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.

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.

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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.

Despite increasing knowledge about the effects of phenylketonuria on brain structure and function, it is uncertain whether white matter microstructure is affected and if it is linked to patients’ metabolic control or cognitive performance. Thus, we quantitatively assessed white matter characteristics in adults with phenylketonuria and assessed their relationship to concurrent brain and blood phenylalanine levels, historical metabolic control, and cognitive performance. Diffusion tensor imaging and 1H spectroscopy were performed in 30 adults with early-treated classical phenylketonuria (median 35.5 years) and 54 healthy controls (median 29.3 years). Fractional anisotropy and mean, axial, and radial diffusivity were investigated using tract-based spatial statistics, and white matter lesion load was evaluated. Brain phenylalanine levels were measured with 1H spectroscopy whereas concurrent plasma phenylalanine levels were assessed after an overnight fast. Retrospective phenylalanine levels were collected to estimate historical metabolic control and a neuropsychological evaluation assessed performance in executive functions, attention, and processing speed. Widespread reductions in mean diffusivity, axial diffusivity, and fractional anisotropy occurred in patients compared to controls. Mean diffusivity and axial diffusivity were decreased in several white matter tracts and were most restricted in the optic radiation (effect size rrb = 0.66 to 0.78, P < 0.001) and posterior corona radiata (rrb = 0.83 to 0.90, P < 0.001). Lower fractional anisotropy was found in the optic radiation and posterior corona radiata (rrb = 0.43 to 0.49, P < 0.001). White matter microstructure in patients was significantly associated with cognition. Specifically, inhibition was related to axial diffusivity in the external capsule (rs = −0.69, P < 0.001) and the superior (rs = −0.58, P < 0.001) and inferior longitudinal fasciculus (rs = −0.60, P < 0.001). Cognitive flexibility was associated with mean diffusivity of the posterior limb of the internal capsule (rs = −0.62, P < 0.001), and divided attention correlated with fractional anisotropy of the external capsule (rs = −0.61, P < 0.001). Neither concurrent nor historical metabolic control was significantly associated with white matter microstructure. White matter lesions were present in 29 out of 30 patients (96.7%), most often in the parietal and occipital lobes. However, total white matter lesion scores were unrelated to patients’ cognitive performance and metabolic control. In conclusion, our findings demonstrate that white matter alterations in early-treated phenylketonuria persist into adulthood, are most prominent in the posterior white matter, and are likely to be driven by axonal damage. Furthermore, diffusion tensor imaging metrics in adults with phenylketonuria were related to performance in attention and executive functions.