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Abstract: Combining fast MR acquisition sequences and high resolution imaging is amajor issue in dynamic imaging. Reducing the acquisition time can be achievedby using non-Cartesian and sparse acquisitions. The reconstruction of MR imagesfrom these measurements is generally carried out using gridding thatinterpolates the missing data to obtain a dense Cartesian k-space filling. TheMR image is then reconstructed using a conventional Fast Fourier Transform. Theestimation of the missing data unavoidably introduces artifacts in the imagethat remain difficult to quantify.A general reconstruction method is proposed to take into account theselimitations. It can be applied to any sampling trajectory in k-space, Cartesianor not, and specifically takes into account the exact location of the measureddata, without making any interpolation of the missing data in k-space.Information about the expected characteristics of the imaged object isintroduced to preserve the spatial resolution and improve the signal to noiseratio in a regularization framework. The reconstructed image is obtained byminimizing a non-quadratic convex objective function. An original rewriting ofthis criterion is shown to strongly improve the reconstruction efficiency.Results on simulated data and on a real spiral acquisition are presented anddiscussed.



Author: R. Boubertakh, J.-F. Giovannelli, A. Herment, A. De Cesare

Source: https://arxiv.org/



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