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Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organization. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple “crossing” fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the “Fixel-Based Analysis” (FBA) framework, which implements bespoke solutions to this end. It has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to Fixel-Based Analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of all current FBA studies, categorized across a broad range of neuro-scientific domains, listing key design choices and summarizing their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the FBA framework, and outline some directions and future opportunities.

The domain of diffusion weighted imaging (DWI) has come a long way since its initial development in the mid-1980s. Over the years, we have gained a better understanding of the accompanying techniques and necessary processing steps involved, furthermore leading to a wealth of new insights in the complex workings of the (human) brain. The introduction of diffusion tensor imaging (DTI) has played a crucial role in thisprocess, as it provided the first model intended to deal with anisotropic diffusion; a particular feature observed in the white matter (WM), asopposed to the other most common 'tissue' types found in the human brain, i.e. the gray matter (GM) and cerebrospinal fluid (CSF). During the last decade, however, we have come to realize that the DTI model is severely lacking in its possibilities to represent voxels that contain so called 'crossing fibers', a general name that is often used to refer to a range of complex geometric fiber configurations caused by the partial volume effect. In this PhD thesis, we intended to design certain representations of (information extracted from) DWI data that take into account the aforementioned variety of complex geometrical configurations. Our proposed novel representations aim to offer a greater flexibility that should inherently render many existing difficult problems (e.g. segmentation and registration) trivial; yet make as little assumptions aspossible on the nature of the data or the properties of the underlying structures. A first major contribution is a generic framework for multi-shell multi-tissue (MSMT) representations, and a specific implementation tailored to represent WM, GM and CSF in the human brain. This representation was specifically designed to render the retransformation problem trivial. The latter was easily solved by a newly introduced preservation of principal volume fractions (PPVF) retransformation strategy. A second major contribution is a more tangible track orientation distribution (TOD) representation for complex fiber track distributions. Our newly developed method to obtain such a TOD, is termed track orientation density imaging (TODI). This technique allowed us to gain further understanding in the amplitude of a short-tracks TOD, which can now be interpreted as a measure of track-like local support (TLS). Furthermore, we showed that employing the latter for TOD-based tractography results in guiding the tracks along directions that are morelikely to correspond to continuous structure over a longer distance; i.e. track-like structure! Both representations (MSMT and the TOD) forthcoming from these major contributions, also allow for increased insightin many other aspects of the data they describe, and provide a large range of opportunities for future research. Dhollander T., ''Accounting for complex structure in diffusion weighted imaging data using volume fraction representations'', Proefschrift voorgedragen tot het behalen van het doctoraat in de ingenieurswetenschappen, KU Leuven, April 2014, Leuven, Belgium. nrpages: 212 status: published

Diffusion weighted imaging (DWI) allows to delineate neural fibres, based on local, directional information of the diffusion of water. Due to its directional nature, the local information needs to be reoriented upon image transformation, in order to preserve correspondence to the anatomy. In this work, we show that reorientation of the fODF with preservation of volume fractions (PVF) affects both deterministic and probabilistic fibre tracking. We identify the main causes for this, and validate them on synthetic and real brain DWI data. The problem is not with the PVF reorientation itself, but rather with the fODF reconstruction, its use in fibre tracking, and the influence of the seeds. ispartof: pages:33-44 ispartof: Proceedings CDMRI 2012 pages:33-44 ispartof: MICCAI 2012 Workshop on Computational Diffusion MRI - CDMRI 2012 location:Nice, France date:5 Oct - 5 Oct 2012 status: published

A crucial operation in every image registration algorithm is the application of a spatial transformation to an image. For scalar valued images, this particular operation is rather trivial. For diffusion weighted imaging (DWI) data however, the problem is more complex due to the information in every voxel being dependent on the angular structure of the underlying tissue. Methods for transforming the diffusion tensor and the fiber orientation distribution function from high angular resolution diffusion imaging have already been proposed. In order to perform registration of DWI data irrespectively of (i.e. before the application of) any particular reconstruction method, it should be done straight on the signal functions in q-space. In this work, we specifically consider the problem of transforming the signal functions in q-space. We develop a plausible method to accomplish this. The proposed method preserves anisotropic as well as isotropic volume fractions. Dhollander, T., Van Hecke, W., Maes, F., Sunaert, S., Suetens, P.: Spatial Transformations of High Angular Resolution Diffusion Imaging Data in Q-space. In: MICCAI 13, CDMRI Workshop, pp. 73–83 (2010) ispartof: pages:73-83 ispartof: Online Proceedings at http://cmic.cs.ucl.ac.uk/cdmri10/ pages:73-83 ispartof: MICCAI 2010 workshop on computational diffusion MRI - CDMRI 2010 location:Beijing, China date:24 Sep - 24 Sep 2010 status: published

Agingisassociatedwithchangesinthecentralnervoussystemandleadsto reducedlifequality.Here,weinvestigatedtheage-relateddifferencesinthe CNSunderlyingmotorperformancedeficitsusingmagneticresonancespectroscopyanddiffusionMRI.MRSmeasuredN-acetylaspartate(NAA),choline(Cho), andcreatine(Cr)concentrationsinthesensorimotorandoccipitalcortex, whereasdMRIquantifiedapparentfiberdensity(FD)inthesamevoxelstoevaluatewhitemattermicrostructuralorganization.WefoundthatagingwasassociatedwithincreasedreactiontimeandreducedFDandNAAconcentrationinthe sensorimotorvoxel.BothFDandNAAmediatedtheassociationbetweenage andreactiontime.TheNAAconcentrationwasfoundtomediatetheassociation betweenageandFDinthesensorimotorvoxel.Weproposethattheage-related decreaseinNAAconcentrationmayresultinreducedaxonalfiberdensityinthe sensorimotorcortexwhichmayultimatelyaccountfortheresponseslownessof olderparticipants. Wethankallparticipantsfortakingpartinthisstudy,PaulMeugensfortaskdeviceconstruction,andRene´ Clerckxforprogrammingthebehavioraltask.ThisworkwassupportedbytheResearchFoundationFlanders(G089818N,K174216N),theExcellenceSciencegrant(EOS,30446199,MEMODYN),andtheKU LeuvenResearchFund(C16/15/070).