Spread Spectrum For Compressed Sensing Techniques In Magnetic Resonance Imaging
Magnetic resonance imaging (MRI) probes signals through Fourier measurements. Accelerating the acquisition process is of major interest for various MRI applications. The recent theory of compressed sensing shows that sparse or compressible signals may be reconstructed from a small number of random measurements in a sensing basis incoherent with the sparsity basis. In this context, we advocate the use of a chirp modulation of MRI signals prior to probing an incomplete Fourier coverage, in the perspective of accelerating the acquisition process relative to a standard setting with complete coverage. We analyze the spread spectrum phenomenon related to the modulation and we prove its effectiveness in enhancing the overall quality of image reconstruction. We also study its impact at each scale of decomposition in a wavelet sparsity basis. Our preliminary results rely both on theoretical considerations related to the mutual coherence between the sparsity and sensing bases, as well as on numerical simulations from synthetic signals.