1,836 research outputs found
High resolution in-vivo MR-STAT using a matrix-free and parallelized reconstruction algorithm
MR-STAT is a recently proposed framework that allows the reconstruction of
multiple quantitative parameter maps from a single short scan by performing
spatial localisation and parameter estimation on the time domain data
simultaneously, without relying on the FFT. To do this at high-resolution,
specialized algorithms are required to solve the underlying large-scale
non-linear optimisation problem. We propose a matrix-free and parallelized
inexact Gauss-Newton based reconstruction algorithm for this purpose. The
proposed algorithm is implemented on a high performance computing cluster and
is demonstrated to be able to generate high-resolution (
in-plane resolution) quantitative parameter maps in simulation, phantom and
in-vivo brain experiments. Reconstructed and values for the gel
phantoms are in agreement with results from gold standard measurements and for
the in-vivo experiments the quantitative values show good agreement with
literature values. In all experiments short pulse sequences with robust
Cartesian sampling are used for which conventional MR Fingerprinting
reconstructions are shown to fail.Comment: Accepted by NMR in Biomedicine on 2019-12-0
Aggregated motion estimation for real-time MRI reconstruction
Real-time magnetic resonance imaging (MRI) methods generally shorten the
measuring time by acquiring less data than needed according to the sampling
theorem. In order to obtain a proper image from such undersampled data, the
reconstruction is commonly defined as the solution of an inverse problem, which
is regularized by a priori assumptions about the object. While practical
realizations have hitherto been surprisingly successful, strong assumptions
about the continuity of image features may affect the temporal fidelity of the
estimated images. Here we propose a novel approach for the reconstruction of
serial real-time MRI data which integrates the deformations between nearby
frames into the data consistency term. The method is not required to be affine
or rigid and does not need additional measurements. Moreover, it handles
multi-channel MRI data by simultaneously determining the image and its coil
sensitivity profiles in a nonlinear formulation which also adapts to
non-Cartesian (e.g., radial) sampling schemes. Experimental results of a motion
phantom with controlled speed and in vivo measurements of rapid tongue
movements demonstrate image improvements in preserving temporal fidelity and
removing residual artifacts.Comment: This is a preliminary technical report. A polished version is
published by Magnetic Resonance in Medicine. Magnetic Resonance in Medicine
201
Fast T2 Mapping with Improved Accuracy Using Undersampled Spin-echo MRI and Model-based Reconstructions with a Generating Function
A model-based reconstruction technique for accelerated T2 mapping with
improved accuracy is proposed using undersampled Cartesian spin-echo MRI data.
The technique employs an advanced signal model for T2 relaxation that accounts
for contributions from indirect echoes in a train of multiple spin echoes. An
iterative solution of the nonlinear inverse reconstruction problem directly
estimates spin-density and T2 maps from undersampled raw data. The algorithm is
validated for simulated data as well as phantom and human brain MRI at 3 T. The
performance of the advanced model is compared to conventional pixel-based
fitting of echo-time images from fully sampled data. The proposed method yields
more accurate T2 values than the mono-exponential model and allows for
undersampling factors of at least 6. Although limitations are observed for very
long T2 relaxation times, respective reconstruction problems may be overcome by
a gradient dampening approach. The analytical gradient of the utilized cost
function is included as Appendix.Comment: 10 pages, 7 figure
Physics-based Reconstruction Methods for Magnetic Resonance Imaging
Conventional Magnetic Resonance Imaging (MRI) is hampered by long scan times
and only qualitative image contrasts that prohibit a direct comparison between
different systems. To address these limitations, model-based reconstructions
explicitly model the physical laws that govern the MRI signal generation. By
formulating image reconstruction as an inverse problem, quantitative maps of
the underlying physical parameters can then be extracted directly from
efficiently acquired k-space signals without intermediate image reconstruction
-- addressing both shortcomings of conventional MRI at the same time. This
review will discuss basic concepts of model-based reconstructions and report
about our experience in developing several model-based methods over the last
decade using selected examples that are provided complete with data and code.Comment: 8 figures, review accepted to Philos. Trans. R. Soc.
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