Characterization of healthy versus pathological tissue in the peritumoral area is confounded by the presence of edema, making free water estimation the key concern in modeling tissue microstructure. Most methods that model tissue microstructure are either based on advanced acquisition schemes not readily available in the clinic or are not designed to address the challenge of edema. This underscores the need for a robust free water elimination (FWE) method that estimates free water in pathological tissue but can be used with clinically prevalent single-shell diffusion tensor imaging data. FWE in single-shell data requires the fitting of a bi-compartment model, which is an ill-posed problem. Its solution requires optimization, which relies on an initialization step. We propose a novel initialization approach for FWE, FERNET, which improves the estimation of free water in edematous and infiltrated peritumoral regions, using single-shell diffusion MRI data. The method has been extensively investigated on simulated data and healthy dataset. Additionally, it has been applied to clinically acquired data from brain tumor patients to characterize the peritumoral region and improve tractography in it.

government personnel experienced potential exposures to uncharacterized directional phenomena while serving in Havana, Cuba, from late 2016 through May 2018. The underlying neuroanatomical findings have not been described.OBJECTIVE To examine potential differences in brain tissue volume, microstructure, and functional connectivity in government personnel compared with individuals not exposed to directional phenomena. DESIGN, SETTING, AND PARTICIPANTS Forty government personnel (patients) who were potentially exposed and experienced neurological symptoms underwent evaluation at a US academic medical center from August 21, 2017, to June 8, 2018, including advanced structural and functional magnetic resonance imaging analytics. Findings were compared with imaging findings of 48 demographically similar healthy controls.EXPOSURES Potential exposure to uncharacterized directional phenomena of unknown etiology, manifesting as pressure, vibration, or sound. MAIN OUTCOMES AND MEASURESPotential imaging-based differences between patients and controls with regard to (1) white matter and gray matter total and regional brain volumes, (2) cerebellar tissue microstructure metrics (eg, mean diffusivity), and (3) functional connectivity in the visuospatial, auditory, and executive control subnetworks. RESULTSImaging studies were completed for 40 patients (mean age, 40.4 years; 23 [57.5%] men; imaging performed a median of 188 [range, 4-403] days after initial exposure) and 48 controls (mean age, 37.6 years; 33 [68.8%] men). Mean whole brain white matter volume was significantly smaller in patients compared with controls (patients: 542.22 cm 3 ; controls: 569.61 cm 3 ; difference, −27.39 [95% CI, −37.93 to −16.84] cm 3 ; P < .001), with no significant difference in the whole brain gray matter volume (patients: 698.55 cm 3 ; controls: 691.83 cm 3 ; difference, 6.72 [95% CI, −4.83 to 18.27] cm 3 ; P = .25). Among patients compared with controls, there were significantly greater ventral diencephalon and cerebellar gray matter volumes and significantly smaller frontal, occipital, and parietal lobe white matter volumes; significantly lower mean diffusivity in the inferior vermis of the cerebellum (patients: 7.

Background: Fiber tracking with diffusion-weighted MRI has become an essential tool for estimating in vivo brain white matter architecture. Fiber tracking results are sensitive to the choice of processing method and tracking criteria. Purpose: To assess the variability for an algorithm in group studies reproducibility is of critical context. However, reproducibility does not assess the validity of the brain connections. Phantom studies provide concrete quantitative comparisons of methods relative to absolute ground truths, yet do no capture variabilities because of in vivo physiological factors. The ISMRM 2017 TraCED challenge was created to fulfill the gap. Study Type: A systematic review of algorithms and tract reproducibility studies. Subjects: Single healthy volunteers.View this article online at wileyonlinelibrary.com.

Quality assessment of diffusion MRI (dMRI) data is essential prior to any analysis, so that appropriate pre-processing can be used to improve data quality and ensure that the presence of MRI artifacts do not affect the results of subsequent image analysis. Manual quality assessment of the data is subjective, possibly error-prone, and infeasible, especially considering the growing number of consortium-like studies, underlining the need for automation of the process. In this paper, we have developed a deep-learning-based automated quality control (QC) tool, QC-Automator, for dMRI data, that can handle a variety of artifacts such as motion, multiband interleaving, ghosting, susceptibility, herringbone and chemical shifts. QC-Automator uses convolutional neural networks along with transfer learning to train the automated artifact detection on a labeled dataset of ~332000 slices of dMRI data, from 155 unique subjects and 5 scanners with different dMRI acquisitions, achieving a 98% accuracy in detecting artifacts. The method is fast and paves the way for efficient and effective artifact detection in large datasets. It is also demonstrated to be replicable on other datasets with different acquisition parameters.

Objective. The use of non-invasive techniques for the estimation of structural brain networks (i.e. connectomes) opened the door to large-scale investigations on the functioning and the architecture of the brain, unveiling the link between neurological disorders and topological changes of the brain network. This study aims at assessing if and how the topology of structural connectomes estimated non-invasively with diffusion MRI is affected by the employment of tractography filtering techniques in structural connectomic pipelines. Additionally, this work investigates the robustness of topological descriptors of filtered connectomes to the common practice of density-based thresholding. Approach. We investigate the changes in global efficiency, characteristic path length, modularity and clustering coefficient on filtered connectomes obtained with the spherical deconvolution informed filtering of tractograms and using the convex optimization modelling for microstructure informed tractography. The analysis is performed on both healthy subjects and patients affected by traumatic brain injury and with an assessment of the robustness of the computed graph-theoretical measures with respect to density-based thresholding of the connectome. Main Result. Our results demonstrate that tractography filtering techniques change the topology of brain networks, and thus alter network metrics both in the pathological and the healthy cases. Moreover, the measures are shown to be robust to density-based thresholding. Significance. The present work highlights how the inclusion of tractography filtering techniques in connectomic pipelines requires extra caution as they systematically change the network topology both in healthy subjects and patients affected by traumatic brain injury. Finally, the practice of low-tomoderate density-based thresholding of the connectomes is confirmed to have negligible effects on the topological analysis.