13,415 research outputs found

    Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and spatial regularization

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    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

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    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

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    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

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    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

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    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

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    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

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    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

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    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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