The diffusion‐weighted magnetic resonance imaging (dMRI) signal measured in vivo arises from multiple diffusion domains, including hindered and restricted water pools, free water and blood ...pseudo‐diffusion. Not accounting for the correct number of components can bias metrics obtained from model fitting because of partial volume effects that are present in, for instance, diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI). Approaches that aim to overcome this shortcoming generally make assumptions about the number of considered components, which are not likely to hold for all voxels. The spectral analysis of the dMRI signal has been proposed to relax assumptions on the number of components. However, it currently requires a clinically challenging signal‐to‐noise ratio (SNR) and accounts only for two diffusion processes defined by hard thresholds. In this work, we developed a method to automatically identify the number of components in the spectral analysis, and enforced its robustness to noise, including outlier rejection and a data‐driven regularization term. Furthermore, we showed how this method can be used to take into account partial volume effects in DTI and DKI fitting. The proof of concept and performance of the method were evaluated through numerical simulations and in vivo MRI data acquired at 3 T. With simulations our method reliably decomposed three diffusion components from SNR = 30. Biases in metrics derived from DTI and DKI were considerably reduced when components beyond hindered diffusion were taken into account. With the in vivo data our method determined three macro‐compartments, which were consistent with hindered diffusion, free water and pseudo‐diffusion. Taking free water and pseudo‐diffusion into account in DKI resulted in lower mean diffusivity and higher fractional anisotropy values in both gray and white matter. In conclusion, the proposed method allows one to determine co‐existing diffusion compartments without prior assumptions on their number, and to account for undesired signal contaminations within clinically achievable SNR levels.
The diffusion‐weighted magnetic resonance imaging (dMRI) signal measured in vivo arises from multiple water pools. Here, we present a method to disentangle such contributions without prior knowledge on the number of components. The voxel‐wise diffusion components were consistently and automatically grouped into compartments that corresponded, in vivo, to hindered diffusion, free water and pseudo‐diffusion. The approach can be used to effectively attenuate biases caused by partial volume effects in diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI).
ABSTRACT
Introduction: The aim of this study was to apply quantitative MRI (qMRI) to assess structural modifications in thigh muscles of subjects with limb girdle muscular dystrophy (LGMD) 2A and 2B ...with long disease duration. Methods: Eleven LGMD2A, 9 LGMD2B patients and 11 healthy controls underwent a multi‐parametric 3T MRI examination of the thigh. The protocol included structural T1‐weighted images, DIXON sequences for fat fraction calculation, T2 values quantification and diffusion MRI. Region of interest analysis was performed on 4 different compartments (anterior compartment, posterior compartment, gracilis, sartorius). Results: Patients showed high levels of fat infiltration as measured by DIXON sequences. Sartorius and anterior compartment were more infiltrated in LGMD2B than LGMD2A patients. T2 values were mildly reduced in both disorders. Correlations between clinical scores and qMRI were found. Conclusions: qMRI measures may help to quantify muscular degeneration, but careful interpretation is needed when fat infiltration is massive. Muscle Nerve 58: 550–558, 2018
•Estimate all parameters of the noise distribution from magnitude data only.•Robust to artifacts and fully automated on both data and noise maps.•Experiments with multiband, parallel MRI and three ...reconstruction algorithms.•Stable and reproducible on repeated acquisitions of the same subject.•Reduce noise on a bias correction and denoising task at high b-value.
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Knowledge of the noise distribution in magnitude diffusion MRI images is the centerpiece to quantify uncertainties arising from the acquisition process. The use of parallel imaging methods, the number of receiver coils and imaging filters applied by the scanner, amongst other factors, dictate the resulting signal distribution. Accurate estimation beyond textbook Rician or noncentral chi distributions often requires information about the acquisition process (e.g., coils sensitivity maps or reconstruction coefficients), which is usually not available. We introduce two new automated methods using the moments and maximum likelihood equations of the Gamma distribution to estimate noise distributions as they explicitly depend on the number of coils, making it possible to estimate all unknown parameters using only the magnitude data. A rejection step is used to make the framework automatic and robust to artifacts. Simulations using stationary and spatially varying noncentral chi noise distributions were created for two diffusion weightings with SENSE or GRAPPA reconstruction and 8, 12 or 32 receiver coils. Furthermore, MRI data of a water phantom with different combinations of parallel imaging were acquired on a 3T Philips scanner along with noise-only measurements. Finally, experiments on freely available datasets from a single subject acquired on a 3T GE scanner are used to assess reproducibility when limited information about the acquisition protocol is available. Additionally, we demonstrated the applicability of the proposed methods for a bias correction and denoising task on an in vivo dataset acquired on a 3T Siemens scanner. A generalized version of the bias correction framework for non integer degrees of freedom is also introduced. The proposed framework is compared with three other algorithms with datasets from three vendors, employing different reconstruction methods. Simulations showed that assuming a Rician distribution can lead to misestimation of the noise distribution in parallel imaging. Results on the acquired datasets showed that signal leakage in multiband can also lead to a misestimation of the noise distribution. Repeated acquisitions of in vivo datasets show that the estimated parameters are stable and have lower variability than compared methods. Results for the bias correction and denoising task show that the proposed methods reduce the appearance of noise at high b-value. The proposed algorithms herein can estimate both parameters of the noise distribution automatically, are robust to signal leakage artifacts and perform best when used on acquired noise maps.
Objectives
Diffusion-weighted MRI can assist preoperative planning by reconstructing the trajectory of eloquent fiber pathways, such as the corticospinal tract (CST). However, accurate reconstruction ...of the full extent of the CST remains challenging with existing tractography methods. We suggest a novel tractography algorithm exploiting unused fiber orientations to produce more complete and reliable results.
Methods
Our novel approach, referred to as multi-level fiber tractography (MLFT), reconstructs fiber pathways by progressively considering previously unused fiber orientations at multiple levels of tract propagation. Anatomical priors are used to minimize the number of false-positive pathways. The MLFT method was evaluated on synthetic data and in vivo data by reconstructing the CST while compared to conventional tractography approaches.
Results
The radial extent of MLFT reconstructions is comparable to that of probabilistic reconstruction:
p
=
0.21
for the left and
p
=
0.53
for the right hemisphere according to Wilcoxon test, while achieving significantly higher topography preservation compared to probabilistic tractography:
p
<
0.01
.
Discussion
MLFT provides a novel way to reconstruct fiber pathways by adding the capability of including branching pathways in fiber tractography. Thanks to its robustness, feasible reconstruction extent and topography preservation, our approach may assist in clinical practice as well as in virtual dissection studies.
We investigated the added value of combining information from direction-encoded color (DEC) maps with high-resolution structural magnetic resonance imaging scans (T1-weighted images T1WIs) to improve ...the identification of regions of interest (ROIs) for fiber tracking during preoperative planning for patients with brain tumors.
The dataset included 42 patients with gliomas and 10 healthy subjects from the Human Connectome Project. For identification of the ROIs, we combined the structural information from high-resolution T1WIs and the directional information from DEC maps. To test our hypothesis, we examined the interrater and intrarater agreement.
We identified specific ROIs to extract the main white matter bundles. The directional information from the DEC maps combined with the T1WIs (T1WI–DEC maps) had significantly facilitated ROI identification in patients with brain tumors, especially patients in whom the tracts had been displaced by the mass effect of the tumor. Fiber tracking using the combined T1WI–DEC maps showed significantly greater inter- and intrarater agreement compared with using either T1WI or DEC maps alone.
Combining the information from diffusion-derived color-encoded maps with high-resolution anatomical details from structural imaging (T1WI–DEC map), especially in patients with brain tumors, could be useful for accurate identification of the ROIs.
Diffusion kurtosis imaging (DKI) is applied to gain insights into the microstructural organization of brain tissues. However, the reproducibility of DKI outside brain white matter, particularly in ...combination with advanced estimation to remedy its noise sensitivity, remains poorly characterized. Therefore, in this study, we investigated the variability and reliability of DKI metrics while correcting implausible values with a fit method called mean kurtosis (MK)‐Curve. A total of 10 volunteers (four women; age: 41.4 ± 9.6 years) were included and underwent two MRI examinations of the brain. The images were acquired on a clinical 3‐T scanner and included a T1‐weighted image and a diffusion sequence with multiple diffusion weightings suitable for DKI. Region of interest analysis of common kurtosis and tensor metrics derived with the MK‐Curve DKI fit was performed, including intraclass correlation (ICC) and Bland–Altman (BA) plot statistics. A p value of less than 0.05 was considered statistically significant. The analyses showed good to excellent agreement of both kurtosis tensor‐ and diffusion tensor‐derived MK‐Curve–corrected metrics (ICC values: 0.77–0.98 and 0.87–0.98, respectively), with the exception of two DKI‐derived metrics (axial kurtosis in the cortex: ICC = 0.68, and radial kurtosis in deep gray matter: ICC = 0.544). Non‐MK‐Curve–corrected kurtosis tensor‐derived metrics ranged from 0.01 to 0.52 and diffusion tensor‐derived metrics from 0.06 to 0.66, indicating poor to moderate reliability. No structural bias was observed in the BA plots for any of the diffusion metrics. In conclusion, MK‐Curve–corrected DKI metrics of the human brain can be reliably acquired in white and gray matter at 3 T and DKI metrics have good to excellent agreement in a test–retest setting.
•VBA showed lower MK in corona radiata and posterior association fibers in BD.•Lower MK and KA found in connections traversing temporal and occipital lobes in BD.•Lower mean AK in right cerebellar, ...thalamo-subcortical pathways in BD.•Significant differences were not seen in DTI metrics following FDR-correction.
White matter pathology likely contributes to the pathogenesis of bipolar disorder (BD). Most studies of white matter in BD have used diffusion tensor imaging (DTI), but the advent of more advanced multi-shell diffusion MRI imaging offers the possibility to investigate other aspects of white matter microstructure. Diffusion kurtosis imaging (DKI) extends the DTI model and provides additional measures related to diffusion restriction. Here, we investigated white matter in BD by applying whole-brain voxel-based analysis (VBA) and a network-based connectivity approach using constrained spherical deconvolution tractography to assess differences in DKI and DTI metrics between BD (n = 25) and controls (n = 24). The VBA showed lower mean kurtosis in the corona radiata and posterior association fibers in BD. Regional differences in connectivity were indicated by lower mean kurtosis and kurtosis anisotropy in streamlines traversing the temporal and occipital lobes, and lower mean axial kurtosis in the right cerebellar, thalamo-subcortical pathways in BD. Significant differences were not seen in DTI metrics following FDR-correction. The DKI findings indicate altered connectivity across cortical, subcortical and cerebellar areas in BD. DKI is sensitive to different microstructural properties and is a useful complementary technique to DTI to more fully investigate white matter in BD.
One of the most important obstacles for taking advantage of forest resources in the Italian Alps is represented by the high level of private properties fragmentation and by their small size. Thus, ...there is an urgent need for tools to support single or multi-forest owners to gain reliable and updated information on their forest stands so that the proper silvicultural activities following all the existing regulations can be adopted. The present research was aimed at promoting a shared management of small private forest properties in the mountainous area of Friuli Venezia Giulia (NE Italy) through the implementation of a new WebGIS tool to support forest decisions and management at different spatial scales. This new tool was developed updating and merging together different available information sources (e.g., tree species composition, the presence of protected areas, forest roads, etc.) with ad-hoc elaborated layers (e.g., standing volume, annual increment of volume, forest accessibility, etc.), also elaborating a cost analysis related to the different possible harvesting methods. The tool allows queries at the level of either a single or a group of cadastral parcels to obtain data in a format, which can be used for filling in the planning document requested by the regional authorities.
Reptiles, including snakes, can be asymptomatically infected with multiple pathogen microorganisms, including
Salmonella
spp., which is considered an important concern for public and animal health. ...Small and uninhabited isles are quite ecologically different from mainland and represent interesting fields of study, to discover unexpected biological and microbiological aspects of their wild inhabitants. This work reports the presence of the very rare
Salmonella enterica
serovar Yopougon, isolated in a carcass of a native wild snake (
Hierophis viridiflavus
) from an Italian uninhabited island of Mediterranean Sea, Montecristo. To our knowledge,
S. enterica
serovar Yopougon was previously isolated only once 34 years earlier in Ivory Coast, from a human fecal sample. In the present study, we present the genomic characterization of the new isolate, the phylogenetic comparison with the previously isolated
S. enterica
serovar Yopougon strain of human origin and with other sequences available in public databases. In addition, an extensive review of available data in the literature and from our case history is provided. Our finding represents an example of the ability of some pathogens to travel for very long distances within their hosts and then to infect others, even from different taxa.
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