Magnetic resonance imaging and spectroscopy is a powerful tool for non-invasively assessing anatomic and metabolic changes that occur in brain diseases. In clinical spectroscopic studies and especially for magnetic resonance spectroscopic imaging (MRSI) data acquisition, short repetition times (TR) are often required to meet scan time constraints, but accurate metabolite longitudinal relaxation time values (T1) are then needed to correct the metabolite concentrations for the T1-weighted effect. The metabolite T1 values are likely to be important for quantifying results that make comparisons between patients and normal controls. Moreover, the knowledge of 1H metabolite longitudinal relaxation times can by itself give insight into the properties of a given region of interest.
In many previous studies, the estimations of the metabolite T1
s were performed using single voxel acquisitions. Short echo time spectra coming from either progressive saturation [1
] or inversion recovery experiments [4
] were collected and T1
values were usually derived from amono-exponential fit. The inversion recovery experiments typically used long repetition times (TRs are usually equal to 6 s) with varying inversion times [4
], which is prohibitively long for MRSI experiments. These single voxel approaches assume a single T1
over the whole voxel regardless of its tissue composition. To obtain white matter or gray matter T1
values and good SNR, large (usually greater than or equal to 8 cc), and relatively heterogeneous single voxels were typically acquired. In most cases, gray matter (GM)-T1
results were obtained from voxels containing 60–70% of GM, while white matter were obtained from voxels containing 70–90% of WM. At the same time, different MRSI studies [7
] using linear regression demonstrated how metabolite concentrations (and thus metabolite signal intensities) can be different according to their tissue origin. Thus a common concern about the single voxel studies is whether the content of GM and WM in the examined voxel has an influence on the metabolite T1
results. Moreover attempting to reduce the size of the single spectroscopic voxel to reduce the voxel tissue heterogeneity would result in increasing the number of averages and the scan time. In contrast, MRSI techniques offer the possibility to acquire simultaneously several spectra over a wide brain region and at a resolution allowing tissue analysis. The first goal of this paper was, therefore, to develop a MRSI post-processing method to estimatemetabolite T1
s while accounting for the voxels’ tissue content. To date, no published studies investigated the use of MRSI data to estimate metabolite T1
Then, as for any quantitative measurement based on model fitting, an assessment of the precision of the T1 estimation is desirable. A benefit of MRSI is that it provides several spectra and thus several data points for the metabolite T1 fit which can be resampled in a bootstrap manner to estimate standard error. Therefore, a second goal of this study was to develop an approach to obtain metabolite T1 standard error by bootstrapping.
The last contribution of the paper was to apply the new techniques to measure metabolite T1s in different regions and tissues of the brains of healthy subjects.
The proposed method estimates metabolite T1
relaxation times by using 2D MRSI data at different repetition times. The progressive saturation method was chosen instead of inversion recovery method for scan time concern. While conventional techniques spend time in averaging single voxel acquisitions to obtain good SNR, we use this time to acquire multi-voxel data and investigate regional and tissue specific metabolite T1
differences. The post-processing takes advantage of the combination of segmented MRI and spatially distributed spectroscopic data to investigate either WM versus GM metabolite T1
values and/or regional differences in longitudinal relaxation times. The proposed method relies on three major concepts:
- Increasing the SNR by averaging voxels according to their WM/GM content and their location (for example anterior vs. posterior) since the SNR of the metabolite signals is very low using the proposed acquisition parameters (number of excitations (NEX) and number of voxels in the slice) at 1.5 T.
- Estimating metabolite T1 for gray and white matter using a non-linear least squares algorithm. The underlying model function used in the fitting procedure associates WM/GM content of a voxel to the metabolite signal intensity.
- Using a bootstrap technique to assess uncertainty on the metabolite T1s and taking into account this confidence when calculating a group mean value.
This paper presents the techniques developed to utilize MRSI data for metabolite T1 measurement. A validation of these techniques is then proposed through Monte Carlo data simulations, demonstrating the statistical performance of the proposed method. Finally the fitting procedure is applied to 2D conventional MRSI data acquired at 1.5 T from eight healthy subjects.