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@PHDTHESIS{Louo:1015160,
author = {Loução, Ricardo},
othercontributors = {Shah, Nadim Joni and Veselinovic, Tanja},
title = {{A}nalysis of multi-b-value diffusion {MRI} data for
characterisation of healthy and pathological brain tissue},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2025-06232},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2026; Dissertation, Rheinisch-Westfälische
Technische Hochschule Aachen, 2025},
abstract = {Typically quantified by the apparent diffusion coefficient
(ADC), diffusion MRI measures water diffusion in biological
tissues which in turn is seen as a probe of microstructural
changes derived from pathology. However, the ADC is
confounded by a number of processes and phenomena rendering
it sub-optimal for the characterisation of brain tissues.
Additionally, the even more complex micro-environments of
pathologies like brain tumours do not lend themselves to be
properly summarised by a single value. In this work, two
methods for the acquisition and analysis of multi-b-value
dMRI data were developed, expanding the ADC formalism and
keeping in mind clinical feasibility. The first is a unified
model for the measurement of perfusion, and Gaussian and
non- Gaussian diffusion. This is achieved by sampling the
tissue at 16 unique b-values, from 0 to 3,000 s/mm2, while
including the clinically established b = 1,000 s/mm2. With
an acquisition time of 4 minutes and 19 seconds, four
quantities can be derived from this protocol:
pseudo-diffusivity and perfusion fraction, characterising
the perfusion regime; apparent diffusivity, describing
Gaussian diffusion; and apparent kurtosis, as a measure of
non-Gaussian diffusion. This method was validated in vivo in
a brain tumour patient cohort by pinning the derived
quantities against their counterparts from established
protocols. The metrics characterising perfusion did not
correlate with their canonical counterparts, which suggests
in part that these reflect different phenomena. Conversely,
the Gaussian and non-Gaussian quantities proposed were
highly correlated with those from the canonic protocols,
indicating that the clinical usefulness of the canonic
quantities is preserved when using the proposed metrics. The
second method is based on a parameter-free fitting of dMRI
data with b-values ranging up to 10,000 s/mm2, using
non-negative least squares. This method provides with a
diffusivity spectrum, as opposed to a fixed set of
diffusivity pools and their relative fractions, allowing for
a richer characterisation of the different diffusivity
regimes in the tissue. Data was also fitted using
established methods such as mono-, bi-, and tri-exponential
decays in order to establish the added value of the proposed
method. This method was demonstrated in a broad brain tumour
cohort, where tissue specific spectra were derived: grey
matter, white matter, cerebrospinal fluid, oedema, tumour,
and restricted diffusion lesions. The latter develops as a
consequence of the disease but it is yet poorly understood.
The proposed method is applied to try to characterise this
lesion. The suggested method was shown to better fit the
data than the fixed-term models and the derived spectra show
distinct differences in both healthy and pathologic tissue.
In conclusion, this work developed methods for the
multi-parametric characterisation of diffusion in biological
tissues, evaluated in brain tumour patients.},
cin = {535000-5 ; 934010},
ddc = {610},
cid = {$I:(DE-82)535000-5_20140620$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2025-06232},
url = {https://publications.rwth-aachen.de/record/1015160},
}
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