Comparison of sagittal and transverse echo planar spectroscopic imaging on the quantification of brain metabolites

Accelerated MR spectroscopic imaging-a review of current and emerging techniques

Over more than 30 years in vivo MR spectroscopic imaging (MRSI) has undergone an enormous evolution from theoretical concepts in the early 1980s to the robust imaging technique that it is today. The development of both fast and efficient sampling and reconstruction techniques has played a fundamental role in this process. State-of-the-art MRSI has grown from a slow purely phase-encoded acquisition technique to a method that today combines the benefits of different acceleration techniques. These include shortening of repetition times, spatial-spectral encoding, undersampling of k-space and time domain, and use of spatial-spectral prior knowledge in the reconstruction. In this way in vivo MRSI has considerably advanced in terms of spatial coverage, spatial resolution, acquisition speed, artifact suppression, number of detectable metabolites and quantification precision. Acceleration not only has been the enabling factor in high-resolution whole-brain 1 H-MRSI, but today is also common in non-proton MRSI (31 P, 2 H and 13 C) and applied in many different organs. In this process, MRSI techniques had to constantly adapt, but have also benefitted from the significant increase of magnetic field strength boosting the signal-to-noise ratio along with high gradient fidelity and high-density receive arrays. In combination with recent trends in image reconstruction and much improved computation power, these advances led to a number of novel developments with respect to MRSI acceleration. Today MRSI allows for non-invasive and non-ionizing mapping of the spatial distribution of various metabolites' tissue concentrations in animals or humans, is applied for clinical diagnostics and has been established as an important tool for neuro-scientific and metabolism research. This review highlights the developments of the last five years and puts them into the context of earlier MRSI acceleration techniques. In addition to 1 H-MRSI it also includes other relevant nuclei and is not limited to certain body regions or specific applications.

Reduction of lipid contamination in MR spectroscopy imaging using signal space projection

PURPOSE

Lipid contamination can complicate the metabolite quantification in MR spectroscopic imaging (MRSI). In addition to various experimental methods demonstrated to be feasible for lipid suppression, the postprocessing method is beneficial in the flexibility of applications. In this study, the signal space projection (SSP) algorithm is proposed to suppress the lipid signal in the MRSI.

METHODS

The performance of lipid suppression using SSP and SSP combined with the Papoulis-Gerchberg (PG) algorithm (PG+SSP) is examined in 2D MRSI data and the results were compared with outer volume saturation (OVS) methods. Up to 10 lipid spatial components were extracted by SSP from lipid signals in the range of 0.8~1.5 ppm.

RESULTS

Our results show that most lipid signals were found in the first 4 to 5 components and that lipid signals on the spectra can be suppressed using 4 to 5 components. Metabolites concentrations were quantified using LCModel. Two regions of interest (ROIs) were manually selected on the peripheral and inner brain regions. The quantification of metabolites in terms of fitting reliability (CRLB) and spatial variations within ROIs (SpaVar) is improved using SSP. When 5 to 6 components were used in SSP and PG+SSP, the metabolite concentrations and the associated SpaVar and CRLB are at the same level as those from the OVS.

CONCLUSION

We have demonstrated that the SSP method can be used to suppress the lipid signals of MRSI and SSP with 5 to 6 components is suggested to have a similar suppression performance as the OVS method.

Combining parallel detection of proton echo planar spectroscopic imaging (PEPSI) measurements with a data-consistency constraint improves SNR

One major challenge of MRSI is the poor signal-to-noise ratio (SNR), which can be improved by using a surface coil array. Here we propose to exploit the spatial sensitivity of different channels of a coil array to enforce the k-space data consistency (DC) in order to suppress noise and consequently to improve MRSI SNR. MRSI data were collected using a proton echo planar spectroscopic imaging (PEPSI) sequence at 3 T using a 32-channel coil array and were averaged with one, two and eight measurements (avg-1, avg-2 and avg-8). The DC constraint was applied using a regularization parameter λ of 1, 2, 3, 5 or 10. Metabolite concentrations were quantified using LCModel. Our results show that the suppression of noise by applying the DC constraint to PEPSI reconstruction yields up to 32% and 27% SNR gain for avg-1 and avg-2 data with λ = 5, respectively. According to the reported Cramer-Rao lower bounds, the improvement in metabolic fitting was significant (p < 0.01) when the DC constraint was applied with λ ≥ 2. Using the DC constraint with λ = 3 or 5 can minimize both root-mean-square errors and spatial variation for all subjects using the avg-8 data set as reference values. Our results suggest that MRSI reconstructed with a DC constraint can save around 70% of scanning time to obtain images and spectra with similar SNRs using λ = 5.