Fast multidimensional NMR spectroscopy for sparse spectra

Comparison of MRI-derived vs. traditional estimations of fatty acid composition from MR spectroscopy signals

INTRODUCTION

The composition of fatty acids in the body is gaining increasing interest, and can be followed up noninvasively by quantitative magnetic resonance spectroscopy (MRS). However, current MRS quantification methods have been shown to provide different quantitative results in terms of lipid signals, with possible varying outcomes for a given biological examination. Quantitative magnetic resonance imaging using multigradient echo sequence (MGE-MRI) has recently been added to MRS approaches. In contrast, these methods fit the undersampled magnetic resonance temporal signal with a simplified model function (expressing the triglyceride [TG] spectrum with only three TG parameters), specific implementations and prior knowledge. In this study, an adaptation of an MGE-MRI method to MRS lipid quantification is proposed.

METHODS

Several versions of the method - with time data fully or undersampled, including or excluding the spectral peak T₂ knowledge in the fitting - were compared theoretically and on Monte Carlo studies with a time-domain, peak-fitting approach. Robustness, repeatability and accuracy were also inspected on in vitro oil acquisitions and test-retest in vivo subcutaneous adipose tissue acquisitions, adding results from the reference LCModel method.

RESULTS

On simulations, the proposed method provided TG parameter estimates with the smallest variability, but with a possible bias, which was mitigated by fitting on undersampled data and considering peak T₂ values. For in vitro measurements, estimates for all approaches were correlated with theoretical values and the best concordance was found for the usual MRS method (LCModel and peak fitting). Limited in vivo test-retest variability was found (4.1% for PUFAindx, 0.6% for MUFAindx and 3.6% for SFAindx), as for LCModel (7.6% for PUFAindx, 7.8% for MUFAindx and 3.0% for SFAindx).

CONCLUSION

This study shows that fitting the three TG parameters directly on MRS data is one valuable solution to circumvent the poor conditioning of the MRS quantification problem.

Compressed sensing for high-resolution nonlipid suppressed 1 H FID MRSI of the human brain at 9.4T

PURPOSE

The aim of this study was to apply compressed sensing to accelerate the acquisition of high resolution metabolite maps of the human brain using a nonlipid suppressed ultra-short TR and TE ¹ H FID MRSI sequence at 9.4T.

METHODS

X-t sparse compressed sensing reconstruction was optimized for nonlipid suppressed ¹ H FID MRSI data. Coil-by-coil x-t sparse reconstruction was compared with SENSE x-t sparse and low rank reconstruction. The effect of matrix size and spatial resolution on the achievable acceleration factor was studied. Finally, in vivo metabolite maps with different acceleration factors of 2, 4, 5, and 10 were acquired and compared.

RESULTS

Coil-by-coil x-t sparse compressed sensing reconstruction was not able to reliably recover the nonlipid suppressed data, rather a combination of parallel and sparse reconstruction was necessary (SENSE x-t sparse). For acceleration factors of up to 5, both the low-rank and the compressed sensing methods were able to reconstruct the data comparably well (root mean squared errors [RMSEs] ≤ 10.5% for Cre). However, the reconstruction time of the low rank algorithm was drastically longer than compressed sensing. Using the optimized compressed sensing reconstruction, acceleration factors of 4 or 5 could be reached for the MRSI data with a matrix size of 64 × 64. For lower spatial resolutions, an acceleration factor of up to R∼4 was successfully achieved.

CONCLUSION

By tailoring the reconstruction scheme to the nonlipid suppressed data through parameter optimization and performance evaluation, we present high resolution (97 µL voxel size) accelerated in vivo metabolite maps of the human brain acquired at 9.4T within scan times of 3 to 3.75 min.

Localized 2D COSY sequences: Method and experimental evaluation for a whole metabolite quantification approach

Two-dimensional spectroscopy offers the possibility to unambiguously distinguish metabolites by spreading out the multiplet structure of J-coupled spin systems into a second dimension. Quantification methods that perform parametric fitting of the 2D MRS signal have recently been proposed for resolved PRESS (JPRESS) but not explicitly for Localized Correlation Spectroscopy (LCOSY). Here, through a whole metabolite quantification approach, correlation spectroscopy quantification performances are studied. The ability to quantify metabolite relaxation constant times is studied for three localized 2D MRS sequences (LCOSY, LCTCOSY and the JPRESS) in vitro on preclinical MR systems. The issues encountered during implementation and quantification strategies are discussed with the help of the Fisher matrix formalism. The described parameterized models enable the computation of the lower bound for error variance--generally known as the Cramér Rao bounds (CRBs), a standard of precision--on the parameters estimated from these 2D MRS signal fittings. LCOSY has a theoretical net signal loss of two per unit of acquisition time compared to JPRESS. A rapid analysis could point that the relative CRBs of LCOSY compared to JPRESS (expressed as a percentage of the concentration values) should be doubled but we show that this is not necessarily true. Finally, the LCOSY quantification procedure has been applied on data acquired in vivo on a mouse brain.