The standard method of diffusion tensor imaging (DTI) involves one diffusion-sensitizing gradient direction per acquired signal. This paper describes an alternative method in which the entire direction set required for calculating the diffusion tensor is captured in a few scans. In this method, a series of radiofrequency (RF) pulses are applied, resulting in a train of spin echoes.
Transport of water and ions through cell membranes plays an important role in cell metabolism. We demonstrate a novel technique to measure water transport dynamics using erythrocyte suspensions as an example. This technique takes advantage of inhomogeneous internal magnetic field created by the magnetic susceptibility contrast between the erythrocytes and plasma. The decay of longitudinal magnetization due to diffusion in this internal field reveals multi-exponential behavior, with one component corresponding to the diffusive exchange of water across erythrocyte membrane.
Long scan times of 3D volumetric MR acquisitions usually necessitate ultrafast in vivo gradient-echo acquisitions, which are intrinsically susceptible to magnetic field inhomogeneities. This is especially problematic for contrast-enhanced (CE)-MRI applications, where non-negligible T2* effect of contrast agent deteriorates the positive signal contrast and limits the available range of MR acquisition parameters and injection doses. To overcome these shortcomings without degrading temporal resolution, ultrafast spin-echo acquisitions were implemented.
In making sense of the visual world, the brain's processing is driven by two factors: the physical information provided by the eyes ("bottom-up" data) and the expectancies driven by past experience ("top-down" influences). We use degraded stimuli to tease apart the effects of bottom-up and top-down processes because they are easier to recognize with prior knowledge of undegraded images. Using machine learning algorithms, we quantify the amount of information that brain regions contain about stimuli as the subject learns the coherent images.
The recent advancement of simultaneous multi-slice imaging using multiband excitation has dramatically reduced the scan time of the brain. The evolution of this parallel imaging technique began over a decade ago and through recent sequence improvements has reduced the acquisition time of multi-slice EPI by over ten fold. This technique has recently become extremely useful for (i) functional MRI studies improving the statistical definition of neuronal networks, and (ii) diffusion based fiber tractography to visualize structural connections in the human brain.
Estimation of the diffusion propagator from a sparse set of diffusion MRI (dMRI) measurements is a field of active research. Sparse reconstruction methods propose to reduce scan time and are particularly suitable for scanning un-coperative patients. Recent work on reconstructing the diffusion signal from very few measurements using compressed sensing based techniques has focussed on propagator (or signal) estimation at each voxel independently. However, the goal of many neuroscience studies is to use tractography to study the pathology in white matter fiber tracts.
Diffusion magnetic resonance imaging (dMRI) is an important tool that allows non-invasive investigation of neural architecture of the brain. The data obtained from these in-vivo scans provides important information about the integrity and connectivity of neural fiber bundles in the brain. A multi-shell imaging (MSI) scan can be of great value in the study of several psychiatric and neurological disorders, yet its usability has been limited due to the long acquisition times required.
It is well known that natural languages share certain aspects of their design. For example, across languages, syllables like blif are preferred to lbif. But whether language universals are myths or mentally active constraints-linguistic or otherwise-remains controversial. To address this question, we used fMRI to investigate brain response to four syllable types, arrayed on their linguistic well-formedness (e.g., blif≻bnif≻bdif≻lbif, where ≻ indicates preference).
