Chemical shift imaging (CSI) has been the mainstay of spectroscopic imaging because of its simple implementation, reliability and ease of image reconstruction. This technique has been widely used for observing the changes in the metabolic signature of tissues during evolving pathological and/or physiological conditions. CSI owes its ease of implementation and analysis to the Fourier encoding approach upon which is based. In this approach, the spectral-spatial information is encoded in a rectilinear fashion that favors the acquisition of very high-resolution information along the spectral axis and relatively low resolution along the spatial directions. For applications where higher spatial resolution is desired over a narrower spectral bandwidth, trajectory designs that repeatedly cross the center of k-space through the use of time-dependent gradients offer a convenient means to achieve significant speedups in data acquisition. This stems from the fact that the readout period could be used to acquire multiple spatial frequency values which, in turn, leads to a reduction in the total number of RF excitations required to provide proper encoding of the spatial and spectral information. Among the trajectory designs that could be well suited for such a spectroscopic imaging approach the Rosette data acquisition approach is particularly attractive because of its relatively simple implementation and modest gradient requirements. The time-varying nature of the gradients in this trajectory design, while flexible, leads to smooth variations in sample density and larger signal bandwidths than those associated with the CSI gold standard. Despite these potential drawbacks, because no time is spent collecting information in the corners of k-space, we demonstrate that rosette spectroscopic imaging (RSI) can lead to an efficiency gain over CSI in a wide range of spectral bandwidths and spatial resolutions. An analytic relationship for the number of excitations to be used in an RSI experiment is derived and a method to obtain a more accurate self-derived B0 map that uses the information of the prevalent resonance in each voxel and linear regression is offered. Moreover, we show that any imaging technique that periodically samples the center and edges of k-space could be used for spectroscopic imaging
