Combined Segmentation and Hemodynamic Analysis of Cerebrovascular Structures using Arterial Spin Labeling

Phellan Aro, Renzo

Combined Segmentation and Hemodynamic Analysis of Cerebrovascular Structures using Arterial Spin Labeling

dc.contributor.advisor	Forkert, Nils Daniel
dc.contributor.author	Phellan Aro, Renzo
dc.contributor.committeemember	Frayne, Richard
dc.contributor.committeemember	Walker, Richard E. A.
dc.contributor.committeemember	Lebel, Robert Marc
dc.contributor.committeemember	Far, Behrouz H.
dc.contributor.committeemember	Duong, Luc
dc.date	2020-06
dc.date.accessioned	2020-01-20T22:25:47Z
dc.date.available	2020-01-20T22:25:47Z
dc.date.issued	2020-01-17
dc.description.abstract	Spatiotemporal arterial spin labeling magnetic resonance angiography (4D ASL MRA) is a non-invasive imaging modality used to acquire dynamic images of cerebrovascular structures. It can achieve high spatial and temporal resolution, while capturing morphological and blood flow data. Recent scientific studies have used 4D ASL MRA to analyze the cerebrovascular system for characterization, diagnosis, and post-treatment assessment of different cerebrovascular diseases, such as aneurysms, arteriovenous malformations, and moyamoya disease. However, this image sequence generates a considerable amount of data, which can be tedious to analyze by simple visual inspection, a problem also present with other 4D imaging methods. In this case, medical image processing methods can be used to extract the morphological and blood flow data contained in 4D ASL MRA datasets and present it in a more useful format to clinicians and researchers. The aim of this work was to develop and evaluate novel image processing methods for advanced analysis of 4D MRA datasets. The overreaching idea for the development of the corresponding methods is to use blood flow information for improving the vessel segmentation while the vessel segmentation is used to improve the results of the hemodynamic analysis. It was hypothesized that this combined analysis improves the vessel segmentation and results the hemodynamic analysis at the same time. The methods were developed and evaluated using 15 datasets of healthy volunteers, flow phantom measurements, and two datasets of patients with a stenosis. The findings of this work indicate that the proposed combined segmentation and hemodynamic analysis can improve the overall accuracy of the segmentation and blood flow parameter estimation while first experiments also show that the proposed methods can be applied to patients with a cerebrovascular disease. The methods developed in this work could help translating 4D ASL MRA datasets into clinical practice and support clinical research of various cerebrovascular diseases using 4D ASL MRA while the developed methods have also the potential to be useful for other 4D imaging sequences.	en_US
dc.identifier.citation	Phellan Aro, R. (2020). Combined Segmentation and Hemodynamic Analysis of Cerebrovascular Structures using Arterial Spin Labeling (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/37479
dc.identifier.uri	http://hdl.handle.net/1880/111527
dc.language.iso	eng	en_US
dc.publisher.faculty	Schulich School of Engineering	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.	en_US
dc.subject	Biomedical engineering	en_US
dc.subject	Cerebrovascular segmentation	en_US
dc.subject	Hemodynamics	en_US
dc.subject.classification	Engineering--Biomedical	en_US
dc.title	Combined Segmentation and Hemodynamic Analysis of Cerebrovascular Structures using Arterial Spin Labeling	en_US
dc.type	doctoral thesis	en_US
thesis.degree.discipline	Engineering – Biomedical	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Doctor of Philosophy (PhD)	en_US
ucalgary.item.requestcopy	true	en_US