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Wake Forest University

DEVELOPMENT OF IMPROVED ARTERIAL SPIN LABELING METHODS IN HUMANS AND ANIMAL MODELS

Abstract

dc:description.abstract

Arterial spin labeling (ASL) is an important tool in evaluating cerebral blood flow (CBF), with applications in clinical imaging for brain tumors and stroke, and as a research tool for brain injury and Alzheimers disease. Multi-TI ASL makes it possible to measure CBF and arterial transit time (ATT). First, multi-TI ASL is optimized for a swine model. For human applications, limitations of current multi-TI ASL techniques include low SNR, inefficient acquisition, and requiring separate M0 and T1 images. We introduce two new methods, Multi-TI Integrated ASL and ASL with added arterial cerebral blood volume estimation, which address these limitations. The first method acquires very short TIs with shortened bolus durations which allow estimation of T1 and M0 without a separate scan, and shortens TR when possible for a shortened scan time. The second method also uses multi-TI ASL acquisition with many shortened TIs for estimation of arterial cerebral blood flow (aCBV) along with CBF and ATT. Both these methods improve scanning efficiency and allow for estimation of additional parameters. Both simulations and scanner experiments are described which validate these new methods and hold great promise in better assessment of CBF, aCBV, and ATT.

Degree

thesis:*
Grantor dc:publisher
Wake Forest University
Year dc:date.issued
2016

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Johnston, Megan E.

Subjects

dc:subject × 1

Rights

Language dc:language.iso
en

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/10339/64175
OAI identifier oai:identifier
oai:wakespace.lib.wfu.edu:10339/64175

Chain of custody

source
Harvested from
Wake Forest University
Base URL
wakespace.lib.wfu.edu/oai/request
Last updated
2026-07-27
Source record
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citation

Johnston, Megan E.. DEVELOPMENT OF IMPROVED ARTERIAL SPIN LABELING METHODS IN HUMANS AND ANIMAL MODELS. Wake Forest University, 2016. http://hdl.handle.net/10339/64175