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In this paper we investigate the utility of several low-rank models for recovery of Magnetic Resonance Imaging (MRI) data from limited sampling in the k — t space for dynamic imaging. In particular, for 3D temporal (2D space + time) MRI data we employ several tensor factorization techniques and assess the degree of dimensionality reduction, or compressibility, that can be obtained. This algebraic...
The quality of high-resolution Echo Planar images of the human brain has improved greatly in recent years, enabled by novel multi-channel receiver coil arrays and parallel imaging. However, in regions with local field inhomogeneity, EPI artifacts limit which parts of the brain can be imaged successfully. In this work, we present evidence that certain image artifacts can be attributed to nonlinear...
Functional MR Imaging (fMRI) is a widely used mechanism to non-invasively evaluate and assess neural activity in the brain, with echo planar imaging (EPI) the most common sequence used in fMRI studies. EPI methods are attractive because they provide functional imaging data of the whole brain at reasonable temporal resolutions. However, EPI is notoriously susceptible to multiple artifacts. This includes...
Echo planar imaging (EPI) is used widely for neurological fMRI studies. However, in regions of magnetic field inhomogeneity - particularly near the nasal sinuses - EPI suffers from signal dropout. Z-shim methods are one approach to recover this lost signal, where a z gradient is employed prior to the EPI readout echo train to counter phase accumulation in the signal dropout region. Typically, two...
We present an adaptation of our previous fast, regularized parallel MRI reconstruction approach (LSQR-hybrid) to encompass the reconstruction of partial-Fourier data. Reconstructions of partial-Fourier data require a constraint on the signal phase variation in the reconstructed image. Here, we employ a two-parameter Tikhonov regularization formulation to constrain the real and imaginary components...
Two image reconstruction methods currently dominate parallel MR imaging: SENSE and GRAPPA. While both seek to reconstruct images from subsampled multi-channel MRI data, there exist fundamental differences between the two. In particular, SENSE reconstructs an image of the excited spin-density directly whereas GRAPPA reconstructs estimates of the fully sampled raw coil data and then combines them to...
Parallel MR imaging is an effective approach to reduce MR image acquisition time. Non-uniform subsampling allows one to tailor the subsampling scheme for improved image quality at high acceleration factors. However, non-uniform subsampling precludes fast reconstruction schemes such as SENSE, and is more likely to require a regularized solution than reconstruction of uniformly subsampled data demands...
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