Joint Estimation of Image and Fieldmap in Parallel MRI Using Single-Shot Acquisitions

We propose a method for joint reconstruction of dynamic images and fieldmaps in parallel MRI, using single-shot trajectories. We exploit the sensitivity encoding from parallel imaging to reduce the length of acquisition and essentially perform joint reconstruction using just one full k-space dataset. We also explore the use of modified trajectories (both EPI and spiral) that provide full coverage of k-space and also contain enough inherent time differences to permit accurate fieldmap estimation. Finally we improve the efficiency of the reconstruction algorithm by using a linearization technique for fieldmap estimation, which allows the use of the conjugate gradient algorithm.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85811/1/Fessler249.pd

Parallel Magnetic Resonance Imaging as Approximation in a Reproducing Kernel Hilbert Space

In Magnetic Resonance Imaging (MRI) data samples are collected in the spatial frequency domain (k-space), typically by time-consuming line-by-line scanning on a Cartesian grid. Scans can be accelerated by simultaneous acquisition of data using multiple receivers (parallel imaging), and by using more efficient non-Cartesian sampling schemes. As shown here, reconstruction from samples at arbitrary locations can be understood as approximation of vector-valued functions from the acquired samples and formulated using a Reproducing Kernel Hilbert Space (RKHS) with a matrix-valued kernel defined by the spatial sensitivities of the receive coils. This establishes a formal connection between approximation theory and parallel imaging. Theoretical tools from approximation theory can then be used to understand reconstruction in k-space and to extend the analysis of the effects of samples selection beyond the traditional g-factor noise analysis to both noise amplification and approximation errors. This is demonstrated with numerical examples.Comment: 28 pages, 7 figure

Multi-coil magnetic resonance imaging reconstruction with a Markov Random Field prior

Recent improvements in magnetic resonance image (MRI) reconstruction from partial data have been reported using spatial context modelling with Markov random field (MRF) priors. However, these algorithms have been developed only for magnitude images from single-coil measurements. In practice, most of the MRI images today are acquired using multi-coil data. In this paper, we extend our recent approach for MRI reconstruction with MRF priors to deal with multi-coil data i.e., to be applicable in parallel MRI (pMRI) settings. Instead of reconstructing images from different coils independently and subsequently combining them into the final image, we recover MRI image by processing jointly the undersampled measurements from all coils together with their estimated sensitivity maps. The proposed method incorporates a Bayesian formulation of the spatial context into the reconstruction problem. To solve the resulting problem, we derive an efficient algorithm based on the alternating direction method of multipliers (ADMM). Experimental results demonstrate the effectiveness of the proposed approach in comparison to some well-adopted methods for accelerated pMRI reconstruction from undersampled data