Application of the limited-memory quasi-Newton algorithm for multi-dimensional, large flip-angle RF pulses at 7T.

OBJECTIVE: Ultrahigh field MRI provides great opportunities for medical diagnostics and research. However, ultrahigh field MRI also brings challenges, such as larger magnetic susceptibility induced field changes. Parallel-transmit radio-frequency pulses can ameliorate these complications while performing advanced tasks in routine applications. To address one class of such pulses, we propose an optimal-control algorithm as a tool for designing advanced multi-dimensional, large flip-angle, radio-frequency pulses. We contrast initial conditions, constraints, and field correction abilities against increasing pulse trajectory acceleration factors. MATERIALS AND METHODS: On an 8-channel 7T system, we demonstrate the quasi-Newton algorithm with pulse designs for reduced field-of-view imaging with an oil phantom and in vivo with scans of the human brain stem. We used echo-planar imaging with 2D spatial-selective pulses. Pulses are computed sufficiently rapid for routine applications. RESULTS: Our dataset was quantitatively analyzed with the conventional mean-square-error metric and the structural-similarity index from image processing. Analysis of both full and reduced field-of-view scans benefit from utilizing both complementary measures. CONCLUSION: We obtained excellent outer-volume suppression with our proposed method, thus enabling reduced field-of-view imaging using pulse trajectory acceleration factors up to 4.

Network analysis shows decreased ipsilesional structural connectivity in glioma patients.

Gliomas that infiltrate networks and systems, such as the motor system, often lead to substantial functional impairment in multiple systems. Network-based statistics (NBS) allow to assess local network differences and graph theoretical analyses enable investigation of global and local network properties. Here, we used network measures to characterize glioma-related decreases in structural connectivity by comparing the ipsi- with the contralesional hemispheres of patients and correlated findings with neurological assessment. We found that lesion location resulted in differential impairment of both short and long connectivity patterns. Network analysis showed reduced global and local efficiency in the ipsilesional hemisphere compared to the contralesional hemispheric networks, which reflect the impairment of information transfer across different regions of a network.

Isotropically weighted intravoxel incoherent motion brain imaging at 7T.

Perfusion magnetic resonance imaging (MRI) is a promising non-invasive technique providing insights regarding the brain's microvascular architecture in vivo. The scalar perfusion metrics can be used for quantitative diagnostics of various brain abnormalities, in particular, in the stroke cases and tumours. However, conventional MRI-based perfusion approaches such as dynamic contrast-enhanced perfusion imaging or arterial spin labelling have a few weaknesses, for instance, contrast agent deposition, low signal-to-noise ratio, limited temporal and spatial resolution, and specific absorption rate constraints. As an alternative, the intravoxel incoherent motion (IVIM) approach exploits an extension of diffusion MRI in order to estimate perfusion parameters in the human brain. Application of IVIM imaging at ultra-high field MRI might employ the advantage of a higher signal-to-noise ratio, and thereby the use of higher spatial and temporal resolutions. In the present work, we demonstrate an application of recently developed isotropic diffusion weighted sequences to the evaluation of IVIM parameters at an ultra-high 7T field. The used sequence exhibits high immunity to image degrading factors and allows one to acquire the data in a fast and efficient way. Utilising the bi-exponential fitting model of the signal attenuation, we performed an extensive analysis of the IVIM scalar metrics obtained by a isotropic diffusion weighted sequence in vivo and compared results with a conventional pulsed gradient sequence at 7T. In order to evaluate a possible metric bias originating from blood flows, we additionally used a truncated b-value protocol (b-values from 100 to 200 s/mm2 with the step 20 s/mm2) accompanied to the full range (b-values from 0 to 200 s/mm2). The IVIM scalar metrics have been assessed and analysed together with a large and middle vessel density atlas of the human brain. We found that the diffusion coefficients and perfusion fractions of the voxels consisting of large and middle vessels have higher values in contrast to other tissues. Additionally, we did not find a strong dependence of the IVIM metrics on the density values of the vessel atlas. Perspectives and limitations of the developed isotropic diffusion weighted perfusion are presented and discussed.

Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks.

Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input) can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners) but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i) different neural subpopulations (e.g., excitatory and inhibitory neurons) have different cellular or connectivity parameters; (ii) the number and strength of the input connections are random (Erdos-Rényi topology) and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of parameters as indicated by comparison with simulation results of large recurrent networks. Our method can help to elucidate how network heterogeneity shapes the asynchronous state in recurrent neural networks.

Validation of DWI pre-processing procedures for reliable differentiation between human brain gliomas.

Diffusion magnetic resonance imaging (dMRI) is a powerful tool in clinical applications, in particular, in oncology screening. dMRI demonstrated its benefit and efficiency in the localisation and detection of different types of human brain tumours. Clinical dMRI data suffer from multiple artefacts such as motion and eddy-current distortions, contamination by noise, outliers etc. In order to increase the image quality of the derived diffusion scalar metrics and the accuracy of the subsequent data analysis, various pre-processing approaches are actively developed and used. In the present work we assess the effect of different pre-processing procedures such as a noise correction, different smoothing algorithms and spatial interpolation of raw diffusion data, with respect to the accuracy of brain glioma differentiation. As a set of sensitive biomarkers of the glioma malignancy grades we chose the derived scalar metrics from diffusion and kurtosis tensor imaging as well as the neurite orientation dispersion and density imaging (NODDI) biophysical model. Our results show that the application of noise correction, anisotropic diffusion filtering, and cubic-order spline interpolation resulted in the highest sensitivity and specificity for glioma malignancy grading. Thus, these pre-processing steps are recommended for the statistical analysis in brain tumour studies.

Anisotropic diffusion phantoms based on microcapillaries.

Diffusion MRI is an efficient and widely used technique for the investigation of tissue structure and organisation in vivo. Multiple phenomenological and biophysical diffusion models are intensively exploited for the analysis of the diffusion experiments. However, the verification of the applied diffusion models remains challenging. In order to provide a "gold standard" and to assess the accuracy of the derived parameters and the limitations of the diffusion models, anisotropic diffusion phantoms with well known architecture are demanded. In the present work we built four anisotropic diffusion phantoms consisting of hollow microcapillaries with very small inner diameters of 5, 10 and 20µm and outer diameters of 90 and 150µm. For testing the suitability of these phantoms, we performed diffusion measurements on all of them and evaluated the resulting data with a set of popular diffusion models, such as diffusion tensor and diffusion kurtosis imaging, a two compartment model with intra- and extra-capillary water spaces using bi-exponential fitting, and time-dependent diffusion coefficients in Mitra's limit. The perspectives and limitations of these diffusion phantoms are presented and discussed.

Comparative analysis of isotropic diffusion weighted imaging sequences.

Visualisation of living tissue structure and function is a challenging problem of modern imaging techniques. Diffusion MRI allows one to probe in vivo structures on a micrometer scale. However, conventional diffusion measurements are time-consuming procedures, because they require several measurements with different gradient directions. Considerable time savings are therefore possible by measurement schemes that generate an isotropic diffusion weighting in a single shot. Multiple approaches for generating isotropic diffusion weighting are known and have become very popular as useful tools in clinical research. Thus, there is a strong need for a comprehensive comparison of different isotropic weighting approaches. In the present work we introduce two new sequences based on simple (co)sine modulations and compare their performance to established q-space magic-angle spinning sequences and conventional DTI, using a diffusion phantom assembled from microcapillaries and in vivo experiments at 7T. The advantages and disadvantages of all compared schemes are demonstrated and discussed.