Stochastic excitation scheme for estimating longitudinal relaxation and radiofrequency transmit inhomogeneity in single voxel spectroscopy
Assaf Tal1

1Chemical Physics, Weizmann Institute of Science, Rehovot, Israel

Synopsis

A stochastic excitation and corresponding dictionary matching scheme is presented for quantifying metabolite concentrations, longitudinal relaxation times and transmit inhomogeneity in single voxel proton magnetic resonance spectroscopy in the human brain.

Introduction

The recently suggested approach of magnetic resonance fingering (MRF) relies on matching the response of a system undergoing semi-random excitations to a dictionary of pre-simulated signal curves with fixed parameters1 (T1, T2, proton density PD, transmit inhomogeneity B1+). The closest matching dictionary entry is used to estimate the system’s parameters.

The application of MRF to magnetic resonance spectroscopy (MRS) requires careful planning. A full MRS spectrum contains over a dozen metabolites; even with only a single spin-1/2 system with three unknown parameters (T1, T2¸ B1+), a typical dictionary contains hundreds of thousands of entries. Furthermore, most metabolites are described by complex Hamiltonians which are significantly more time consuming to simulate than a single spin-1/2 system. The macromolecular baseline is also difficult to model and is often fitted non-parametrically. Acquisitions cannot be made arbitrarily short, as at least ~ 0.5-1.0 seconds are required for water suppression and the sampling of the free induction decay. Finally, the very low signal to noise ratio (SNR) of MRS makes temporal fitting extremely unreliable.

A fingerprinting scheme to accurately quantify the T1, PD and B1+ of all metabolites’ is presented. To overcome the low SNR, we averaged subsets of measurements together; this allowed for fitting each averaged subset to pre-simulated basis sets using conventional fitting algorithms. Varying the repetition time TR and the excitation angle a introduced T1 and B1+ weighting. Standard match and pursuit algorithms were used to fit averaged signal curves to average dictionary entries.

Methods

Data acquisition: A single 1.5×1.5×2 cm3 voxel was placed in the white matter of a healthy volunteer (Fig. 1a) at 3T (Tim Trio, Siemens) and a PRESS sequence was acquired with 120 averages. TE was fixed at 35 ms. TR and α, the flip angle of the first PRESS pulse, were varied per-scan as shown in Fig. 1b, 1c. A moving Navg=8 scan average was applied to the data, i.e. the ith scan was replaced by the sum of scans {i, i+1, ..., i-1+Navg} to increase SNR. The N-acetyl-aspartate (NAA), choline (Cho) and creatine (Cr) peaks were fitted with VESPA6. A correction (based on simulations of the Bloch equations) was applied to each scan's intensity, since varying the flip angle α distorts the excitation pulse's profile slightly. A non-suppressed water acquisition was acquired for quantification.

Dictionary generation and entry matching: Only singlets were simulated for this preliminary study. Echo intensities for each scan were computed by solving the Bloch equations, and were summed with the same moving average. Prior to matching all dictionary entries and experimental data had their initial point normalized to unity. Dictionary matching was achieved with a standard match & pursuit algorithm2.

Absolute Quantification: Following matching and determination of T1, B1+, the dictionary entry was rescaled to fit the experimental time series to extract its PD. The relative intensity of each dictionary curve, compared to a standard full excitation with TR=2000 ms, α=90°, was also calculated from simulation and used to perform absolute quantification using the reference water scan using literature values7 of T2.

"Gold Standard": PD was estimated using a PRESS with TR/TE=2000/35, 90 averages. T1 was estimated using standard inversion recovery with 5 inversion points. B1+ was estimated using a saturated double angle method3. To ensure fair comparison, total time of all sequences was kept equal to the stochastic excitation sequence.

Results

Fig 2a shows a sample 8-scan average spectrum. Fig. 2b shows the averaged NAA level as a function of scan number after averaging (blue) and the closest matching dictionary entry (red). Fitted concentrations, T1 and B1+ values are summarized in Fig. 3. White matter concentrations and T1 values agree with the literature4,5. The conventional and MRF approaches differ by about 10%-20%. It is difficult to ascertain whether one method is superior; phantom experiments (not shown) have exhibited smaller discrepancy levels ~5%. A good consistency check is afforded by B1+, which shows excellent agreement between all metabolites using MRF.

Discussion

The principles off fingerprinting can be successfully adapted to single voxel spectroscopy if appropriate modifications to the basic scheme are made:scans must be averaged, fit and only then matched to corresponding dictionary entries.The number of scans in the moving average should be optimized: on the one hand, increasing Navg increases the SNR and reduces fitting error; on the other hand, it diminishes the signal variability introduced by the stochastic excitation and, consequently, its dependence on T1 and B1+. Note that T2 was not estimated since the long TRs used in MRS greatly diminish transverse coherences and the signal's dependence on T2.

Acknowledgements

Assaf Tal acknowledges the support of the Monroy-Marks Career Development Fund, the Carolito Stiftung Fund, the Leona M. and Harry B. Helmsley Charitable Trust and the historic generosity of the Harold Perlman Family.

References

[1] Ma D, Gulani V, Seiberlich N, Liu K, Sunshine JL, Duerk JL, Griswold MA. Magnetic resonance fingerprinting. Nature 2013;495(7440):187-192.

[2] Tropp JA, Gilbert AC. Signal recovery from random measurements via orthogonal matching pursuit. Ieee Transactions on Information Theory 2007;53(12):4655-4666.

[3] Cunningham CH, Pauly JM, Nayak KS, Saturated double-angle method for rapid B1+ mapping, Magn Reson Med 2006; 55(6):1326-1333

[4] Tal A, Kirov II, Grossman RI, Gonen O, The role of gray and white matter segmentation in quantitative proton MR spectroscopic imaging, NMR Biomed, 2012; 25(12):1392-1400

[5] Mlynarik V, Gruber S, Moser E, Proton T1 and T2 relaxation times of human brain metabolites at 3 Tesla, NMR Biomed, 2001; 14(5):325-331

[6] Soher BJ, Semanchuk P, Todd D, Steinberg J, Young K. VeSPA: integrated applications for RF pulse design, spectral simulation and MRS data analysis. 2011; Montreal, Canada. p 1410.

[7] Kirov II, Fleysher L, Fleysher R, Patil V, Liu S, Gonen O, Age dependence of regional proton metabolites T2 relaxation times in the human brain at 3 T, Magn Reson Med 2008; 60(4):790-795

Figures

(a.) MRS voxel placement. (b.) Flip angle as a function of scan number. (c.) TR as a function of scan number (minimum TR dictated by 240 ms water suppression module, 256 ms acquisition at 1 kHz bandwidth, echo time and spoiling gradients).

(a.) Average spectrum obtained by summing up 8 scans. (b.) Plot of NAA amplitude for experimental data (blue) and closest dictionary entry (red).

Concentrations, T1 and B1+ values extracted from both the stochastic MRF scheme and the "conventional" acquisitions (PRESS for concentrations, inversion recovery to T1, double flip angle for B1+). B1+ is indeed very similar for all metabolites.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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