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Purpose: The interest in applying and modeling dynamic MRS has recently grown. Two‐dimensional modeling yields advantages for the precision of metabolite estimation in interrelated MRS data. However, it is unknown whether including all transients simultaneously in a 2D model without averaging (presuming a stable signal) performs similarly to one‐dimensional (1D) modeling of the averaged spectrum. Therefore, we systematically investigated the accuracy, precision, and uncertainty estimation of both described model approaches. Methods: Monte Carlo simulations of synthetic MRS data were used to compare the accuracy and uncertainty estimation of simultaneous 2D multitransient linear‐combination modeling (LCM) with 1D‐LCM of the average. A total of 2,500 data sets per condition with different noise representations of a 64‐transient MRS experiment at six signal‐to‐noise levels for two separate spin systems (scyllo‐inositol and gamma‐aminobutyric acid) were analyzed. Additional data sets with different levels of noise correlation were also analyzed. Modeling accuracy was assessed by determining the relative bias of the estimated amplitudes against the ground truth, and modeling precision was determined by SDs and Cramér‐Rao lower bounds (CRLBs). Results: Amplitude estimates for 1D‐ and 2D‐LCM agreed well and showed a similar level of bias compared with the ground truth. Estimated CRLBs agreed well between both models and with ground‐truth CRLBs. For correlated noise, the estimated CRLBs increased with the correlation strength for the 1D‐LCM but remained stable for the 2D‐LCM. Conclusion: Our results indicate that the model performance of 2D multitransient LCM is similar to averaged 1D‐LCM. This validation on a simplified scenario serves as a necessary basis for further applications of 2D modeling. [ABSTRACT FROM AUTHOR]

Temperature is a hallmark parameter influencing almost all magnetic resonance properties (e.g., T1, T2, proton density, and diffusion). In the preclinical setting, temperature has a large influence on animal physiology (e.g., respiration rate, heart rate, metabolism, and oxidative stress) and needs to be carefully regulated, especially when the animal is under anesthesia and thermoregulation is disrupted. We present an open‐source heating and cooling system capable of regulating the temperature of the animal. The system was designed using Peltier modules capable of heating or cooling a circulating water bath with active temperature feedback. Feedback was obtained using a commercial thermistor, placed in the animal rectum, and a proportional‐integral‐derivative controller was used to modulate the temperature. Its operation was demonstrated in a phantom as well as in mouse and rat animal models, where the standard deviation of the temperature of the animal upon convergence was less than a 10th of a degree. An application where brain temperature of a mouse was modulated was demonstrated using an invasive optical probe and noninvasive magnetic resonance spectroscopic thermometry measurements. [ABSTRACT FROM AUTHOR]

Purpose: Many MRS paradigms produce 2D spectral‐temporal datasets, including diffusion‐weighted, functional, and hyperpolarized and enriched (carbon‐13, deuterium) experiments. Conventionally, temporal parameters—such as T2, T1, or diffusion constants—are assessed by first fitting each spectrum independently and subsequently fitting a temporal model (1D fitting). We investigated whether simultaneously fitting the entire dataset using a single spectral‐temporal model (2D fitting) would improve the precision of the relevant temporal parameter. Methods: We derived a Cramer Rao lower bound for the temporal parameters for both 1D and 2D approaches for 2 experiments: a multi‐echo experiment designed to estimate metabolite T2s, and a functional MRS experiment designed to estimate fractional change (δ$$ \delta $$) in metabolite concentrations. We investigated the dependence of the relative standard deviation (SD) of T2 in multi‐echo and δ$$ \delta $$ in functional MRS. Results: When peaks were spectrally distant, 2D fitting improved precision by approximately 20% relative to 1D fitting, regardless of the experiment and other parameter values. These gains increased exponentially as peaks drew closer. Dependence on temporal model parameters was weak to negligible. Conclusion: Our results strongly support a 2D approach to MRS fitting where applicable, and particularly in nuclei such as hydrogen and deuterium, which exhibit substantial spectral overlap. [ABSTRACT FROM AUTHOR]

Clinical magnetic resonance spectroscopy (MRS) mainly concerns itself with the quantification of metabolite concentrations. Metabolite relaxation values, which reflect the microscopic state of specific cellular and sub‐cellular environments, could potentially hold additional valuable information, but are rarely acquired within clinical scan times. By varying the flip angle, repetition time and echo time in a preset way (termed a schedule), and matching the resulting signals to a pre‐generated dictionary – an approach dubbed magnetic resonance fingerprinting – it is possible to encode the spins' relaxation times into the acquired signal, simultaneously quantifying multiple tissue parameters for each metabolite. Herein, we optimized the schedule to minimize the averaged root mean square error (RMSE) across all estimated parameters: concentrations, longitudinal and transverse relaxation time, and transmitter inhomogeneity. The optimal schedules were validated in phantoms and, subsequently, in a cohort of healthy volunteers, in a 4.5 mL parietal white matter single voxel and an acquisition time under 5 minutes. The average intra‐subject, inter‐scan coefficients of variation (CVs) for metabolite concentrations, T1 and T2 relaxation times were found to be 3.4%, 4.6% and 4.7% in‐vivo, respectively, averaged over all major singlets. Coupled metabolites were quantified using the short echo time schedule entries and spectral fitting, and reliable estimates of glutamate+glutamine, glutathione and myo‐inositol were obtained. [ABSTRACT FROM AUTHOR]

B0 field maps are used ubiquitously in neuroimaging, in disciplines ranging from magnetic resonance spectroscopy to temperature mapping and susceptibility‐weighted imaging. Most B0 maps are acquired using standard gradient‐echo–based vendor‐provided sequences, often comprised of two echoes spaced a few milliseconds apart. Herein, we analyze the optimal spacing of echo times, defined as those maximizing precision—minimizing the standard deviation—for a fixed total acquisition time. Field estimation is carried out using a weighted least squares estimator. The standard deviation is shown to be approximately inversely proportional to the total acquisition time, suggesting a law of diminishing returns, whereby substantial gains are obtained up to a certain point, with little improvement beyond that point. Validations are provided in a phantom and a group of volunteers. Multi‐gradient echo sequences are readily available on all manufacturer platforms, making our recommendations straightforward to implement on any modern scanner. [ABSTRACT FROM AUTHOR]

Purpose: Unlike conventional MR spectroscopy (MRS), which only measures metabolite concentrations, multiparametric MRS also quantifies their longitudinal (T1) and transverse (T2) relaxation times, as well as the radiofrequency transmitter inhomogeneity (B1+). To test whether knowledge of these additional parameters can improve the clinical utility of brain MRS, we compare the conventional and multiparametric approaches in terms of expected classification accuracy in differentiating controls from patients with neurological disorders. Theory and Methods: A literature review was conducted to compile metabolic concentrations and relaxation times in a wide range of neuropathologies and regions of interest. Simulations were performed to construct receiver operating characteristic curves and compute the associated areas (area under the curve) to examine the sensitivity and specificity of MRS for detecting each pathology in each region. Classification accuracy was assessed using metabolite concentrations corrected using population‐averages for T1, T2, and B1+ (conventional MRS); using metabolite concentrations corrected using per‐subject values (multiparametric MRS); and using an optimal linear multiparametric estimator comprised of the metabolites' concentrations and relaxation constants (multiparametric MRS). Additional simulations were conducted to find the minimal intra‐subject precision needed for each parameter. Results: Compared with conventional MRS, multiparametric approaches yielded area under the curve improvements for almost all neuropathologies and regions of interest. The median area under the curve increased by 0.14 over the entire dataset, and by 0.24 over the 10 instances with the largest individual increases. Conclusions: Multiparametric MRS can substantially improve the clinical utility of MRS in diagnosing and assessing brain pathology, motivating the design and use of novel multiparametric sequences. [ABSTRACT FROM AUTHOR]