Speaker: Kin Quan and Claire Cury
UCL Contact: Dominique Drai (Visitors from outside UCL please email in advance).
Date/Time: 22 Mar 17, 13:00 - 14:00
Venue: Roberts 106
Title: Measuring the Tapering of Airways in Bronchiectasis Patients with Phantom Validation.
Bronchiectasis is defined as the permeant dilation of the airways caused by bacterial infection. At present, a diagnosis is performed by analysing a chest CT by a trained expert. We believe, that this process can be greatly improved by quantifying the morphology of the airways.
To this end, we developed a pipeline to measure the tapering of airways in CT. The pipeline was validated by 3D printed tubes of varying dimeter, curvature and tapering. The precision and accuracy of the 3D printer was verified by a micro CT scan. Furthermore, we examine the accuracy of a clinical CT scanner using a milled phantom as a ground truth. Our experiment shows that the pipeline can measure tapering to sub-voxel precision in lumen with at 2.5mm diameter scale. We find the pipeline is robust to different morphologies. Results on components of the pipeline – area measurements and centreline regularisation will also be presented. The overall goal is to use the tapering information to develop a computer assist diagnosis for bronchiectasis.
Spatio-temporal shape analysis of cross-sectional data for detection of early changes in neurodegenerative disease
The detection of pathological changes in neurodegenerative diseases that occur before clinical onset would be of great value for identifying suitable subjects and assessing drug ecacy in trials aimed at preventing or slowing onset. Using MRI derived volumetric information, researchers have been able to detect signicant dierences between patients in the presymptomatic phase of neurodegenerative diseases and healthy controls. However, volumetric studies provide only a summary representation of complex morphological changes. Shape analysis has already been successfully applied to model pathological features in neurodegeneration and represents a valuable instrument to model presymptomatic anatomical changes occurring in specic brain regions. In this study we propose a computational framework to model groupwise spatio-temporal shape dierences, and to statistically evaluate the eects of time and pathological components on the modeled variability. The proposed approach leverages the geodesic regression framework based on varifolds, and models the spatio-temporal shape variability via dimensionality reduction of the subject-specic "residual" transformations normalised in a common reference frame through parallel transport. The proposed approach is applied to patients with genetic variants of fronto-temporal dementia, and shows that shape dierences in the posterior part of the thalamus can be observed several years before the appearance of clinical symptoms.