An adaptive acquisition sequence for Sparse 2D Magnetic Resonance Imaging (MRI) is presented. The method combines random sampling of Cartesian trajectories with an adaptive 2D acquisition of radial projections. It is based on the evaluation of the information content of a small percentage of the k-space data collected randomly to identify radial blades of k-space coefficients having maximum information content. The information content of each direction is evaluated by calculating an entropy function defined on the power spectrum of the projections. The images are obtained by using a non linear reconstruction strategy, based on the homotopic L0-norm, on the sparse data. The method is tested on MRI images and it is also compared to the weighted Compressed Sensing. Some results are reported and discussed.

Adaptive Sampling and Reconstruction for Sparse Magnetic Resonance Imaging / Ciancarella, Laura; Avola, Danilo; Placidi, Giuseppe. - (2014), pp. 115-130. - LECTURE NOTES IN COMPUTATIONAL VISION AND BIOMECHANICS. [10.1007/978-3-319-04039-4_7].

Adaptive Sampling and Reconstruction for Sparse Magnetic Resonance Imaging

Avola, Danilo;
2014

Abstract

An adaptive acquisition sequence for Sparse 2D Magnetic Resonance Imaging (MRI) is presented. The method combines random sampling of Cartesian trajectories with an adaptive 2D acquisition of radial projections. It is based on the evaluation of the information content of a small percentage of the k-space data collected randomly to identify radial blades of k-space coefficients having maximum information content. The information content of each direction is evaluated by calculating an entropy function defined on the power spectrum of the projections. The images are obtained by using a non linear reconstruction strategy, based on the homotopic L0-norm, on the sparse data. The method is tested on MRI images and it is also compared to the weighted Compressed Sensing. Some results are reported and discussed.
2014
Computational Modeling of Objects Presented in Images (Lecture Notes in Computational Vision and Biomechanics)
978-3-319-04038-7
978-3-319-04039-4
Compressed sensing (CS); Homotopic L0-norm; L1-norm; Magnetic resonance imaging (MRI); Radial adaptive acquisition; Signal Processing; Biomedical Engineering; Mechanical Engineering; 1707; Computer Science Applications1707 Computer Vision and Pattern Recognition; Artificial Intelligence
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Adaptive Sampling and Reconstruction for Sparse Magnetic Resonance Imaging / Ciancarella, Laura; Avola, Danilo; Placidi, Giuseppe. - (2014), pp. 115-130. - LECTURE NOTES IN COMPUTATIONAL VISION AND BIOMECHANICS. [10.1007/978-3-319-04039-4_7].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1247118
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