Column-based cortical depth analysis of the diffusion anisotropy and radiality in submillimeter whole-brain diffusion tensor imaging of the human cortical gray matter in vivo.

High-resolution diffusion tensor imaging (DTI) can noninvasively probe the microstructure of cortical gray matter in vivo. In this study, 0.9-mm isotropic whole-brain DTI data were acquired in healthy subjects with an efficient multi-band multi-shot echo-planar imaging sequence.

Simultaneous and inherent correction of B and eddy-current induced distortions in high-resolution diffusion MRI using reversed polarity gradients and multiplexed sensitivity encoding (RPG-MUSE).

In diffusion MRI (dMRI), static magnetic field (B) inhomogeneity and time varying gradient eddy currents induce spatial distortions in reconstructed images. These distortions are exacerbated when high spatial resolutions are used, and many field-mapping based correction techniques often only acquire maps of static B distortion, which are not adequate for correcting eddy current induced image distortions. This report presents a novel technique, termed RPG-MUSE, for achieving distortion-free high-resolution diffusion MRI by integrating reversed polarity gradients (RPG) into the multi-shot echo planar imaging acquisition scheme used in multiplexed sensitivity encoding (MUSE).

3D-MB-MUSE: A robust 3D multi-slab, multi-band and multi-shot reconstruction approach for ultrahigh resolution diffusion MRI.

Recent advances in achieving ultrahigh spatial resolution (e.g. sub-millimeter) diffusion MRI (dMRI) data have proven highly beneficial in characterizing tissue microstructures in organs such as the brain.

Separation of parallel encoded complex-valued slices (SPECS) from a single complex-valued aliased coil image.

Purpose: Achieving a reduction in scan time with minimal inter-slice signal leakage is one of the significant obstacles in parallel MR imaging. In fMRI, multiband-imaging techniques accelerate data acquisition by simultaneously magnetizing the spatial frequency spectrum of multiple slices. The SPECS model eliminates the consequential inter-slice signal leakage from the slice unaliasing, while maintaining an optimal reduction in scan time and activation statistics in fMRI studies.

Incorporating relaxivities to more accurately reconstruct MR images.

Purpose: To develop a mathematical model that incorporates the magnetic resonance relaxivities into the image reconstruction process in a single step.

Materials And Methods: In magnetic resonance imaging, the complex-valued measurements of the acquired signal at each point in frequency space are expressed as a Fourier transformation of the proton spin density weighted by Fourier encoding anomalies: T2(⁎), T1, and a phase determined by magnetic field inhomogeneity (∆B) according to the MR signal equation. Such anomalies alter the expected symmetry and the signal strength of the k-space observations, resulting in images distorted by image warping, blurring, and loss in image intensity.

Quantification of the statistical effects of spatiotemporal processing of nontask FMRI data.

Nontask functional magnetic resonance imaging (fMRI) has become one of the most popular noninvasive areas of brain mapping research for neuroscientists. In nontask fMRI, various sources of "noise" corrupt the measured blood oxygenation level-dependent signal. Many studies have aimed to attenuate the noise in reconstructed voxel measurements through spatial and temporal processing operations.

Quantifying the statistical impact of GRAPPA in fcMRI data with a real-valued isomorphism.

The interpolation of missing spatial frequencies through the generalized auto-calibrating partially parallel acquisitions (GRAPPA) parallel magnetic resonance imaging (MRI) model implies a correlation is induced between the acquired and reconstructed frequency measurements. As the parallel image reconstruction algorithms in many medical MRI scanners are based on the GRAPPA model, this study aims to quantify the statistical implications that the GRAPPA model has in functional connectivity studies. The linear mathematical framework derived in the work of Rowe , 2007, is adapted to represent the complex-valued GRAPPA image reconstruction operation in terms of a real-valued isomorphism, and a statistical analysis is performed on the effects that the GRAPPA operation has on reconstructed voxel means and correlations.

A statistical fMRI model for differential T2* contrast incorporating T1 and T2* of gray matter.

Relaxation parameter estimation and brain activation detection are two main areas of study in magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI). Relaxation parameters can be used to distinguish voxels containing different types of tissue whereas activation determines voxels that are associated with neuronal activity. In fMRI, the standard practice has been to discard the first scans to avoid magnetic saturation effects.

The SENSE-Isomorphism Theoretical Image Voxel Estimation (SENSE-ITIVE) model for reconstruction and observing statistical properties of reconstruction operators.

The acquisition of sub-sampled data from an array of receiver coils has become a common means of reducing data acquisition time in MRI. Of the various techniques used in parallel MRI, SENSitivity Encoding (SENSE) is one of the most common, making use of a complex-valued weighted least squares estimation to unfold the aliased images. It was recently shown in Bruce et al.

A statistical examination of SENSE image reconstruction via an isomorphism representation.

In magnetic resonance imaging, the parallel acquisition of subsampled spatial frequencies from an array of multiple receiver coils has become a common means of reducing data acquisition time. SENSitivity Encoding (SENSE) is a popular parallel image reconstruction model that uses a complex-valued least squares estimation process to unfold aliased images. In this article, the linear mathematical framework derived in Rowe et al.