University of Illinois Urbana-Champaign

Towards imaging cerebral microvascular blood flow using MRI

Ouyang, Cheng

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Ouyang_Cheng.pdf

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https://hdl.handle.net/2142/29792

Description

Title
Towards imaging cerebral microvascular blood flow using MRI
Author(s)
Ouyang, Cheng
Issue Date
2012-02-06T20:17:22Z
Director of Research (if dissertation) or Advisor (if thesis)
Sutton, Bradley P.
Doctoral Committee Chair(s)
Sutton, Bradley P.
Committee Member(s)
  • Liang, Zhi-Pei
  • Georgiadis, John G.
  • Gratton, Gabriele
Department of Study
Bioengineering
Discipline
Bioengineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2012-02-06T20:17:22Z
Keyword(s)
  • microvascular blood flow
  • arterial spin labeling
  • flow-enhanced signal intensity (FENSI)
  • pseudo-continuous transfer insensitive labeling technique (pTILT)
  • slice profile artifact
  • labeling efficiency
  • regional perfusion imaging
  • magnetic resonance imaging (MRI)
Abstract
Microvasculature consists of arterioles, capillaries and venules, in which the blood flow velocity is slow (≤ 2 cm/s). The hemodynamic signals, blood oxygenation-level dependent response, cerebral blood flow, cerebral blood volume and cerebral metabolic rate of oxygen are the common surrogate markers to detect brain neural activities in many neuroimaging modalities. These imaging signals originate from the microvasculature, which is closely tied to neuronal activity. In this study, two novel flow imaging techniques have been developed to investigate the localized cerebral microvascular blood flow and flow changes associated with aging, named (i) pseudo-continuous transfer insensitive labeling technique (pTILT) and (ii) flow-enhanced signal intensity (FENSI). pTILT is an advanced pseudo-continuous ASL approach which is developed from the traditional pulsed ASL method of transfer insensitive labeling technique (TILT). pTILT improves perfusion acquisitions by 1) realizing pseudo-continuous tagging with non-adiabatic pulses, 2) being sensitive to slow flows in addition to fast flows, 3) providing flexible labeling geometries, 4) generating low power deposition at high magnetic strength of 3 T, and 5) significantly reducing slice profile artifacts in flow measurements, and 6) being more immune to arterial transit time effects compared to the existing continuous or pseudo-continuous ASL methods. To optimize the pTILT perfusion imaging in the presence of magnetic field inhomogeneities, an acquisition and estimation method is developed to obtain accurate perfusion measures with improved labeling efficiency and higher experimental signal-to-noise ratio. The microvascular flow-based functional imaging method of FENSI 1) is an extension of a diffusion enhancement method from MR microscopy (DESIRE), 2) offers a highly-localized flow signal by tagging a thin slice inside the imaging slice, 3) can have an arbitrary tagging plane, 4) tag flowing spins very close to or inside the microvasculature (arterioles, capillaries and venules) with slow velocity, 5) provides a both velocity- and direction-sensitive signal enhancement of the flow. There are two versions of FENSI: the first one is qualitative FENSI which is used as a flow-based functional MRI technique; FENSI is further extended and revised from a qualitative functional MRI tool to a quantitative imaging technique, which is capable of providing absolute localized blood flow maps free from magnetization transfer and slice profile errors and calculating microvascular flow as volume flux in the units of µL/min/cm2. Both pTILT and FENSI are validated as reliable and sensitive blood flow imaging approaches by demonstrating that: 1) consistent flow measurements can be obtained for healthy human subjects during resting state scans, and 2) significant flow changes can be detected when the brain is activated during functional stimulus. Finally, flow maps acquired with pTILT are used as physiological biomarkers to evaluate blood flow changes with aging.
Graduation Semester
2011-12
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http://hdl.handle.net/2142/29792
Copyright and License Information
Copyright 2011 Cheng Ouyang

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