Axon Diameter Distributions (ADDs) change during brain development and are altered in several brain pathologies. Mapping ADDs non-invasively using dMRI could provide a useful biomarker, but existing methods are either parametric, orientation dependent, surmmarize the whole ADD as a single measure or use non-standard protocols. We propose to estimate the ADD from an orientation-invariant PGSE protocol optimized for axon diameter sensitivity, using a discrete linear model with smoothness and sparsity regularization. To our knowledge, we are the first to report orientationally invarant ADD estimates from dMRI data.

Over the last decade microstructure imaging has become commonly endorsed to estimate quantitative features of neuronal tissue. However, those techniques estimate the microstructure only locally. Microstructure informed tractography was recently proposed to bolster microstructure estimates by accounting for the structure of the white matter bundles. The purpose of this study was to extend this novel technique for evaluating bundle-specific axon diameter distributions and investigate bundle-specific properties in the human brain. The experiment was performed on the MGH adult HCP dataset. The findings suggest potential application in the estimation of the axon diameter distribution along white matter bundles in whole-brain tractograms.

Volumetric analysis of bundles derived from tractography is a popular statistical measure used in neurological disorder studies. Recent research performed by Gauvin shows that different bundles saturate with different tractography parameters, however, to achieve that saturation millions of streamlines need to be computed. In this investigation, the aim was to use microstructure informed tractography, a novel technique that combine tractography and microstructure models, to study the saturation of the bundles. This study has found that generally microstructure informed tractography makes the volume estimation less sensitive to tracking parameters. The findings may have profound implications in volumetric analysis in group studies.

Fiber tractography based on non-invasive diffusion imaging is at the heart of connectivity studies of the human brain. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain dataset with ground truth white matter tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. While most state-of-the-art algorithms reconstructed 90% of ground truth bundles to at least some extent, on average they produced four times more invalid than valid bundles. About half of the invalid bundles occurred systematically in the majority of submissions. Our results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.

The main limitation of diffusion tractography for connectivity studies is that the reconstructed tractograms are not truly quantitative. Microstructure imaging allows the estimation of more quantitative features of the neuronal tissue, such as the axon diameter distribution, but this analysis can only be performed voxelwise. Here, we extend a framework that we recently proposed for evaluating the plausibility of tractograms (COMMIT) with the aim of assessing bundle-specific axon diameter distributions as well. This possibility may have implications in connectomics, as it opens new perspectives for investigating brain connectivity at different scales.

Keywords: Diffusion MRI ; White Matter ; Microstructure ; Convex Optimization Reference EPFL-TALK-229267 Record created on 2017-06-22, modified on 2017-06-22

Standard applicators for cervix cancer brachytherapy (BT) do not always enable a sufficient radiation dose coverage of the target structure (HR-CTV). The aim of this study was to develop methodology for building models of the BT target from a cohort of cervix cancer patients, which would enable BT applicator testing. In this paper we propose two model types, a spatial distribution model and a principal component model. Each of them can be built from data of several patients that includes medical images of arbitrary resolution and modality supplemented with delineations of HR-CTV structure, reconstructed applicator structure and eventual organs at risk (OAR) structures. The spatial distribution model is a static model providing probability distribution of the target in the applicator coordinate system, and as such provides information of the target region that applicators must be able to cover. The principal component model provides information of the target spatial variability described by only a few parameters. It can be used to predict specific extreme situations in the scope of sufficient applicator radiation dose coverage in the target structure as well as radiation dose avoidance in OARs. The results are generated 3D images that can be imported into existent BT planning systems for further BT applicator analysis and eventual improvements.

The purpose of this study was to develop a methodology for the construction of models of interest to improve the choice of areas to radiate in the use of brachytherapy (BT). This work aims to propose a principal component model which is constructed from the data of different patients including medical images of arbitrary resolution and modality supplemented with delineations of radiation target (HR-CTV) structure, reconstructed applicator structure and eventual organs at risk (OAR) structures. The principal component model provides information about the spatial variability described by only a few parameters. It can be used to predict specific extreme situations in the scope of sufficient applicator radiation dose coverage in the target structure as well as radiation dose avoidance in OAR structures.

The decision process of artificial intelligence is elusive. We proposed a new method that by combining an attention-based convolutional neural network (GAMER-MRI) with the modified Layer-wise Relevance Propagation could reveal relevant regions on quantitative imaging maps in differentiating multiple sclerosis patients with mild-moderate and severe disabilities. The assessment of the relevant regions included the impact of inverting values within the regions and the heatmap on the MNI152 template. Our results show good network performance and identify brain regions relevant to the corticospinal tract. The proposed method might be useful to further explore patterns of brain microstructural alterations associated with disability.

A scarce body of literature studies the Corpus Callosum (CC) normal-appearing white matter (NAWM) in Relapsing-Remitting Multiple Sclerosis (RRMS). This work aimed to characterize the degree of the alterations in different CC segments by assessing the extent of damage to myelin and axon, as measured with myelin volume fraction, axonal volume fraction, and g-ratio. These microstructural measures correlated with disease duration, EDSS, number, and volume of the lesions in other white matter regions. In sum, this work shows that CC pathology in RRMS patients is related to focal damage and/or diffuse neurodegeneration, which is clinically relevant.

The visualization and characterization of cortical MS lesions is challenging on conventional MRI. In the present study, we used a susceptibility source separation algorithm to divide the positive and negative susceptibility of quantitative susceptibility mapping (QSM) MRI, so that we could disentangle signal alterations due to myelin loss or iron accumulation. In 19 MS patient with 123 cortical lesions, we found that the major part of QSM susceptibility of cortical lesions is driven by myelin and iron loss, while only a small proportion of lesions (12/123, 9.8%) showed an increment of susceptibility that is caused by iron accumulation.

The utility of advanced quantitative MRI for assessment of spinal cord tissue damage in multiple sclerosis has not yet been established. In this work, we used T1-mapping as well as quantitative magnetization transfer saturation and echo-planar imaging to quantify the extent of pathologic changes in the cervical cord of multiple sclerosis patients. Our results point to extensive demyelination and axonal loss both in the normal-appearing and lesional cervical cord, as well as to and chronic inflammation of cSCWM lesions in secondary progressive multiple sclerosis. Hence, quantitative spinal cord MRI may provide valuable information about the pathologic substrate of this disease.

A new approach for estimating inner axon radii from intra-axonal T2 relaxation times was recently proposed. However, further validations are required before this technique can be used widely. The main aim of this study is to validate this T2-based pore size estimation technique in phantoms comprising co-electrospun hollow axon-mimicking fibres designed to have non-circular cross-sections and different radii distributions. For this purpose, a diffusion-relaxation MRI dataset was acquired with a 7T preclinical scanner, from which the intra-fibre T2 times and pore sizes were estimated. The resulting pore sizes were compared to those measured from Scanning Electron Microscope images.

In-vivo quantification of axon diameter is an attractive and debated topic in the MRI community. The possibility to resolve submicrometric axon diameters non-invasively yields the potential to push further the boundaries in research and clinics but yet, further work is needed to better explore and validate the existing approaches to estimate the inner axon diameter. Recently, the feasibility of estimating the axon diameter from the intra-axonal transverse relaxation time has been investigated combining a diffusion-relaxation protocol and histological data. In the present study, we apply this approach in a larger in vivo population to assess variability across participants.