Spread spectrum compressed sensing MRI using chirp radio frequency pulses

Compressed sensing has shown great potential in reducing data acquisition time in magnetic resonance imaging (MRI). Recently, a spread spectrum compressed sensing MRI method modulates an image with a quadratic phase. It performs better than the conventional compressed sensing MRI with variable density sampling, since the coherence between the sensing and sparsity bases are reduced. However, spread spectrum in that method is implemented via a shim coil which limits its modulation intensity and is not convenient to operate. In this letter, we propose to apply chirp (linear frequency-swept) radio frequency pulses to easily control the spread spectrum. To accelerate the image reconstruction, an alternating direction method of multipliers (ADMM) algorithm is modified by exploiting the complex orthogonality of the quadratic phase encoding. Reconstruction on the acquired data demonstrates that more image features are preserved using the proposed approach than those of conventional compressed sensing MRI.

[1]  Frédéric Lesage,et al.  The Application of Compressed Sensing for , 2009 .

[2]  Richard G. Baraniuk,et al.  ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems , 2004, IEEE Transactions on Signal Processing.

[3]  D. Donoho,et al.  Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.

[4]  Di Guo,et al.  Projected Iterative Soft-Thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging , 2015, IEEE Transactions on Medical Imaging.

[5]  Jing Li,et al.  Partial Fourier transform reconstruction for single‐shot MRI with linear frequency‐swept excitation , 2013, Magnetic resonance in medicine.

[6]  Junfeng Yang,et al.  A Fast Alternating Direction Method for TVL1-L2 Signal Reconstruction From Partial Fourier Data , 2010, IEEE Journal of Selected Topics in Signal Processing.

[7]  Di Guo,et al.  A Simple and Fast Iterative Soft-thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging , 2015, ArXiv.

[8]  Justin P. Haldar,et al.  Compressed-Sensing MRI With Random Encoding , 2011, IEEE Transactions on Medical Imaging.

[9]  Zhong Chen,et al.  Undersampled MRI reconstruction with patch-based directional wavelets. , 2012, Magnetic resonance imaging.

[10]  Michal Irani,et al.  Super‐resolved spatially encoded single‐scan 2D MRI , 2010, Magnetic resonance in medicine.

[11]  Di Guo,et al.  Magnetic resonance image reconstruction using trained geometric directions in 2D redundant wavelets domain and non-convex optimization. , 2013, Magnetic resonance imaging.

[12]  Jean-Philippe Thiran,et al.  Spread Spectrum Magnetic Resonance Imaging , 2012, IEEE Transactions on Medical Imaging.

[13]  Di Guo,et al.  Fast Multiclass Dictionaries Learning With Geometrical Directions in MRI Reconstruction , 2015, IEEE Transactions on Biomedical Engineering.

[14]  E. Candès,et al.  Sparsity and incoherence in compressive sampling , 2006, math/0611957.

[15]  J. Finn,et al.  Cardiac MR imaging: state of the technology. , 2006, Radiology.

[16]  Di Guo,et al.  Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator , 2014, Medical Image Anal..