Bronchiectasis is the permanent dilation of airways. Patients with the disease can suffer recurrent exacerbations, reducing their quality of life. The gold standard to diagnose and monitor bronchiectasis is accomplished by inspection of chest computed tomography (CT) scans. A clinician examines the broncho-arterial ratio to determine if an airway is brochiectatic. The visual analysis assumes the blood vessel diameter remains constant, although this assumption is disputed in the literature. We propose a simple measurement of tapering along the airways to diagnose and monitor bronchiectasis. To this end, we constructed a pipeline to measure the cross-sectional area along the airways at contiguous intervals, starting from the carina to the most distal point observable. Using a phantom with calibrated 3D printed structures, the precision and accuracy of our algorithm extends to the sub voxel level. The tapering measurement is robust to bifurcations along the airway and was applied to chest CT images acquired in clinical practice. The result is a statistical difference in tapering rate between airways with bronchiectasis and controls.
Biomechanical modelling enables large deformation simulations of breast tissues under different loading conditions to be performed. Such simulations can be utilised to transform prone Magnetic Resonance (MR) images into a different patient position, such as upright or supine. We present a novel integration of biomechanical modelling with a surface registration algorithm which optimises the unknown material parameters of a biomechanical model and performs a subsequent regularised surface alignment. This allows deformations induced by effects other than gravity, such as those due to contact of the breast and MR coil, to be reversed. Correction displacements are applied to the biomechanical model enabling transformation of the original pre-surgical images to the corresponding target position.
The algorithm is evaluated for the prone-to-supine case using prone MR images and the skin outline of supine Computed Tomography (CT) scans for three patients. A mean target registration error (TRE) of 10:9 mm for internal structures is achieved. For the prone-to-upright scenario, an optical 3D surface scan of one patient is used as a registration target and the nipple distances after alignment between the transformed MRI and the surface are 10:1 mm and 6:3 mm respectively.
In biomechanical simulations of the human breast, the analysed geometry is often reconstructed from in vivo medical imaging procedures. For example in dynamic contrast enhanced magnetic resonance imaging, the acquired geometry of the patient's breast when lying in the prone position represents a deformed configuration that is pre-stressed by typical in vivo conditions and gravity. Thus, physically realistic simulations require consideration of this loading and, hence, establishing the undeformed configuration is an important task for accurate and reliable biomechanical modelling of the breast. We compare three different numerical approaches to recover the unloaded configuration from the loaded geometry given patient-specific biomechanical models built from prone and supine MR images. The algorithms compared are:(i) the simple inversion of gravity without the consideration of pre-stresses, (ii) an inversefinite deformation approach and (iii) afixed point type iterative approach which uses only forward simulations. It is shown that the iterative and the inverse approach produce similar zero-gravity estimates, where as the simple inversion of gravity is only appropriate for small or highly constrained deformations.