Search

Lung biomechanical properties can be used to detect disease, assess abnormal lung function, and track disease progression. In this work, we used computed tomography (CT) imaging to measure three biomechanical properties in the lungs of subjects with varying degrees of chronic obstructive pulmonary disease (COPD): the Jacobian determinant (J), a measure of volumetric expansion or contraction; the anisotropic deformation index (ADI), a measure of the magnitude of anisotropic deformation; and the the slab-rod index (SRI), a measure of the nature of anisotropy (i.e., whether the volume is deformed to a rod-like or slab-like shape). We analyzed CT data from 247 subjects collected as part of the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS). The results show that mean $J$ and mean ADI decrease as disease severity increases, indicating less volumetric expansion and more isotropic expansion with increased disease. No differences in average SRI index were observed across the different levels of disease. The methods and analysis described in this study may provide new insights into our understanding of the biomechanical behavior of the lung for COPD patients with various Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages.

This paper examines the shape collapse problem that occurs when registering a pair of images or a population of images of the brain to a reference (target) image coordinate system using diffeomorphic image registration. Shape collapse occurs when a foreground or background structure in an image with non-zero volume is transformed into a set of zero or near zero volume as measured on a discrete voxel lattice in the target image coordinate system. Shape collapse may occur during image registration when the moving image has a structure that is either missing or does not sufficiently overlap the corresponding structure in the target image[4]. Such a problem is common in image registration algorithms with large degrees of freedom such as many diffeomorphic image registration algorithms. Shape collapse is a concern when mapping functional data. For example, loss of signal may occur when mapping functional data such as fMRI, PET, SPECT using a transformation with a shape collapse if the functional signal occurs at the collapse region. This paper proposes an novel shape collapse measurement algorithm to detect the regions of shape collapse after image registration in pairwise registration. We further compute the shape collapse for a population of pairwise transformations such as occurs when registering many images to a common atlas coordinate system. Experiments are presented using the SyN diffeomorphic image registration algorithm. We demonstrate how changing the input parameters to the SyN registration algorithm can mitigate some of the collapse image registration artifacts.

Registering lung CT images is an important problem for many applications including tracking lung motion over the breathing cycle, tracking anatomical and function changes over time, and detecting abnormal mechanical properties of the lung. This paper compares and contrasts current-and varifold-based diffeomorphic image registration approaches for registering tree-like structures of the lung. In these approaches, curve-like structures in the lung—for example, the skeletons of vessels and airways segmentation—are represented by currents or varifolds in the dual space of a Reproducing Kernel Hilbert Space (RKHS). Current and varifold representations are discretized and are parameterized via of a collection of momenta. A momenta corresponds to a line segment via the coordinates of the center of the line segment and the tangent direction of the line segment at the center. A varifold-based registration approach is similar to currents except that two varifold representations are aligned independent of the tangent vector orientation. An advantage of varifolds over currents is that the orientation of the tangent vectors can be difficult to determine especially when the vessel and airway trees are not connected. In this paper, we examine the image registration sensitivity and accuracy of current-and varifold-based registration as a function of the number and location of momentum used to represent tree like-structures in the lung. The registrations presented in this paper were generated using the Deformetrica software package ([Durrleman et al. 2014]).