into the vascular constitutive
pressure-radius relationship. Results demonstrated a relatively consistent flow distribution in all postures indicating the
large influence of branching structure on flow distribution. It is hoped that this modeling approach may provide insights
to enable interpolation of imaging measurements in alternate postures and lung volumes and enable an increased
understanding of the mechanisms influencing pulmonary perfusion distribution.
A computational model of blood flow through the human pulmonary arterial tree has been developed to investigate the relative influence of branching structure and gravity on blood flow distribution in the human lung. A geometric model of the largest arterial vessels and definitions of the lobar boundaries were first derived using multi-detector row x-ray computed tomography (MDCT) scans from the Lung Atlas. Further accompanying arterial vessels were generated from the MDCT vessel end points into the lobar volumes using a volume filling branching algorithm. A reduced form of the Navier-Stokes equations were solved within the geometric model to simulate pressure, velocity and vessel radius throughout the network. Blood flow results in the anatomically-based model, with and without gravity, and in a symmetric arterial model were compared in order to investigate their relative contributions to blood flow heterogeneity. Results showed a persistent blood flow gradient and flow heterogeneity in the absence of gravitational forces in the anatomically-based model. Results revealed that the asymmetric branching structure of the model was largely responsible for producing this heterogeneity. Analysis of average results in different slice thicknesses illustrated a clear flow gradient due to gravity in 'lower-resolution’ data (thicker slices), but on examination of higher resolution data a trend was less obvious. Results suggest that while gravity does influence flow distribution, the influence of the tree branching structure is also a dominant factor. These results are consistent with high-resolution experimental studies that have demonstrated gravity to be only a minor determinant of blood flow distribution.
A computational model of soft tissue mechanics and air flow has been developed with the aim of linking computed tomography measures of ventilation distribution to subject-specific predictions in image-based geometric (finite element) models of the lung and airway tree. Computational techniques that can deal with anatomical detail and spatially-distributed non-linear material properties have been used to couple solution of parenchymal soft tissue mechanics in an anatomically-based model of the ovine lung to predictions of flow and pressure in an embedded model of the ovine airway tree. The lung is modeled as a homogeneous, compressible, non-linear elastic body. Using equations for large deformation mechanics, the change in geometry of the lung is simulated at static inflation pressures from 25 to 0 cmH<sub>2</sub>O. Multi-detector row computed tomography imaging has been used to define the model geometry (lung and airway), to define the movement of the model lung surface during inflation, and for measurements of internal material point displacements for comparison with the predicted internal displacements of the model. This preliminary model predicts airway bifurcation point displacements that are generally in agreement with imaged displacements (total RMS error for all bifurcation points is < 4 mm from 25 to 0 cm H<sub>2</sub>O). Further development of the model will provide a predictive link between subject-specific anatomical and functional information.