Time Samples Selection In Spiral Acquisition For Sparse Magnetic Resonance Spectroscopic Imaging

Magnetic resonance spectroscopic imaging (MRSI) has multiple interests in clinical practice, and especially for brain disease diagnosis. However, it faces quite long acquisition time in practice which limits their use in a clinical environment. Fast MRI acquisitions can help to reduce this acquisition time and intensify their clinical use. In this work, a new fast Magnetic Resonance Spectroscopic image acquisition method is introduced and evaluated based on a k-t space spiral sampling. The time-domain subsampling, below the Shannon-Nyquist rate, is allowed on the hypothesis of a sparse spectrum with an a priori known support. Then the nonzero components of the spectrum of each voxel are recovered using an over determined least square problem. When data are noise-free the recovered spectrum is exact. In the real-world noisy scenario the error in the recovered spectrum highly depends on the acquired samples. We reduce this error to an acceptable level by selecting irregularly the samples using the Sequential Backward Selection algorithm. A realistic simulated irregular spiral acquisition proves the feasibility of the proposed approach.