Effect of linewidth on estimation of metabolic concentration when using water lineshape spectral model fitting for single voxel proton spectroscopy at 7 T

B0-insensitive image navigators for prospective motion-corrected MRS with localized second-order shimming

PURPOSE

Localized shimming in single-voxel MRS often results in large B0 inhomogeneity outside the volume-of-interest. This causes unacceptable degradation in motion navigator images. Switching back and forth between whole-brain shim and localized shim is possible for linear shims, but not for higher-order shims. Here we propose motion navigators largely insensitive to B0 inhomogeneity for prospective motion-corrected MRS with localized higher-order shimming.

METHODS

A recent fast high-resolution motion navigator based on spiral-in/out k-space trajectories and multislice-to-volume registration was modified by splitting the readout into multiple shot interleaves which shortened the echo time and reduced the effect of B0 inhomogeneity. The performance of motion correction was assessed in healthy subjects in the prefrontal cortex using a sLASER sequence at 3T (N = 5) and 7T (N = 5).

RESULTS

With multiple spatial interleaves, excellent quality navigator images were acquired in the whole brain in spite of large B0 inhomogeneity outside the MRS voxel. The total duration of the navigator in sLASER remained relatively short even with multiple shots (3T: 10 spatial interleaves 94 ms per slice; 7T: 15 spatial interleaves 103 ms per slice). Prospective motion correction using the multi-shot navigators yielded comparable spectral quality (water linewidth and metabolite SNR) with and without subject motion.

CONCLUSION

B0-insensitive motion navigators enable prospective motion correction for MRS with all first- and second-order shims adjusted in the MRS voxel, providing optimal spectral linewidth.

Linewidth-related bias in modelled concentration estimates from GABA-edited 1H-MRS

J-difference-edited MRS is widely used to study GABA in the human brain. Editing for low-concentration target molecules (such as GABA) typically exhibits lower signal-to-noise ratio (SNR) than conventional non-edited MRS, varying with acquisition region, volume and duration. Moreover, spectral lineshape may be influenced by age-, pathology-, or brain-region-specific effects of metabolite T2, or by task-related blood-oxygen level dependent (BOLD) changes in functional MRS contexts. Differences in both SNR and lineshape may have systematic effects on concentration estimates derived from spectral modelling. The present study characterises the impact of lineshape and SNR on GABA+ estimates from different modelling algorithms: FSL-MRS, Gannet, LCModel, Osprey, spant and Tarquin. Publicly available multi-site GABA-edited data (222 healthy subjects from 20 sites; conventional MEGA-PRESS editing; TE = 68 ms) were pre-processed with a standardised pipeline, then filtered to apply controlled levels of Lorentzian and Gaussian linebroadening and SNR reduction. Increased Lorentzian linewidth was associated with a 2-5% decrease in GABA+ estimates per Hz, observed consistently (albeit to varying degrees) across datasets and most algorithms. Weaker, often opposing effects were observed for Gaussian linebroadening. Variations are likely caused by differing baseline parametrization and lineshape constraints between models. Effects of linewidth on other metabolites (e.g., Glx and tCr) varied, suggesting that a linewidth confound may persist after scaling to an internal reference. These findings indicate a potentially significant confound for studies where linewidth may differ systematically between groups or experimental conditions, e.g. due to T2 differences between brain regions, age, or pathology, or varying T2* due to BOLD-related changes. We conclude that linewidth effects need to be rigorously considered during experimental design and data processing, for example by incorporating linewidth into statistical analysis of modelling outcomes or development of appropriate lineshape matching algorithms.

Acquisition and quantification pipeline for in vivo hyperpolarized 13 C MR spectroscopy

PURPOSE

The goal of this study was to combine a specialized acquisition method with a new quantification pipeline to accurately and efficiently probe the metabolism of hyperpolarized ¹³ C-labeled compounds in vivo. In this study, we tested our approach on [2-¹³ C]pyruvate and [1-¹³ C]α-ketoglutarate data in rat orthotopic brain tumor models at 3T.

METHODS

We used a multiband metabolite-specific radiofrequency (RF) excitation in combination with a variable flip angle scheme to minimize substrate polarization loss and measure fast metabolic processes. We then applied spectral-temporal denoising using singular value decomposition to enhance spectral quality. This was combined with LCModel-based automatic ¹³ C spectral fitting and flip angle correction to separate overlapping signals and rapidly quantify the different metabolites.

RESULTS

Denoising improved the metabolite signal-to-noise ratio (SNR) by approximately 5. It also improved the accuracy of metabolite quantification as evidenced by a significant reduction of the Cramer Rao lower bounds. Furthermore, the use of the automated and user-independent LCModel-based quantification approach could be performed rapidly, with the kinetic quantification of eight metabolite peaks in a 12-spectrum array achieved in less than 1 minute.

CONCLUSION

The specialized acquisition method combined with denoising and a new quantification pipeline using LCModel for the first time for hyperpolarized ¹³ C data enhanced our ability to monitor the metabolism of [2-¹³ C]pyruvate and [1-¹³ C]α-ketoglutarate in rat orthotopic brain tumor models in vivo. This approach could be broadly applicable to other hyperpolarized agents both preclinically and in the clinical setting.