Myelin insulates neuronal axons and enables fast signal transmission, constituting a key component of brain development, aging and disease. Yet, myelin-specific imaging of macroscopic samples remains a challenge. Here, we exploit myelin’s nanostructural periodicity, and use small-angle X-ray scattering tensor tomography (SAXS-TT) to simultaneously quantify myelin levels, nanostructural integrity and axon orientations in nervous tissue. Proof-of-principle is demonstrated in whole mouse brain, mouse spinal cord and human white and gray matter samples. Outcomes are validated by 2D/3D histology and compared to MRI measurements sensitive to myelin and axon orientations. Specificity to nanostructure is exemplified by concomitantly imaging different myelin types with distinct periodicities. Finally, we illustrate the method’s sensitivity towards myelin-related diseases by quantifying myelin alterations in dysmyelinated mouse brain. This non-destructive, stain-free molecular imaging approach enables quantitative studies of myelination within and across samples during development, aging, disease and treatment, and is applicable to other ordered biomolecules or nanostructures.

Altered cerebral connectivity is one of the core pathophysiological mechanism underlying the development and progression of information-processing deficits in schizophrenia. To date, most diffusion tensor imaging (DTI) studies used fractional anisotropy (FA) to investigate disrupted white matter connections. However, a quantitative interpretation of FA changes is often impeded by the inherent limitations of the underlying tensor model. A more fine-grained measure of white matter alterations could be achieved by measuring fiber density (FD) - a novel non-tensor-derived diffusion marker. This study investigates, for the first time, FD alterations in schizophrenia patients. FD and FA maps were derived from diffusion data of 25 healthy controls (HC) and 21 patients with schizophrenia (SZ). Using tract-based spatial statistics (TBSS), group differences in FD and FA were investigated across the entire white matter. Furthermore, we performed a region of interest (ROI) analysis of frontal fasciculi to detect potential correlations between FD and positive symptoms. As a result, whole brain TBSS analysis revealed reduced FD in SZ patients compared to HC in several white matter tracts including the left and right thalamic radiation (TR), superior longitudinal fasciculus (SLF), corpus callosum (CC), and corticospinal tract (CST). In contrast, there were no significant FA differences between groups. Further, FD values in the TR were negatively correlated with the severity of positive symptoms and medication dose in SZ patients. In summary, a novel diffusion-weighted data analysis approach enabled us to identify widespread FD changes in SZ patients with most prominent white matter alterations in the frontal and subcortical regions. Our findings suggest that the new FD measure may be more sensitive to subtle changes in the white matter microstructure compared to FA, particularly in the given population. Therefore, investigating FD may be a promising approach to detect subtle changes in the white matter microstructure of altered connectivity in schizophrenia.

Dafne is a free and open-source software suite that implements the principles of continuous and decentralized deep learning to assist users in medical image segmentation, serving as a potential alternative to traditional centralized learning approaches.

Qualitative and quantitative MRI of the rotator cuff muscles enables detailed analysis of fatty muscle degeneration and muscle volume atrophy, which are predictors of functional and surgical outcomes.

Background and Purpose: Diffusion MRI tractography enables to investigate white matter pathways noninvasively by reconstructing estimated fiber pathways. However, such tractograms remain biased and nonquantitative. Several techniques have been proposed to reestablish the link between tractography and tissue microstructure by modeling the diffusion signal or fiber orientation distribution (FOD) with the given tractogram and optimizing each fiber or compartment contribution according to the diffusion signal or FOD. Nevertheless, deriving a reliable quantification of connectivity strength between different brain areas is still a challenge. Moreover, evaluating the quality of a tractogram and measuring the possible error sources contained in a specific reconstructed fiber bundle also remains difficult. Lastly, all of these optimization techniques fail if specific fiber populations within a tractogram are underrepresented, for example, due to algorithmic constraints, anatomical properties, fiber geometry or seeding patterns.Methods: In this work, we propose an approach which enables the inspection of the quality of a tractogram optimization by evaluating the residual error signal and its FOD representation. The automated fiber quantification (AFQ) is applied, whereby the framework is extended to reflect not only scalar diffusion metrics along a fiber bundle, but also directionally dependent FOD amplitudes along and perpendicular to the fiber direction. Furthermore, we also present an up‐sampling procedure to increase the number of streamlines of a given fiber population. The introduced error metrics and fiber up‐sampling method are tested and evaluated on single‐shell diffusion data sets of 16 healthy volunteers.Results and Conclusion: Analyzing the introduced error measures on specific fiber bundles shows a considerable improvement in applying the up‐sampling method. Additionally, the error metrics provide a useful tool to spot and identify potential error sources in tractograms.

Objectives: The aim of this study was to quantify and compare fat fraction (FF) and muscle volume between patients with failed and intact rotator cuff (RC) repair as well as a control group with nonsurgical conservative treatment to define FF cutoff values for predicting the outcome of RC repair. Materials and Methods: Patients with full-thickness RC tears who received magnetic resonance imaging (MRI) before and after RC repair including a 2-point Dixon sequence were retrospectively screened. Patients with retear of 1 or more tendons diagnosed on MRI (Sugaya IV–V) were enrolled and matched to patients with intact RC repair (Sugaya I–II) and to a third group with conservatively treated RC tears. Two radiologists evaluated morphological features (Cofield, Patte, and Goutallier), as well as the integrity of the RC after repair (Sugaya). Fat fractions were calculated from the 2-point Dixon sequence, and the RC muscles were segmented semiautomatically to calculate FFs and volume for each muscle. Receiver operator characteristics curves were used to determine FF cutoff values that best predict RC retears. Results: In total, 136 patients were enrolled, consisting of 3 groups: 41 patients had a failed RC repair (58 ± 7 years, 16 women), 50 patients matched into the intact RC repair group, and 45 patients were matched into the conservative treatment group. Receiver operator characteristics curves showed reliable preoperative FF cutoff values for predicting retears at 6.0% for the supraspinatus muscle (0.83 area under the curve [AUC]), 7.4% for the infraspinatus muscle (AUC 0.82), and 8.3% for the subscapularis muscle (0.94 AUC). Conclusions: Preoperative quantitative FF calculated from 2-point Dixon MRI can be used to predict the risk of retear after arthroscopic RC repair with cutoff values between 6% and 8.3%.