Assaf Tal

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.

Temperature is a hallmark parameter influencing almost all magnetic resonance properties (e.g., T 1 , T 2 , 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.