| Search the Collection|Browse Subjects|Services|Library Information|Community |News & Events | |
|
|
|
|
|
|
|
|
||||
|
|||||||
Type of Document Dissertation Author Wu, Yang , Author's Email Address ywu8@ncsu.edu URN etd-12062005-135014 Title Signal Processing Tools of MRI Perfusion-weighted Imaging Data Analysis Degree PhD Graduate Program Electrical Engineering Advisory Committee
Advisor Name Title Hamid Krim Committee Chair Brian L. Hughes Committee Member Griff Bilbro Committee Member Jeffrey Macdonald Committee Member Kazufumi Ito Committee Member Weili Lin Committee Member Keywords
- flow heterogeneity
- vasucular transport function
- CBF
- recirculation
- DSC
- MRI
- Perfusion-weighted imaging
Date of Defense 2005-12-06 Availability unrestricted Abstract In dynamic susceptibility contrast (DSC) magnetic resonance(MR) approaches, by injecting a bolus of paramagnetic contrast agent intravenously,
the measured MR signal is converted to a concentration time course
to estimate hemodynamic parameters like cerebral blood flow (CBF),
cerebral blood volume (CBV) and mean transit time (MTT).
Before estimating hemodynamic parameters, recirculation effects
need to be removed by a gamma-variate fit of the concentration
curve. In this dissertation, however, it has been found and
demonstrated by simulation that fitting may not discern
recirculation from the first-pass in case of cerebral ischemia. A
new methodology using temporal independent component analysis
(ICA) to remove recirculation in both normal and ischemic brain
tissues while preserving the first-pass is therefore proposed.
This should improve hemodynamics accuracy particularly in ischemic
lesions.
In DSC MR approaches, bolus delays between the arterial input
function (AIF) and tissue curves may induce significant CBF
quantification error. Our second contribution is using ICA to
estimate bolus arrival time for each 5x5 region of interest
(ROI) throughout the brain parenchyma. A global AIF measured from
a major artery can then be shifted in accordance to define a local
AIF for each ROI. The bolus delay may therefore be minimized, and
the general shape of the AIF is preserved. This should improve the
flow quantification.
Transfer function has been widely used to characterize an unknown
system. In DSC MR approaches, vascular transfer function (VTF)
represents the probability density function of the vascular
transit time. Our third contribution is to propose a new tool to
estimate intracranial VTF non-invasively. This should provide an
alterative means of assessing tissue perfusion status,
particularly in patients with cerebrovascular diseases.
Bolus dispersion between the AIF and tissue curves may induce flow
quantification error, which cannot be minimized without the
knowledge of vasculature. Our final contribution is to develop an
extended cerebral vascular model to minimize delay and dispersion
dependence by modelling flow heterogeneity in both bulk small
arteries and capillary bed. This should yield more stable flow
rates less sensitive to bolus delay and dispersion.
Files
Filename Size Approximate Download Time (Hours:Minutes:Seconds)
28.8 Modem 56K Modem ISDN (64 Kb) ISDN (128 Kb) Higher-speed Access etd.pdf 1.88 Mb 00:08:42 00:04:28 00:03:55 00:01:57 00:00:10