In silico modelling to advance arterial spin labelling magnetic resonance imaging
type: CreativeWork
description: Rationale Arterial spin labelling (ASL) magnetic resonance imaging (MRI) of the lungs is an imaging methodology used for the measurement of pulmonary perfusion. Unlike other pulmonary perfusion imaging techniques such as Positron emission tomography (PET), Single-photon emission computed tomography and Computed tomography (CT), ASL MRI does not involve the use of ionising radiations; thereby enabling repetitive measurements. Perfusion is quantified by obtaining a difference image whereby blood is used as an endogenous tracer and alternately tagged as ‘bright’ and ‘dark’. The difference between the bright and dark image gives the ASL magnetic resonance (MR) image. Although pulmonary ASL MRI aims to quantify perfusion, these measurements are often contaminated with artefacts such as signals from non-capillary blood. Intensity thresholding is one approach that has been proposed for minimising the noncapillary blood signal within an ASL MR image. This technique involves eliminating voxels with signal intensity above a set threshold as these encompass large vessels feeding capillary beds outside the imaging plane. This method of minimising non-capillary blood signal has been tested in a previous in silico human modelling study; however, it has only been tested under a restricted set of physiological conditions (supine posture and a cardiac output of 5 L/min). This thesis presents an in silico approach that extends previous intensity thresholding analysis to estimate the optimal ‘per-slice’ intensity threshold value under various conditions and across two species (human and pig). The intensity thresholding was examined using the individual components of the simulated ASL signal (signal arising independently from capillary blood as well as pulmonary arterial and pulmonary venous blood). This analysis aimed to assess whether the threshold value as applied in human ASL MRI should vary with slice location, posture or cardiac output. Pigs are often used for translational resea…
abstract: Rationale Arterial spin labelling (ASL) magnetic resonance imaging (MRI) of the lungs is an imaging methodology used for the measurement of pulmonary perfusion. Unlike other pulmonary perfusion imaging techniques such as Positron emission tomography (PET), Single-photon emission computed tomography and Computed tomography (CT), ASL MRI does not involve the use of ionising radiations; thereby enabling repetitive measurements. Perfusion is quantified by obtaining a difference image whereby blood is used as an endogenous tracer and alternately tagged as ‘bright’ and ‘dark’. The difference between the bright and dark image gives the ASL magnetic resonance (MR) image. Although pulmonary ASL MRI aims to quantify perfusion, these measurements are often contaminated with artefacts such as signals from non-capillary blood. Intensity thresholding is one approach that has been proposed for minimising the noncapillary blood signal within an ASL MR image. This technique involves eliminating voxels with signal intensity above a set threshold as these encompass large vessels feeding capillary beds outside the imaging plane. This method of minimising non-capillary blood signal has been tested in a previous in silico human modelling study; however, it has only been tested under a restricted set of physiological conditions (supine posture and a cardiac output of 5 L/min). This thesis presents an in silico approach that extends previous intensity thresholding analysis to estimate the optimal ‘per-slice’ intensity threshold value under various conditions and across two species (human and pig). The intensity thresholding was examined using the individual components of the simulated ASL signal (signal arising independently from capillary blood as well as pulmonary arterial and pulmonary venous blood). This analysis aimed to assess whether the threshold value as applied in human ASL MRI should vary with slice location, posture or cardiac output. Pigs are often used for translational resea…
author: Addo, Daniel Akwei
license: https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm
date published: 2020
date modified: 2026-07-24T01:03:56Z
provider: University of Auckland
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: ResearchSpace@Auckland

PropertyValue
property ID: Handle
value: https://hdl.handle.net/2292/52937