Combining anatomical and spectral information to enhance MRSI resolution and quantification: Application to Multiple Sclerosis

Multiple sclerosis is a progressive autoimmune disease that a˙ects young adults. Magnetic resonance (MR) imaging has become an integral part in monitoring multiple sclerosis disease. Conventional MR imaging sequences such as fluid attenuated inversion recovery imaging have high spatial resolution, and can visualise the presence of focal white matter brain lesions in multiple sclerosis disease. Manual delineation of these lesions on conventional MR images is time consuming and su˙ers from intra and inter-rater variability. Among the advanced MR imaging techniques, MR spectroscopic imaging can o˙er complementary information on lesion characterisation compared to conventional MR images. However, MR spectroscopic images have low spatial resolution. Therefore, the aim of this thesis is to automatically segment multiple sclerosis lesions on conventional MR images and use the information from high-resolution conventional MR images to enhance the resolution of MR spectroscopic images. Automatic single time ...

Jain, Saurabh — KU Leuven


Unsupervised Models for White Matter Fiber-Bundles Analysis in Multiple Sclerosis

Diffusion Magnetic Resonance Imaging (dMRI) is a meaningful technique for white matter (WM) fiber-tracking and microstructural characterization of axonal/neuronal integrity and connectivity. By measuring water molecules motion in the three directions of space, numerous parametric maps can be reconstructed. Among these, fractional anisotropy (FA), mean diffusivity (MD), and axial (λa) and radial (λr) diffusivities have extensively been used to investigate brain diseases. Overall, these findings demonstrated that WM and grey matter (GM) tissues are subjected to numerous microstructural alterations in multiple sclerosis (MS). However, it remains unclear whether these tissue alterations result from global processes, such as inflammatory cascades and/or neurodegenerative mechanisms, or local inflammatory and/or demyelinating lesions. Furthermore, these pathological events may occur along afferent or afferent WM fiber pathways, leading to antero- or retrograde degeneration. Thus, for a better understanding of MS pathological processes like its spatial and ...

Stamile, Claudio — Université Claude Bernard Lyon 1, KU Leuven


Classification of brain tumors based on magnetic resonance spectroscopy

Nowadays, diagnosis and treatment of brain tumors is based on clinical symptoms, radiological appearance, and often histopathology. Magnetic resonance imaging (MRI) is a major noninvasive tool for the anatomical assessment of tumors in the brain. However, several diagnostic questions, such as the type and grade of the tumor, are difficult to address using MRI. The histopathology of a tissue specimen remains the gold standard, despite the associated risks of surgery to obtain a biopsy. In recent years, the use of magnetic resonance spectroscopy (MRS), which provides a metabolic profile, has gained a lot of interest for a more detailed and specific noninvasive evaluation of brain tumors. In particular, magnetic resonance spectroscopic imaging (MRSI) is attractive as this may also enable to visualize the heterogeneous spatial extent of tumors, both inside and outside the MRI detectable lesion. As manual, individual, viewing ...

Luts, Jan — Katholieke Universiteit Leuven


Three dimensional shape modeling: segmentation, reconstruction and registration

Accounting for uncertainty in three-dimensional (3D) shapes is important in a large number of scientific and engineering areas, such as biometrics, biomedical imaging, and data mining. It is well known that 3D polar shaped objects can be represented by Fourier descriptors such as spherical harmonics and double Fourier series. However, the statistics of these spectral shape models have not been widely explored. This thesis studies several areas involved in 3D shape modeling, including random field models for statistical shape modeling, optimal shape filtering, parametric active contours for object segmentation and surface reconstruction. It also investigates multi-modal image registration with respect to tumor activity quantification. Spherical harmonic expansions over the unit sphere not only provide a low dimensional polarimetric parameterization of stochastic shape, but also correspond to the Karhunen-Lo´eve (K-L) expansion of any isotropic random field on the unit sphere. Spherical ...

Li, Jia — University of Michigan


Signal processing and classification for magnetic resonance spectroscopic data with clinical applications

Over the last decades, Magnetic Resonance Imaging (MRI) has taken a leading role in the study of human body and it is widely used in clinical diagnosis. In vivo and ex vivo Magnetic Resonance Spectroscopic (MRS) techniques can additionally provide valuable metabolic information as compared to MRI and are gaining more clinical interest. The analysis of MRS data is a complex procedure and requires several preprocessing steps aiming to improve the quality of the data and to extract the most relevant features before any classification algorithm can be successfully applied. In this thesis a new approach to quantify magnetic resonance spectroscopic imaging (MRSI) data and therefore to obtain improved metabolite estimates is proposed. Then an important part is focusing on improving the diagnosis of glial brain tumors which are characterized by an extensive heterogeneity since various intramural histopathological properties such ...

Croitor Sava, Anca Ramona — KU Leuven


Learning from structured EEG and fMRI data supporting the diagnosis of epilepsy

Epilepsy is a neurological condition that manifests in epileptic seizures as a result of an abnormal, synchronous activity of a large group of neurons. Depending on the affected brain regions, seizures produce various severe clinical symptoms. Epilepsy cannot be cured and in many cases is not controlled by medication either. Surgical resection of the region responsible for generating the epileptic seizures might offer remedy for these patients. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) measure the changes of brain activity in time over different locations of the brain. As such, they provide valuable information on the nature, the timing and the spatial origin of the epileptic activity. Unfortunately, both techniques record activity of different brain and artefact sources as well. Hence, EEG and fMRI signals are characterised by low signal to noise ratio. Data quality and the vast amount ...

Hunyadi, Borbála — KU Leuven


Least squares support vector machines classification applied to brain tumour recognition using magnetic resonance spectroscopy

Magnetic Resonance Spectroscopy (MRS) is a technique which has evolved rapidly over the past 15 years. It has been used specifically in the context of brain tumours and has shown very encouraging correlations between brain tumour type and spectral pattern. In vivo MRS enables the quantification of metabolite concentrations non-invasively, thereby avoiding serious risks to brain damage. While Magnetic Resonance Imaging (MRI) is commonly used for identifying the location and size of brain tumours, MRS complements it with the potential to provide detailed chemical information about metabolites present in the brain tissue and enable an early detection of abnormality. However, the introduction of MRS in clinical medicine has been difficult due to problems associated with the acquisition of in vivo MRS signals from living tissues at low magnetic fields acceptable for patients. The low signal-to-noise ratio makes accurate analysis of ...

Lukas, Lukas — Katholieke Universiteit Leuven


Subspace-based quantification of magnetic resonance spectroscopy data using biochemical prior knowledge

Nowadays, Nuclear Magnetic Resonance (NMR) is widely used in oncology as a non-invasive diagnostic tool in order to detect the presence of tumor regions in the human body. An application of NMR is Magnetic Resonance Imaging, which is applied in routine clinical practice to localize tumors and determine their size. Magnetic Resonance Imaging is able to provide an initial diagnosis, but its ability to delineate anatomical and pathological information is significantly improved by its combination with another NMR application, namely Magnetic Resonance Spectroscopy. The latter reveals information on the biochemical profile tissues, thereby allowing clinicians and radiologists to identify in a non{invasive way the different tissue types characterizing the sample under investigation, and to study the biochemical changes underlying a pathological situation. In particular, an NMR application exists which provides spatial as well as biochemical information. This application is called ...

Laudadio, Teresa — Katholieke Universiteit Leuven


Analysis and improvement of quantification algorithms for magnetic resonance spectroscopy

Magnetic Resonance Spectroscopy (MRS) is a technique used in fundamental research and in clinical environments. During recent years, clinical application of MRS gained importance, especially as a non-invasive tool for diagnosis and therapy monitoring of brain and prostate tumours. The most important asset of MRS is its ability to determine the concentration of chemical substances non-invasively. To extract relevant signal parameters, MRS data have to be quantified. This usually doesn¢t prove to be straightforward since in vivo MRS signals are characterized by poor signal-to-noise ratios, overlapping peaks, acquisition related artefacts and the presence of disturbing components (e.g. residual water in proton spectra). The work presented in this thesis aims to improve the quantification in different applications of MRS in vivo. To obtain the signal parameters related to MRS data, different approaches were suggested in the past. Black-box methods, don¢t require ...

Pels, Pieter — Katholieke Universiteit Leuven


Automated detection of epileptic seizures in pediatric patients based on accelerometry and surface electromyography

Epilepsy is one of the most common neurological diseases that manifests in repetitive epileptic seizures as a result of an abnormal, synchronous activity of a large group of neurons. Depending on the affected brain regions, seizures produce various severe clinical symptoms. There is no cure for epilepsy and sometimes even medication and other therapies, like surgery, vagus nerve stimulation or ketogenic diet, do not control the number of seizures. In that case, long-term (home) monitoring and automatic seizure detection would enable the tracking of the evolution of the disease and improve objective insight in any responses to medical interventions or changes in medical treatment. Especially during the night, supervision is reduced; hence a large number of seizures is missed. In addition, an alarm should be integrated into the automated seizure detection algorithm for severe seizures in order to help the ...

Milošević, Milica — KU Leuven


Optimal estimation of diffusion MRI parameters

Diffusion magnetic resonance imaging (dMRI) is currently the method of choice for the in vivo and non-invasive quantification of water diffusion in biological tissue. Several diffusion models have been proposed to obtain quantitative diffusion parameters, which have shown to provide novel information on the structural and organizational features of biological tissue, the brain white matter in particular. The goal of this dissertation is to improve the accuracy of the diffusion parameter estimation, given the non-Gaussian nature of the diffusion-weighted MR data. In part I of this manuscript, the necessary basics of dMRI are provided. Next, Part II deals with diffusion parameter estimation and includes the main contributions of the research. Finally, Part III covers the construction of a population-based dMRI atlas of the rat brain.

Veraart, Jelle — University of Antwerp


Constrained Non-negative Matrix Factorization for Vocabulary Acquisition from Continuous Speech

One desideratum in designing cognitive robots is autonomous learning of communication skills, just like humans. The primary step towards this goal is vocabulary acquisition. Being different from the training procedures of the state-of-the-art automatic speech recognition (ASR) systems, vocabulary acquisition cannot rely on prior knowledge of language in the same way. Like what infants do, the acquisition process should be data-driven with multi-level abstraction and coupled with multi-modal inputs. To avoid lengthy training efforts in a word-by-word interactive learning process, a clever learning agent should be able to acquire vocabularies from continuous speech automatically. The work presented in this thesis is entitled \emph{Constrained Non-negative Matrix Factorization for Vocabulary Acquisition from Continuous Speech}. Enlightened by the extensively studied techniques in ASR, we design computational models to discover and represent vocabularies from continuous speech with little prior knowledge of the language to ...

Sun, Meng — Katholieke Universiteit Leuven


Numerical Approaches for Solving the Combined Reconstruction and Registration of Digital Breast Tomosynthesis

Heavy demands on the development of medical imaging modalities for breast cancer detection have been witnessed in the last three decades in an attempt to reduce the mortality associated with the disease. Recently, Digital Breast Tomosynthesis (DBT) shows its promising in the early diagnosis when lesions are small. In particular, it offers potential benefits over X-ray mammography - the current modality of choice for breast screening - of increased sensitivity and specificity for comparable X-ray dose, speed, and cost. An important feature of DBT is that it provides a pseudo-3D image of the breast. This is of particular relevance for heterogeneous dense breasts of young women, which can inhibit detection of cancer using conventional mammography. In the same way that it is difficult to see a bird from the edge of the forest, detecting cancer in a conventional 2D mammogram ...

Yang, Guang — University College London


Contributions to Signal Processing for MRI

Magnetic Resonance Imaging (MRI) is an important diagnostic tool for imaging soft tissue without the use of ionizing radiation. Moreover, through advanced signal processing, MRI can provide more than just anatomical information, such as estimates of tissue-specific physical properties. Signal processing lies at the very core of the MRI process, which involves input design, information encoding, image reconstruction, and advanced filtering. Based on signal modeling and estimation, it is possible to further improve the images, reduce artifacts, mitigate noise, and obtain quantitative tissue information. In quantitative MRI, different physical quantities are estimated from a set of collected images. The optimization problems solved are typically nonlinear, and require intelligent and application-specific algorithms to avoid suboptimal local minima. This thesis presents several methods for efficiently solving different parameter estimation problems in MRI, such as multi-component T2 relaxometry, temporal phase correction of complex-valued ...

Björk, Marcus — Uppsala University


Quantification and classification of Magnetic Resonance Spectroscopy data and applications to brain tumour recognition

The medical diagnosis of brain tumours is one of the main applications of Magnetic Resonance. Magnetic Resonance consists of two main branches: Imaging and Spectroscopy. Magnetic Resonance Imaging is very well-known as the radiologic technique applied to produce high-quality images of tissues, such as the brain tissue, for diagnostic purposes. Magnetic Resonance Spectroscopy provides chemical information about all the molecules present in the brain, such as their concentrations. Both Imaging and Spectroscopy can be exploited for the grading and typing of brain tumours, also called the classification of brain tumours. As first topic, this thesis mainly studied the contribution of Spectroscopy for automated classification and the influence of several factors on the classification performance. It was found that a few preprocessing steps did not have a large impact on the classification results. This implies that several preprocessing steps can be ...

Devos, Andy — Katholieke Universiteit Leuven

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