PURPOSE:Three-dimensional (3D) multiplexed sensitivity encoding and reconstruction (3D-MUSER) algorithm is proposed to reduce aliasing artifacts and signal corruption caused by inter-shot 3D phase variations in 3D diffusion-weighted echo planar imaging (DW-EPI).THEORY AND METHODS:3D-MUSER extends the original framework of multiplexed sensitivity encoding (MUSE) to a hybrid k-space-based reconstruction, thereby enabling the correction of inter-shot 3D phase variations. A 3D single-shot EPI navigator echo was used to measure inter-shot 3D phase variations. The performance of 3D-MUSER was evaluated by analyses of point-spread function (PSF), signal-to-noise ratio (SNR), and artifact levels. The efficacy of phase correction using 3D-MUSER for different slab thicknesses and b-values were investigated.RESULTS:Simulations showed that 3D-MUSER could eliminate artifacts because of through-slab phase variation and reduce noise amplification because of SENSE reconstruction. All aliasing artifacts and signal corruption in 3D interleaved DW-EPI acquired with different slab thicknesses and b-values were reduced by our new algorithm. A near-whole brain single-slab 3D DTI with 1.3-mm isotropic voxel acquired at 1.5T was successfully demonstrated.CONCLUSION:3D phase correction for 3D interleaved DW-EPI data is made possible by 3D-MUSER, thereby improving feasible slab thickness and maximum feasible b-value.
Functional magnetic resonance imaging (fMRI) aims to localize task-related brain activation or resting-state functional connectivity. Most existing fMRI data analysis techniques rely on fixed thresholds to identify active voxels under a task condition or functionally connected voxels in the resting state. Due to fMRI non-stationarity, a fixed threshold cannot adapt to intra- and inter-subject variation and provide a reliable mapping of brain function. In this work, a machine learning method is proposed for a unified analysis of both task-related and resting state fMRI data. Specifically, the mapping of brain function in a task condition or resting state is formulated as an outlier detection process. Support vector machines are used to provide an initial mapping and refine mapping results. The method does not require a fixed threshold for the final decision, and can adapt to fMRI non-stationarity. The proposed method was evaluated using experimental data acquired from multiple human subjects. The results indicate that the proposed method can provide reliable mapping of brain function, and is applicable to various quantitative fMRI studies.
Parallel MRI techniques reconstruct full-FOV images from undersampled k-space data by using the uncorrelated information from RF array coil elements. One disadvantage of parallel MRI is that the image signal-to-noise ratio (SNR) is degraded because of the reduced data samples and the spatially correlated nature of multiple RF receivers. Regularization has been proposed to mitigate the SNR loss originating due to the latter reason. Since it is necessary to utilize static prior to regularization, the dynamic contrast-to-noise ratio (CNR) in parallel MRI will be affected. In this paper we investigate the CNR of regularized sensitivity encoding (SENSE) acquisitions. We propose to implement regularized parallel MRI acquisitions in functional MRI (fMRI) experiments by incorporating the prior from combined segmented echo-planar imaging (EPI) acquisition into SENSE reconstructions. We investigated the impact of regularization on the CNR by performing parametric simulations at various BOLD contrasts, acceleration rates, and sizes of the active brain areas. As quantified by receiver operating characteristic (ROC) analysis, the simulations suggest that the detection power of SENSE fMRI can be improved by regularized reconstructions, compared to unregularized reconstructions. Human motor and visual fMRI data acquired at different field strengths and array coils also demonstrate that regularized SENSE improves the detection of functionally active brain regions.
Line-scan echo planar spectroscopic imaging (LSEPSI) is applied to quickly measure the T2 and T2* relaxation time constants in pre-selected 2D or 3D regions. Results from brain imaging studies at 3T suggest that the proposed method may prove valuable for both basic research (e.g., quantifying the changes of T2/T2* values in functional MRI with blood oxygenation level-dependent contrast) and clinical studies (e.g., measuring the T2' shortening due to iron deposition). The proposed spatially selective T2 and T2* mapping technique is especially well suited for studies, where T2/T2* quantification needs to be performed dynamically in a pre-selected 2D or 3D region.
The combination of phase demodulation and field mapping is a practical method to correct echo planar imaging (EPI) geometric distortion. However, since phase dispersion accumulates in each phase-encoding step, the calculation complexity of phase modulation is Ny-fold higher than conventional image reconstructions. Thus, correcting EPI images via phase demodulation is generally a time-consuming task.
