Optimizing acquisition and fitting conditions for 1 H MR spectroscopy investigations in global brain pathology.

PURPOSE: To optimize acquisition and fitting conditions for nonfocal disease in terms of voxel size and use of individual coil element data. Increasing the voxel size yields a higher signal-to-noise ratio, but leads to larger linewidths and more artifacts. Several ways to improve the spectral quality for large voxels are exploited and the optimal use of individual coil signals investigated. METHODS: Ten human subjects were measured at 3 T using a 64-channel receive head coil with a semi-LASER localization sequence under optimized and deliberately mis-set field homogeneity. Eight different voxel sizes (8 to 99 cm3 ) were probed. Spectra were fitted either as weighted sums of the individual coil elements or simultaneously without summation. Eighteen metabolites were included in the fit model that also included the lineshapes from all coil elements as reflected in water reference data. Fitting errors for creatine, myo-Inositol and glutamate are reported as representative parameters to judge optimal acquisition and evaluation conditions. RESULTS: Minimal Cramér-Rao lower bounds and thus optimal acquisition conditions were found for a voxel size of ~ 70 cm3 for the representative upfield metabolites. Spectral quality in terms of lineshape and artifact appearance was determined to differ substantially between coil elements. Simultaneous fitting of spectra from individual coil elements instead of traditional fitting of a weighted sum spectrum reduced Cramer-Rao lower bounds by up to 17% for large voxel sizes. CONCLUSION: The optimal voxel size for best precision in determined metabolite content is surprisingly large. Such an acquisition condition is most relevant for detection of low-concentration metabolites, like NAD+ or phenylalanine, but also for longitudinal studies where very small alterations in metabolite content are targeted. In addition, simultaneous fitting of single channel spectra enforcing lineshape and coil sensitivity information proved to be superior to traditional signal combination with subsequent fitting.

Diffusion-weighted magnetic resonance spectroscopy boosted by simultaneously acquired water reference signals.

PURPOSE: To combine the metabolite-cycling technique with diffusion-weighted 1 H-MR spectroscopy and to use the inherent water reference for compensation of motion-related signal loss for improved estimation of metabolite apparent diffusion coefficients (ADCs). METHODS: Diffusion-weighted spectra of water and metabolites were acquired simultaneously using metabolite-cycling at 3 T. The water information was used for signal correction of phase, frequency, and eddy currents, as well as for compensation of motion-induced signal loss. ADCs were estimated by 2D simultaneous fitting. The quality of ADC restoration was investigated in vitro. Subsequently, the new approach was applied in 13 subjects for enhanced metabolite ADC estimation in gray matter. RESULTS: Metabolite-cycled diffusion 1 H-MRS is suitable to measure metabolite and water ADCs simultaneously. The water reference facilitates signal amplitude restoration, compensating for motion-related artefacts. 2D fitting stabilizes the fitting procedure and allows the estimation of ADCs even for low signal-to-noise metabolites. Use of the motion-compensation scheme leads to estimation of smaller ADCs for virtually all metabolites (44% smaller ADC on average), to a reduction of fitting uncertainties for metabolite ADCs in individual subjects and reduced variance over the cohort (45% smaller SD on average). CONCLUSION: Using the simultaneously acquired water signal as internal reference allows not only for compensation of phase and frequency fluctuations but also for signal amplitude restoration, and thus improved metabolite ADC estimation. Combination with 2D simultaneous fitting promises access to the diffusion properties even for low signal-to-noise metabolites. The combination of both techniques increases the specificity and sensitivity of estimated metabolite ADC values in the cohort.

Fitting interrelated datasets: metabolite diffusion and general lineshapes.

OBJECTIVE: Simultaneous modeling of true 2-D spectroscopy data, or more generally, interrelated spectral datasets has been described previously and is useful for quantitative magnetic resonance spectroscopy applications. In this study, a combined method of reference-lineshape enhanced model fitting and two-dimensional prior-knowledge fitting for the case of diffusion weighted MR spectroscopy is presented. MATERIALS AND METHODS: Time-dependent field distortions determined from a water reference are applied to the spectral bases used in linear-combination modeling of interrelated spectra. This was implemented together with a simultaneous spectral and diffusion model fitting in the previously described Fitting Tool for Arrays of Interrelated Datasets (FiTAID), where prior knowledge conditions and restraints can be enforced in two dimensions. RESULTS: The benefit in terms of increased accuracy and precision of parameters is illustrated with examples from Monte Carlo simulations, in vitro and in vivo human brain scans for one- and two-dimensional datasets from 2-D separation, inversion recovery and diffusion-weighted spectroscopy (DWS). For DWS, it was found that acquisitions could be substantially shortened. CONCLUSION: It is shown that inclusion of a measured lineshape into modeling of interrelated MR spectra is beneficial and can be combined also with simultaneous spectral and diffusion modeling.