Engineering a 3D ultrasound system for image-guided vascular modelling

Atherosclerosis is often diagnosed using an ultrasound (US) examination in the carotid and femoral arteries and the abdominal aorta. A decision to operate requires tw o measures of disease severity: the degree of stenosis measured using B-mode US; and the blood flow patterns in the artery measured using spectral Doppler US. However other biomech anical factors such as wall shear stress (WSS) and areas of flow recirculation are also impor tant in disease development and rupture. These are estimated using an image-guided modelling appr oach, where a three-dimensional computational mesh of the artery is simulated. To generate a patient-specific arterial 3D computational mesh, a 3D ultrasou nd (3DUS) system was developed. This system uses a standard clinical US scanner with an o ptical position sensor to measure the position of the transducer; a video capture card to record v ideo images from the scanner; and a PC running Stradwin software to reconstruct 3DUS d ata. The system was characterised using an industry-standard set of calibration phantoms, g iving a reconstruction accuracy of± 0.17 mm with a 12 MHz linear array transducer. Artery movements from pulsatile flow were reduced using a retrospective gating technique. The eff ect o pressure applied to the transducer moving and deforming the artery was reduced using an imag e-based rigid registration technique. The artery lumen found on each 3DUS image was segmented using a semi-automa tic segmentation technique known as ShIRT (the Sheffield Image Registration Toolkit). A rterial scans from healthy volunteers and patients with diagnosed arterial disease were segmented using the technique. The accuracy of the semi-automatic technique was assessed by comparing it to manual segmentation of each artery using a set of segmentation metrics. The mean accuracy of the semi-automatic technique ranged from 85% to 99% and depended on the quality of the images and the complexity of the shape of the lumen. Patient-specific 3D computational artery meshes were created using ShIRT . An idealised mesh was created using key features of the segmented 3DUS scan. This was re gistered and deformed to the rest of the segmented dataset, producing a mesh that represents the s hape of the artery. Meshes created using ShIRT were compared to meshes created using the R hino solid modelling package. ShIRT produced smoother meshes; Rhino reproduced the sh ap of arterial disease more accurately. The use of 3DUS with image-guided modelling has the potential to be an effective tool in the diagnosis of atherosclerosis. Simulations using these d ata reflect in vivo studies of wall shear stress and recirculation in diseased arteries and ar e comparable with results in the literature created using MRI and other 3DUS systems. Declaration of originality I hereby declare that the research recorded in this thesis and the thesis its lf wa composed and originated entirely by myself in the Department of Medical Physics at The University of Edinburgh. Exceptions: • The initial design of a transducer holder for the phantom scanner prese nted in chapter 2 • The development of an algorithm to measure the curvature of an artery cen treline in chapter 4 • CFD simulations described in chapter 7 • Creation of idealised artery geometries for registration-based artery mesh cr ation in chapter 7 • Creation of a diseased artery geometry using ShIRT in chapter 7