Engineering a 3D ultrasound system for image-guided vascular modelling
View/ Open
Hammer2009LaTex_files.zip (108.9Mb)
Date
2009Author
Hammer, Steven James
Metadata
Abstract
Atherosclerosis is often diagnosed using an ultrasound (US) examination in the carotid and
femoral arteries and the abdominal aorta. A decision to operate requires two 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 biomechanical factors such
as wall shear stress (WSS) and areas of flow recirculation are also important in disease development
and rupture. These are estimated using an image-guided modelling approach, where a
three-dimensional computational mesh of the artery is simulated.
To generate a patient-specific arterial 3D computational mesh, a 3D ultrasound (3DUS) system
was developed. This system uses a standard clinical US scanner with an optical position sensor
to measure the position of the transducer; a video capture card to record video images from
the scanner; and a PC running Stradwin software to reconstruct 3DUS data. The system was
characterised using an industry-standard set of calibration phantoms, giving a reconstruction
accuracy of ± 0.17 mm with a 12MHz linear array transducer. Artery movements from pulsatile
flow were reduced using a retrospective gating technique. The effect of pressure applied
to the transducer moving and deforming the artery was reduced using an image-based rigid
registration technique.
The artery lumen found on each 3DUS image was segmented using a semi-automatic segmentation
technique known as ShIRT (the Sheffield Image Registration Toolkit). Arterial 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 registered and deformed
to the rest of the segmented dataset, producing a mesh that represents the shape of the artery.
Meshes created using ShIRT were compared to meshes created using the Rhino solid modelling
package. ShIRT produced smoother meshes; Rhino reproduced the shape 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 data reflect in vivo
studies of wall shear stress and recirculation in diseased arteries and are comparable with results
in the literature created using MRI and other 3DUS systems.