Fast Fiber Orientation Estimation in Diffusion MRI from kq-Space Sampling and Anatomical Priors

High spatio-angular resolution diffusion MRI (dMRI) has been shown to provide accurate identification of complex fiber configurations, albeit at the cost of long acquisition times. We propose a method to recover intra-voxel fiber configurations at high spatio-angular resolution relying on a kq-space under-sampling scheme to enable accelerated acquisitions. The inverse problem for reconstruction of the fiber orientation distribution (FOD) is regularized by a structured sparsity prior promoting simultaneously voxelwise sparsity and spatial smoothness of fiber orientation. Prior knowledge of the spatial distribution of white matter, gray matter and cerebrospinal fluid is also assumed. A minimization problem is formulated and solved via a forward-backward convex optimization algorithmic structure. Simulations and real data analysis suggest that accurate FOD mapping can be achieved from severe kq-space under-sampling regimes, potentially enabling high spatio-angular dMRI in the clinical setting.Comment: 10 pages, 5 figures, Supplementary Material

Diffantom: Whole-Brain Diffusion MRI Phantoms Derived from Real Datasets of the Human Connectome Project

Diffantom is a whole-brain diffusion MRI (dMRI) phantom publicly available through the Dryad Digital Repository (doi:10.5061/dryad.4p080). The dataset contains two single-shell dMRI images, along with the corresponding gradient information, packed following the BIDS standard (Brain Imaging Data Structure, Gorgolewski et al., 2015). The released dataset is designed for the evaluation of the impact of susceptibility distortions and benchmarking existing correction methods. In this Data Report we also release the software instruments involved in generating diffantoms, so that researchers are able to generate new phantoms derived from different subjects, and apply these data in other applications like investigating diffusion sampling schemes, the assessment of dMRI processing methods, the simulation of pathologies and imaging artifacts, etc. In summary, Diffantom is intended for unit testing of novel methods, cross-comparison of established methods, and integration testing of partial or complete processing flows to extract connectivity networks from dMRI

Advanced image-processing techniques in magnetic resonance imaging for the investigation of brain pathologies and tumour angiogenesis

L'imaging a risonanza magnetica (MRI) \ue8 sempre pi\uf9 utilizzato in ambiente medico per la sua abilit\ue0 di produrre in modo non invasivo immagini di alt\ue0 qualit\ue0 dell'interno del corpo umano. Sin dalla sua introduzione nei primi anni 70, techiche di acquisizione via via pi\uf9 complesse sono state proposte, portando l'MRI ad essere utilizzata su uno spettro di applicazioni sempre pi\uf9 ampio. Le tecniche pi\uf9 innovative, tra cui la risonanza magnetica funzionale e di diffusione, richiedono tecniche di analisi ed algoritmi di elaborazione molto complessi per estrarre informazioni utili dai dati acquisiti. Lo scopo di questa tesi \ue8 stato quello di sviluppare e ottimizzare tecniche avanzate di elaborazione per applicarle all'analisi di dati di risonanza magnetica sia in ambiente preclinico che clinico. Durante il corso di dottorato sono stato coinvolto attivamente in diversi progetti di ricerca, ed ogni volta mi sono trovato ad affrontare problematiche diverse. In questa tesi, tuttavia, saranno riportati i risultati ottenuti nei tre progetti pi\uf9 interessanti a cui ho preso parte. Tali progetti avevano come obiettivo (i) l'implementazione di un protocollo sperimentale innovativo per imaging funzionale in animali da laboratorio, (ii) lo sviluppo di nuovi metodi per l'analisi di dati di Dynamic Contrast Enhanced MRI in modelli sperimentali di tumore e (iii) l'analisi di dati di diffusione in pazienti affetti da ischemia cerebrale. Particolare enfasi sar\ue0 posta sugli aspetti tecnici che riguardano gli algoritmi ed i metodi di elaborazione utilizzati nel processo di analisi.Magnetic resonance imaging (MRI) is increasingly being used in medical settings because of its ability to produce, non-invasively, high quality images of the inside of the human body. Since its introduction in early 70\u2019s, more and more complex acquisition techniques have been proposed, raising MRI to be exploited in a wide spectrum of applications. Innovative MRI modalities, such as diffusion and functional imaging, require complex analysis techniques and advanced algorithms in order to extract useful information from the acquired data. The aim of the present work has been to develop and optimize state-of-the-art techniques to be applied in the analysis of MRI data both in experimental and clinical settings. During my doctoral program I have been actively involved in several research projects, each time facing many different issues. In this dissertation, however, I will report the results obtained in three most appealing projects I partecipated to. These projects were devoted (i) to the implementation of an innovative experimental protocol for functional MRI in laboratory animals, (ii) to the development of new methods for the analysis of Dynamic Contrast Enhanced MRI data in experimental tumour models and (iii) to the analysis of diffusion MRI data in stroke patients. Particular emphasis will be given to the technical aspects regarding the algorithms and processing methods used in the analysis of data

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

Bundle-o-graphy: improving structural connectivity estimation with adaptive microstructure-informed tractography

Tractography is a powerful tool for the investigation of the complex organization of the brain in vivo, as it allows inferring the macroscopic pathways of the major fiber bundles of the white matter based on non-invasive diffusion-weighted magnetic resonance imaging acquisitions. Despite this unique and compelling ability, some studies have exposed the poor anatomical accuracy of the reconstructions obtained with this technique and challenged its effectiveness for studying brain connectivity. In this work, we describe a novel method to readdress tractography reconstruction problem in a global manner by combining the strengths of so-called generative and discriminative strategies. Starting from an input tractogram, we parameterize the connections between brain regions following a bundle-based representation that allows to drastically reducing the number of parameters needed to model groups of fascicles. The parameters space is explored following an MCMC generative approach, while a discrimininative method is exploited to globally evaluate the set of connections which is updated according to Bayes' rule. Our results on both synthetic and real brain data show that the proposed solution, called bundle-o-graphy, allows improving the anatomical accuracy of the reconstructions while keeping the computational complexity similar to other state-of-the-art methods

Incorporating outlier information into diffusion-weighted MRI modeling for robust microstructural imaging and structural brain connectivity analyses

A B S T R A C T The white matter structures of the human brain can be represented using diffusion-weighted MRI tractography. Unfortunately, tractography is prone to find false-positive streamlines causing a severe decline in its specificity and limiting its feasibility in accurate structural brain connectivity analyses. Filtering algorithms have been pro-posed to reduce the number of invalid streamlines but the currently available filtering algorithms are not suitable to process data that contains motion artefacts which are typical in clinical research. We augmented the Con-vex Optimization Modelling for Microstructure Informed Tractography (COMMIT) algorithm to adjust for these signals drop-out motion artefacts. We demonstrate with comprehensive Monte-Carlo whole brain simulations and in vivo infant data that our robust algorithm is capable of properly filtering tractography reconstructions despite these artefacts. We evaluated the results using parametric and non-parametric statistics and our results demonstrate that if not accounted for, motion artefacts can have severe adverse effects in human brain structural connectivity analyses as well as in microstructural property mappings. In conclusion, the usage of robust filtering methods to mitigate motion related errors in tractogram filtering is highly beneficial, especially in clinical stud-ies with uncooperative patient groups such as infants. With our presented robust augmentation and open-source implementation, robust tractogram filtering is readily available.Peer reviewe