A new sequence combining chemical-exchange saturation-transfer (CEST) with traditional MRS is used to simultaneously determine metabolite content and effects of magnetization exchange.
A CEST ...saturation block consisting of a train of RF-pulses is placed before a metabolite-cycled semi-LASER single-voxel spectroscopy sequence. The saturation parameters are adjustable to allow optimization of the saturation for a specific target. Data were collected in brain from 20 subjects in experiments with different B
-settings (0.4-2.0 µT) on a 3T MR scanner. CEST Z-spectra were calculated from water intensities and fitted with a multi-pool Lorentzian model. Interrelated metabolite spectra were fitted in fitting tool for arrays of interrelated datasets (FiTAID).
Evaluation of traditional Z-spectra from water revealed exchange effects from amides, amines, and hydroxyls as well as an upfield nuclear Overhauser effect. The magnetization transfer effect was evaluated on metabolites and macromolecules for the whole spectral range and for the different B
levels. A correction scheme for direct saturation on metabolites is proposed. Both magnetization-transfer and direct saturation proved to differ for individual metabolites.
Using non-water-suppressed spectroscopy offers time-saving simultaneous recording of the traditional CEST Z-spectrum from water and the metabolite spectrum under frequency-selective saturation. In addition, exchange and magnetization-transfer effects on metabolites and macromolecules can be detected, which might offer additional possibilities for quantification or give further insight into the composition of the traditional CEST Z-spectrum. Apparent magnetization-transfer effects on macromolecular signals in the
H-MR spectrum have been found. Detailed knowledge of magnetization-transfer effects is also relevant for judging the influence of water-suppression on the quantification of metabolite signals.
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.
The optimal voxel size for detection of low‐concentration metabolites, or for lowest fit error in longitudinal studies, was found to be surprisingly large at ~ 70 cm3. Signals from individual coil elements in multi‐coil arrays differed highly in spectral quality, proving individual scrutinizing and simultaneous fitting of single channel spectra enforcing lineshape and coil sensitivity information was superior to traditional signal combination with subsequent fitting.
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.
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 cm
) 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.
Minimal Cramér-Rao lower bounds and thus optimal acquisition conditions were found for a voxel size of ~ 70 cm
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.
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.
The detection of nicotinamide-adenine-dinucleotide (NAD
) is challenging using standard
H MR spectroscopy, because it is of low concentration and affected by polarization-exchange with water. ...Therefore, this study compares three techniques to access NAD
quantification at 3 T-one with and two without water presaturation.
A large brain volume in 10 healthy subjects was investigated with three techniques: semi-LASER with water-saturation (WS) (TE = 35 ms), semi-LASER with metabolite-cycling (MC) (TE = 35 ms), and the non-water-excitation (nWE) technique 2D ISIS-localization with chemical-shift-selective excitation (2D I-CSE) (TE = 10.2 ms). Spectra were quantified with optimized modeling in FiTAID.
NAD
could be well quantified in cohort-average spectra with all techniques. Obtained apparent NAD
tissue contents are all lower than expected from literature confirming restricted visibility by
H MRS. The estimated value from WS-MRS (58 μM) was considerably lower than those obtained with non-WS techniques (146 μM for MC-semi-LASER and 125 μM for 2D I-CSE). The nWE technique with shortest TE gave largest NAD
signals but suffered from overlap with large amide signals. MC-semi-LASER yielded best estimation precision as reflected in relative Cramer-Rao bounds (14%, 21 μM/146 μM) and also best robustness as judged by the coefficient-of-variance over the cohort (11%, 10 μM/146 μM). The MR-visibility turned out as 16% with WS and 41% with MC.
Three methods to assess NAD
in human brain at 3 T have been compared. NAD
could be detected with a visibility of ∼41% for the MC method. This may open a new window for the observation of pathological changes in the clinical research setting.
Purpose
The detection of nicotinamide‐adenine‐dinucleotide (NAD+) is challenging using standard 1H MR spectroscopy, because it is of low concentration and affected by polarization‐exchange with ...water. Therefore, this study compares three techniques to access NAD+ quantification at 3 T–one with and two without water presaturation.
Methods
A large brain volume in 10 healthy subjects was investigated with three techniques: semi‐LASER with water‐saturation (WS) (TE = 35 ms), semi‐LASER with metabolite‐cycling (MC) (TE = 35 ms), and the non‐water‐excitation (nWE) technique 2D ISIS‐localization with chemical‐shift‐selective excitation (2D I‐CSE) (TE = 10.2 ms). Spectra were quantified with optimized modeling in FiTAID.
Results
NAD+ could be well quantified in cohort‐average spectra with all techniques. Obtained apparent NAD+ tissue contents are all lower than expected from literature confirming restricted visibility by 1H MRS. The estimated value from WS‐MRS (58 μM) was considerably lower than those obtained with non‐WS techniques (146 μM for MC‐semi‐LASER and 125 μM for 2D I‐CSE). The nWE technique with shortest TE gave largest NAD+ signals but suffered from overlap with large amide signals. MC‐semi‐LASER yielded best estimation precision as reflected in relative Cramer‐Rao bounds (14%, 21 μM/146 μM) and also best robustness as judged by the coefficient‐of‐variance over the cohort (11%, 10 μM/146 μM). The MR‐visibility turned out as 16% with WS and 41% with MC.
Conclusion
Three methods to assess NAD+ in human brain at 3 T have been compared. NAD+ could be detected with a visibility of ∼41% for the MC method. This may open a new window for the observation of pathological changes in the clinical research setting.
Macromolecular signals are crucial constituents of short echo‐time 1H MR spectra with potential clinical implications in themselves as well as essential ramifications for the quantification of the ...usually targeted metabolites. Their parameterization, needed for general fitting models, is difficult because of their unknown composition. Here, a macromolecular signal parameterization together with metabolite signal quantification including relaxation properties is investigated by multidimensional modeling of interrelated 2DJ inversion‐recovery (2DJ‐IR) datasets. Simultaneous and iterative procedures for defining the macromolecular background (MMBG) as mono‐exponentially or generally decaying signals over TE are evaluated. Varying prior knowledge and restrictions in the metabolite evaluation are tested to examine their impact on results and fitting stability for two sets of three‐dimensional spectra acquired with metabolite‐cycled PRESS from cerebral gray and white matter locations. One dataset was used for model optimization, and also examining the influence of prior knowledge on estimated parameters. The most promising model was applied to a second dataset. It turned out that the mono‐exponential decay model appears to be inadequate to represent TE‐dependent signal features of the MMBG. TE‐adapted MMBG spectra were therefore determined. For a reliable overall quantification of implicated metabolite concentrations and relaxation times, a general fitting model had to be constrained in terms of the number of fitting variables and the allowed parameter space. With such a model in place, fitting precision for metabolite contents and relaxation times was excellent, while fitting accuracy is difficult to judge and bias was likely influenced by the type of fitting constraints enforced. In summary, the parameterization of metabolite and macromolecule contributions in interrelated MR spectra has been examined by using multidimensional modeling on complex 2DJ‐IR datasets. A tightly restricted model allows fitting of individual subject data with high fitting precision documented in small Cramér‐Rao lower bounds, good repeatability values and a relatively small spread of estimated concentration and relaxation values for a healthy subject cohort.
Complex 2DJ‐IR datasets have been examined using multidimensional fitting in FiTAID for the parameterization of metabolite and macromolecule contributions. TE‐specific models deviating from mono‐exponential decay for the macromolecules were tested, showing little impact on metabolite quantification. A tightly restricted model allows fitting of individual subject data with high fitting precision documented in small Cramér‐Rao lower bounds, good repeatability values and a relatively small spread of estimated concentration and relaxation values for a healthy subject cohort.
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