13,415 research outputs found
Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and spatial regularization
Spherical deconvolution (SD) methods are widely used to estimate the
intra-voxel white-matter fiber orientations from diffusion MRI data. However,
while some of these methods assume a zero-mean Gaussian distribution for the
underlying noise, its real distribution is known to be non-Gaussian and to
depend on the methodology used to combine multichannel signals. Indeed, the two
prevailing methods for multichannel signal combination lead to Rician and
noncentral Chi noise distributions. Here we develop a Robust and Unbiased
Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with
realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to
Rician and noncentral Chi likelihood models. To quantify the benefits of using
proper noise models, RUMBA-SD was compared with dRL-SD, a well-established
method based on the RL algorithm for Gaussian noise. Another aim of the study
was to quantify the impact of including a total variation (TV) spatial
regularization term in the estimation framework. To do this, we developed TV
spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The
evaluation was performed by comparing various quality metrics on 132
three-dimensional synthetic phantoms involving different inter-fiber angles and
volume fractions, which were contaminated with noise mimicking patterns
generated by data processing in multichannel scanners. The results demonstrate
that the inclusion of proper likelihood models leads to an increased ability to
resolve fiber crossings with smaller inter-fiber angles and to better detect
non-dominant fibers. The inclusion of TV regularization dramatically improved
the resolution power of both techniques. The above findings were also verified
in brain data
Arbeiten zur Optischen Kohärenztomographie, Magnetresonanzspektroskopie und Ultrahochfeld-Magnetresonanztomographie
Abstrakt (Deutsch)
Hintergrund: Die Multiple Sklerose ist eine der häufigsten neurologischen Erkrankungen, die zu Behinderung bereits im jungen Erwachsenenalter führen kann. Hierzu tragen im Krankheitsprozess sowohl neuroinflammatorische wie auch neurodegenerative Komponenten bei. Moderne bildgebende Verfahren wie die Ultrahochfeld-Magnetresonanztomographie (UHF-MRT), die Optische Kohärenztomographie (OCT) und die Magnetresonanzspektroskopie (MRS) können benutzt werden, um diese neurodegenerativen Prozesse näher zu charakterisieren und im zeitlichen Verlauf zu beobachten.
Zielsetzung: Ziel ist es, die genannten Verfahren zur Charakterisierung von Kohorten von MS-Patienten einzusetzen und die Verfahren zueinander, sowie mit klinischen Parametern in Beziehung zu setzen oder diagnostisch zu nutzen.
Methodik: Patienten mit Multipler Sklerose oder Neuromyelitis optica wurden klinisch-neurologisch, mit Optischer Kohärenztomographie, Sehprüfungen, Untersuchungen der visuell evozierten Potentiale (VEP), (Ultrahochfeld-) Magnetresonanztomographie und Magnetresonanzspektroskopie untersucht.
Ergebnisse: Die in der Studie eingesetzten bildgebenden Verfahren konnten dazu beitragen, Neuroinflammation und Neurodegeneration bei an Multiple Sklerose erkrankten Patienten näher zu charakterisieren. So steht eine mittels OCT messbare Verdünnung retinaler Nervenfaserschichten (RNFL) in Zusammenhang mit dem per MRT gemessenen Hirnparenchymvolumen und Neurodegeneration anzeigenden Parametern, die mithilfe der Magnetresonanzspektroskopie untersucht wurden. Mithilfe der UHF-MRT konnte ein Zusammenhang zwischen dem Volumen und der entzündlichen Läsionslast der Sehstrahlung, der RNFL-Dicke, VEP-Latenzen und Einschränkungen des Sehvermögens dargestellt werden. Außerdem ließen sich mit der UHF-MRT auch neurogenerative Aspekte im Sinne von bleibenden Parenchymdefekten innerhalb entzündlicher Läsionen und einer Verschmächtigung der Sehstrahlung nachweisen und die Detektion insbesondere kortikaler MS-Läsionen wurde im Vergleich zur konventionellen MRT verbessert.
Zusammenfassung: OCT, MRS und UHF-MRT sind Verfahren, die eine genauere Beschreibung von Neuroinflammation und Neurodegeneration bei MS-Patienten ermöglichen, wie hier vor allem für die Sehbahn gezeigt wurde. Sie sind nichtinvasiv und lassen sich zur näheren Charakterisierung des aktuellen Zustandes und zur Beobachtung des Krankheitsverlaufs von MS-Patienten benutzen.Abstract (English)
Background: Multiple sclerosis (MS) is the most common disabling neurologic disease, that causes impairment in younger people. Both neuroinflammatory and neurodegenerative processes contribute to the pathogenesis of multiple sclerosis. Innovative imaging methods, such as ultra-high field magnetic resonance tomography (UHF-MRI), optic coherence tomography (OCT) and magnetic resonance spectroscopy (MRS) can be used for characterizing these neurodegenerative processes in detail and over time course.
Objective: To use the imaging methods mentioned above to further characterize cohorts of MS patients and to correlate the parameters with themselves as well as with clinical parameters and to evaluate their prognostic and diagnostic relevance.
Methods: Patients with multiple sclerosis were examined clinically, by OCT, visual acuity testing, examination of visually evoked potentials, ultra high field magnetic resonance tomography and magnetic resonance spectroscopy.
Results: The imaging methods used in these studies contributed to further characterize neuroinflammation und neurodegeneration in multiple sclerosis patients. A thinning of the retinal nerve fiber layer (RNFL) is correlated with brain parenchyma volume measured by MRI, and markers indicating ongoing neurodegenerative processes as detected by MRS. Using UHF-MRI, a correlation between optic radiation properties (such as inflammatory lesion load and its volume) and RNFL thickness, VEP latencies and visual impairment could be demonstrated.
Furthermore, UHF-MRI demonstrated neurodegenerative aspects such as parenchymal defects within inflammatory lesions, an optic radiation thinning and allowed a more precise detection of MS lesions than conventional MRI, in particular cortical grey matter lesions.
Summary: OCT, MRS and UHF-MRI are feasible methods to provide a more detailed description of neuroinflammation and neurodegeneration in MS patients, as demonstrated in these studies particularly for the visual pathway. They are non-invasive and can be utilized for clinical to study the disease course and also in differential diagnostic procedures
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Towards in vivo g-ratio mapping using MRI: unifying myelin and diffusion imaging
The g-ratio, quantifying the comparative thickness of the myelin sheath
encasing an axon, is a geometrical invariant that has high functional relevance
because of its importance in determining neuronal conduction velocity. Advances
in MRI data acquisition and signal modelling have put in vivo mapping of the
g-ratio, across the entire white matter, within our reach. This capacity would
greatly increase our knowledge of the nervous system: how it functions, and how
it is impacted by disease. This is the second review on the topic of g-ratio
mapping using MRI. As such, it summarizes the most recent developments in the
field, while also providing methodological background pertinent to aggregate
g-ratio weighted mapping, and discussing pitfalls associated with these
approaches. Using simulations based on recently published data, this review
demonstrates the relevance of the calibration step for three myelin-markers
(macromolecular tissue volume, myelin water fraction, and bound pool fraction).
It highlights the need to estimate both the slope and offset of the
relationship between these MRI-based markers and the true myelin volume
fraction if we are really to achieve the goal of precise, high sensitivity
g-ratio mapping in vivo. Other challenges discussed in this review further
evidence the need for gold standard measurements of human brain tissue from ex
vivo histology. We conclude that the quest to find the most appropriate MRI
biomarkers to enable in vivo g-ratio mapping is ongoing, with the potential of
many novel techniques yet to be investigated.Comment: Will be published as a review article in Journal of Neuroscience
Methods as parf of the Special Issue with Hu Cheng and Vince Calhoun as Guest
Editor
Serial Correlations in Single-Subject fMRI with Sub-Second TR
When performing statistical analysis of single-subject fMRI data, serial
correlations need to be taken into account to allow for valid inference.
Otherwise, the variability in the parameter estimates might be under-estimated
resulting in increased false-positive rates. Serial correlations in fMRI data
are commonly characterized in terms of a first-order autoregressive (AR)
process and then removed via pre-whitening. The required noise model for the
pre-whitening depends on a number of parameters, particularly the repetition
time (TR). Here we investigate how the sub-second temporal resolution provided
by simultaneous multislice (SMS) imaging changes the noise structure in fMRI
time series. We fit a higher-order AR model and then estimate the optimal AR
model order for a sequence with a TR of less than 600 ms providing whole brain
coverage. We show that physiological noise modelling successfully reduces the
required AR model order, but remaining serial correlations necessitate an
advanced noise model. We conclude that commonly used noise models, such as the
AR(1) model, are inadequate for modelling serial correlations in fMRI using
sub-second TRs. Rather, physiological noise modelling in combination with
advanced pre-whitening schemes enable valid inference in single-subject
analysis using fast fMRI sequences
Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data
Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample
Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI
Parallel MRI is a fast imaging technique that enables the acquisition of
highly resolved images in space or/and in time. The performance of parallel
imaging strongly depends on the reconstruction algorithm, which can proceed
either in the original k-space (GRAPPA, SMASH) or in the image domain
(SENSE-like methods). To improve the performance of the widely used SENSE
algorithm, 2D- or slice-specific regularization in the wavelet domain has been
deeply investigated. In this paper, we extend this approach using 3D-wavelet
representations in order to handle all slices together and address
reconstruction artifacts which propagate across adjacent slices. The gain
induced by such extension (3D-Unconstrained Wavelet Regularized -SENSE:
3D-UWR-SENSE) is validated on anatomical image reconstruction where no temporal
acquisition is considered. Another important extension accounts for temporal
correlations that exist between successive scans in functional MRI (fMRI). In
addition to the case of 2D+t acquisition schemes addressed by some other
methods like kt-FOCUSS, our approach allows us to deal with 3D+t acquisition
schemes which are widely used in neuroimaging. The resulting 3D-UWR-SENSE and
4D-UWR-SENSE reconstruction schemes are fully unsupervised in the sense that
all regularization parameters are estimated in the maximum likelihood sense on
a reference scan. The gain induced by such extensions is illustrated on both
anatomical and functional image reconstruction, and also measured in terms of
statistical sensitivity for the 4D-UWR-SENSE approach during a fast
event-related fMRI protocol. Our 4D-UWR-SENSE algorithm outperforms the SENSE
reconstruction at the subject and group levels (15 subjects) for different
contrasts of interest (eg, motor or computation tasks) and using different
parallel acceleration factors (R=2 and R=4) on 2x2x3mm3 EPI images.Comment: arXiv admin note: substantial text overlap with arXiv:1103.353
NODDI-SH: a computational efficient NODDI extension for fODF estimation in diffusion MRI
Diffusion Magnetic Resonance Imaging (DMRI) is the only non-invasive imaging
technique which is able to detect the principal directions of water diffusion
as well as neurites density in the human brain. Exploiting the ability of
Spherical Harmonics (SH) to model spherical functions, we propose a new
reconstruction model for DMRI data which is able to estimate both the fiber
Orientation Distribution Function (fODF) and the relative volume fractions of
the neurites in each voxel, which is robust to multiple fiber crossings. We
consider a Neurite Orientation Dispersion and Density Imaging (NODDI) inspired
single fiber diffusion signal to be derived from three compartments:
intracellular, extracellular, and cerebrospinal fluid. The model, called
NODDI-SH, is derived by convolving the single fiber response with the fODF in
each voxel. NODDI-SH embeds the calculation of the fODF and the neurite density
in a unified mathematical model providing efficient, robust and accurate
results. Results were validated on simulated data and tested on
\textit{in-vivo} data of human brain, and compared to and Constrained Spherical
Deconvolution (CSD) for benchmarking. Results revealed competitive performance
in all respects and inherent adaptivity to local microstructure, while sensibly
reducing the computational cost. We also investigated NODDI-SH performance when
only a limited number of samples are available for the fitting, demonstrating
that 60 samples are enough to obtain reliable results. The fast computational
time and the low number of signal samples required, make NODDI-SH feasible for
clinical application
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