Analysis of the magneto-rotational instability with the effect of cosmic-ray diffusion

We present the results obtained from linear stability analysis and 2.5-dimensional magnetohydrodynamic (MHD) simulations of the magnetorotational instability (MRI), including the effects of cosmic rays (CRs). We took into account of the CR diffusion along the magnetic field but neglect the cross-field-line diffusion. Two models are considered in this paper: shearing box model and differentially rotating cylinder model. We studied how MRI is affected by the initial CR pressure (i.e., energy) distribution. In the shearing box model, the initial state is uniform distribution. Linear analysis shows that the growth rate of MRI does not depend on the value of CR diffusion coefficient. In the differentially rotating cylinder model, the initial state is a constant angular momentum polytropic disk threaded by weak uniform vertical magnetic field. Linear analysis shows that the growth rate of MRI becomes larger if the CR diffusion coefficient is larger. Both results are confirmed by MHD simulations. The MHD simulation results show that the outward movement of matter by the growth of MRI is not impeded by the CR pressure gradient, and the centrifugal force which acts to the concentrated matter becomes larger. Consequently, the growth rate of MRI is increased. On the other hand, if the initial CR pressure is uniform, then the growth rate of the MRI barely depends on the value of the CR diffusion coefficient.Comment: 41 pages, 12 figures, published in ApJ in 201

Probing white-matter microstructure with higher-order diffusion tensors and susceptibility tensor MRI.

Diffusion MRI has become an invaluable tool for studying white matter microstructure and brain connectivity. The emergence of quantitative susceptibility mapping and susceptibility tensor imaging (STI) has provided another unique tool for assessing the structure of white matter. In the highly ordered white matter structure, diffusion MRI measures hindered water mobility induced by various tissue and cell membranes, while susceptibility sensitizes to the molecular composition and axonal arrangement. Integrating these two methods may produce new insights into the complex physiology of white matter. In this study, we investigated the relationship between diffusion and magnetic susceptibility in the white matter. Experiments were conducted on phantoms and human brains in vivo. Diffusion properties were quantified with the diffusion tensor model and also with the higher order tensor model based on the cumulant expansion. Frequency shift and susceptibility tensor were measured with quantitative susceptibility mapping and susceptibility tensor imaging. These diffusion and susceptibility quantities were compared and correlated in regions of single fiber bundles and regions of multiple fiber orientations. Relationships were established with similarities and differences identified. It is believed that diffusion MRI and susceptibility MRI provide complementary information of the microstructure of white matter. Together, they allow a more complete assessment of healthy and diseased brains

Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks

Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue properties can be obtained using diffusion models and data processing pipelines. However, it is costly and time consuming to collect high quality diffusion data. Here, we therefore demonstrate how Generative Adversarial Networks (GANs) can be used to generate synthetic diffusion scalar measures from structural T1-weighted images in a single optimized step. Specifically, we train the popular CycleGAN model to learn to map a T1 image to FA or MD, and vice versa. As an application, we show that synthetic FA images can be used as a target for non-linear registration, to correct for geometric distortions common in diffusion MRI

Design of Anisotropic Diffusion Hardware Fiber Phantoms

A gold standard for the validation of diffusion weighted magnetic resonance imaging (DW-MRI) in brain white matter (WM) is essential for clinical purposes but still not available. Synthetic anisotropic fiber bundles are proposed as phantoms for the validation of DW-MRI because of their well-known structure, their long preservability and the possibility to create complex geometries such as curved and fiber crossings. A crucial question is how the different material properties and size of the fiber phantoms influence the outcome of the DW-MRI experiment. Several fiber materials are compared in this study. The effect of surface relaxation and internal gradients on the SNR is evaluated. In addition, the dependency of the fiber density and fiber radius on the diffusion properties is investigated

Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching in x-q Space

Diffusion MRI requires sufficient coverage of the diffusion wavevector space, also known as the q-space, to adequately capture the pattern of water diffusion in various directions and scales. As a result, the acquisition time can be prohibitive for individuals who are unable to stay still in the scanner for an extensive period of time, such as infants. To address this problem, in this paper we harness non-local self-similar information in the x-q space of diffusion MRI data for q-space upsampling. Specifically, we first perform neighborhood matching to establish the relationships of signals in x-q space. The signal relationships are then used to regularize an ill-posed inverse problem related to the estimation of high angular resolution diffusion MRI data from its low-resolution counterpart. Our framework allows information from curved white matter structures to be used for effective regularization of the otherwise ill-posed problem. Extensive evaluations using synthetic and infant diffusion MRI data demonstrate the effectiveness of our method. Compared with the widely adopted interpolation methods using spherical radial basis functions and spherical harmonics, our method is able to produce high angular resolution diffusion MRI data with greater quality, both qualitatively and quantitatively.Comment: 15 pages, 12 figure

MRI in multiple myeloma : a pictorial review of diagnostic and post-treatment findings

Magnetic resonance imaging (MRI) is increasingly being used in the diagnostic work-up of patients with multiple myeloma. Since 2014, MRI findings are included in the new diagnostic criteria proposed by the International Myeloma Working Group. Patients with smouldering myeloma presenting with more than one unequivocal focal lesion in the bone marrow on MRI are considered having symptomatic myeloma requiring treatment, regardless of the presence of lytic bone lesions. However, bone marrow evaluation with MRI offers more than only morphological information regarding the detection of focal lesions in patients with MM. The overall performance of MRI is enhanced by applying dynamic contrast-enhanced MRI and diffusion weighted imaging sequences, providing additional functional information on bone marrow vascularization and cellularity. This pictorial review provides an overview of the most important imaging findings in patients with monoclonal gammopathy of undetermined significance, smouldering myeloma and multiple myeloma, by performing a 'total' MRI investigation with implications for the diagnosis, staging and response assessment. Main message aEuro cent Conventional MRI diagnoses multiple myeloma by assessing the infiltration pattern. aEuro cent Dynamic contrast-enhanced MRI diagnoses multiple myeloma by assessing vascularization and perfusion. aEuro cent Diffusion weighted imaging evaluates bone marrow composition and cellularity in multiple myeloma. aEuro cent Combined morphological and functional MRI provides optimal bone marrow assessment for staging. aEuro cent Combined morphological and functional MRI is of considerable value in treatment follow-up

Stroke with unknown time of symptom onset: baseline clinical and magnetic resonance imaging data of the first thousand patients in WAKE-UP (efficacy and safety of mri-based thrombolysis in wake-up stroke: a randomized, doubleblind, placebo-controlled trial)

Background and Purpose—We describe clinical and magnetic resonance imaging (MRI) characteristics of stroke patients with unknown time of symptom onset potentially eligible for thrombolysis from a large prospective cohort. Methods—We analyzed baseline data from WAKE-UP (Efficacy and Safety of MRI-Based Thrombolysis in Wake-Up Stroke: A Randomized, Doubleblind, Placebo-Controlled Trial), an investigator-initiated, randomized, placebo-controlled trial of MRI-based thrombolysis in stroke patients with unknown time of symptom onset. MRI judgment included assessment of the mismatch between visibility of the acute ischemic lesion on diffusion-weighted imaging and fluid-attenuated inversion recovery. Results—Of 1005 patients included, diffusion-weighted imaging and fluid-attenuated inversion recovery mismatch was present in 479 patients (48.0%). Patients with daytime-unwitnessed stroke (n=138, 13.7%) had a shorter delay between symptom recognition and hospital arrival (1.5 versus 1.8 hours; P=0.002), a higher National Institutes of Stroke Scale score on admission (8 versus 6; P<0.001), and more often aphasia (72.5% versus 34.0%; P<0.001) when compared with stroke patients waking up from nighttime sleep. Frequency of diffusion-weighted imaging and fluid-attenuated inversion recovery mismatch was comparable between both groups (43.7% versus 48.7%; P=0.30). Conclusions—Almost half of the patients with unknown time of symptom onset stroke otherwise eligible for thrombolysis had MRI findings making them likely to be within a time window for safe and effective thrombolysis. Patients with daytime onset unwitnessed stroke differ from wake-up stroke patients with regards to clinical characteristics but are comparable in terms of MRI characteristics of lesion age. Clinical Trial Registration—URL: http://www.clinicaltrials.gov. Unique identifier: NCT01525290. URL: https://www.clinicaltrialsregister.eu. Unique identifier: 2011-005906-32