| dc.description.abstracteng | Magnetic resonance imaging (MRI) is a versatile imaging technology with a broad variety of
biomedical and clinical applications.
It provides an excellent soft tissue contrast without the need for
ionizing radiation or radioactive materials as required in
modalities like computed tomography (CT) and positron emission tomography (PET).
In general, the image contrast in MRI depends on tissue properties
and hardware characteristics as well as the measurement technique.
Thus, conventional MRI provides only a qualitative image contrast
where images are interpreted based on relative intensity differences.
In contrast, in quantitative MRI (QMRI) physical properties such as
relaxation constants, flow velocities, temperatures, or diffusion coefficients
are determined in physical units.
From a clinical perspective, QMRI is relevant for classification,
detection, and monitoring of abnormal tissue.
It can be used to detect subtle changes
that are not observable using conventional MRI and has the
potential to replace measurements which require the use of contrast agents,
benefiting otherwise excluded patients.
From a methodological perspective,
measuring physical quantities adds
robustness of the acquired data against variations
in the scanner hardware, the operating personnel or the software.
It improves the longitudinal and inter-site comparability
of results and thereby increases the reproducibility of studies.
Most existing techniques in QMRI rely on special sequences
designed for high sensitivity to specific physical quantities,
while being robust against other influences.
They utilize analytical signal representations
that are used in an additional fitting step to extract quantitative maps
from conventionally reconstructed images.
The focus on robust sequences makes conventional
QMRI methods very accurate in measuring
specific physical quantities such as the T1 and T2 relaxation constants.
Yet, this comes with a severe downside:
The requirement to first obtain a number of high quality intermediate images
for pixel-wise fitting leads to long measurement protocols
which are then not feasible in a clinical setting.
By exploiting the data in a more efficient way,
e.g., by incorporating prior knowledge, it is possible to
reduce the amount of data required for reconstruction, and, consequently,
shorten the measuring time.
This can be achieved with model-based reconstructions
that bypass the reconstruction of intermediate images completely
by formulating the image estimation as an inverse problem
and by incorporating the physical signal model directly
into the image
reconstruction.
Thus, the acquisition of redundant information is avoided,
which reduces the measurement time substantially.
Model-based reconstruction methods rely on sequences
with analytical signal models,
which are convenient to use with numerical optimization algorithms
applied in image reconstructions.
These signal models are often derived using assumptions
that limit their accuracy by excluding certain physical effects
of the magnetization during the acquisition.
Furthermore, the requirement of analytical models restricts the
application of model-based reconstructions to specific MRI measurements.
Several efficient sequences that are simultaneously sensitive to multiple parameters
have complicated, often non-analytical signal expressions which prevent their use in
established model-based reconstruction schemes.
The acquisition of multiple parameter maps then requires multiple
scans, which -even with shorter measurements- still presents
a challenge in a clinical setting.
The aim of this thesis is to develop a generic model-based reconstruction method
for quantitative mapping of multiple parameters with
arbitrary MRI sequences.
Building on a previous proof-of-principle study,
a complete framework for QMRI that uses model-based reconstruction
with the Bloch equations is developed and validated.
The Bloch equations describe the behavior of nuclear spins
under the influence of external magnetic fields and
can be used to describe most MRI experiments.
To integrate this into a practical reconstruction framework,
a generic technique for the solution of the Bloch equations is described
that efficiently exploits repeated patterns of the MRI measurement
by pre-computation of state-transition matrices.
This is combined with a direct sensitivity analysis for the
computation of the partial derivatives that are required for numerical optimization.
These techniques were then integrated into a calibration-less model-based
reconstruction framework, which establishes a versatile and generic tool for QMRI.
The technique was validated using simulations, phantom scans, and in vivo experiments. | de |