% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@PHDTHESIS{Lindemeyer:465249,
author = {Lindemeyer, Johannes},
othercontributors = {Shah, Nadim Joni and Stahl, Achim},
title = {{O}ptimisation of {P}hase {D}ata {P}rocessing for
{S}usceptibility {R}econstruction in {M}agnetic {R}esonance
{I}maging},
school = {Aachen, Techn. Hochsch.},
type = {Dissertation},
address = {Aachen},
publisher = {Publikationsserver der RWTH Aachen University},
reportid = {RWTH-2015-01614},
pages = {264 S. : Ill., graph. Darst.},
year = {2015},
note = {Aachen, Techn. Hochsch., Diss., 2015},
abstract = {Nuclear magnetic resonance (NMR) is exquisitely sensitive
to variations of the static magneticeld. Nevertheless,
historically mainstream magnetic resonance imaging
techniques have focusedon the magnitude of the complex NMR
signal only. Hence, valuable information about themagnetic
eld contained within the signal phase was not considered.
Only during the last decadehas the phase been increasingly
employed to obtain structural characteristics complementary
tothat included in the signal magnitude. Phase is inuenced
by the physical properties of the imagedobject such as
chemical shifts and in particular magnetic susceptibility
and its spatial distribution.To eciently exploit the phase
information, a number of processing steps have to be
performed.The recorded phase is ambiguous and has to be
unwrapped, data from multiple receive channelsmust be
combined, eldmaps have to be obtained and eld shifts of
external origin must beremoved in order to estimate the
susceptibility distribution in tissue. In this thesis, the
workowfor phase imaging is described step by step. Methods
for enhancing each processing step as wellas for optimising
data acquisition are investigated and the development of
several new evaluationtechniques is described.The key
algorithms developed in this work bear the acronyms URSULA
and MUBAFIRE. URSULAcombines one of the most robust known
spatial phase unwrapping strategies with
volumecompartmentalisation, allowing for reliable and fast
phase unwrapping of large data arrays asacquired at
ultra-high eld strength. MUBAFIRE corrects for background
elds that originatefrom sources residing outside of the
volume of interest. Its great performance is due to the
applicationof several sequential and complementary
background-correction strategies, each preservingphysical
validity of the solution and addressing dierent
characteristics of eld distortions.The novel algorithms
combined with methods adopted from the literature allow for
the calculationof detailed eld and susceptibility
distributions resulting in image contrast that is distinct
fromthat found in magnitude images. The applicability of the
established workow is veried in severalpost mortem brain
measurements and in studies on healthy volunteers as well as
on patients withbrain tumours or Parkinson's disease. In
particular, the challenges of performing post mortem andin
vivo imaging on a whole-body 9.4T scanner - at present, the
highest magnetic eld availablefor human phase imaging
worldwide - are met by employing the technical innovations
developedwithin the scope of this thesis.},
cin = {535000-5},
ddc = {550},
cid = {$I:(DE-82)535000-5_20140620$},
typ = {PUB:(DE-HGF)11},
urn = {urn:nbn:de:hbz:82-rwth-2015-016143},
url = {https://publications.rwth-aachen.de/record/465249},
}
guest ::