Candace C. Fleischer

BACKGROUND AND OBJECTIVES: Brain temperature is a promising marker of prognosis after injury. Although magnetic resonance (MR) thermometry and biophysical modeling can approximate absolute temperature, limited validation has been performed with direct measurements in a large animal model. The goal of this pilot study was to compare brain temperature measured using MR chemical shift thermometry (CST) and biophysical modeling with that measured using a fiber optic probe in a porcine model. METHODS: MR data were acquired from a Yorkshire pig using a 3 T whole body MR scanner, with simultaneous monitoring of brain and body temperature using a fiber optic probe surgically implanted in the left thalamus and a rectal temperature probe, respectively. MR spectroscopy was acquired for CST. T ₁ -weighted images and MR angiography and venography were acquired for biophysical modeling. Temperature was calculated using the chemical shift difference between water and N -acetylaspartate. Using MR input data of tissue and vessel structures, along with bioheat equations, whole brain temperatures were predicted. Brain temperatures measured using CST, biophysical modeling, and direct measurements from the fiber optic probe, along with rectal temperatures, were compared. RESULTS: Brain temperature measured using CST was 39.9°C, whereas that measured using the implanted probe was 40.6°C in the left thalamus. Modeled brain temperature, averaged across all voxels in the left thalamus, was 40.1 ± 0.2°C. The difference between brain temperatures measured using CST and modeled temperature was −0.2°C. Brain temperatures from all 3 methods were equal to or higher than the mean rectal temperature during CST (39.9°C, range = 39.6-40.2°C). The difference between brain temperatures measured using the implanted probe and CST was 0.7°C, whereas the difference between probe-measured and modeled temperature was 0.5°C, on the same order as CST (±0.8°C) and probe accuracy (±0.3°C). CONCLUSION: Brain temperature quantification using CST and biophysical modeling showed strong agreement with ground truth measurements using a fiber optic probe, providing initial validation of both methods to quantify absolute brain temperature.

Computational bioheat modeling offers a powerful approach for characterizing brain temperature when direct measurements are limited. We developed a multiscale biophysical model with conservation of mass, momentum, and energy across all spatial scales to predict human brain temperature and compared the temperature maps with a previously developed model without global momentum conservation and in vivo magnetic resonance (MR) thermometry. Subject-specific brain anatomy obtained from MR images was incorporated into bioheat equations, integrating physiological parameters such as metabolic heat and cerebral blood flow. A novel approach using "transitional" microvasculature was introduced to bridge macro- and micro-scale domains, enabling momentum conservation and accurate prediction of cerebral hemodynamics. Across 30 healthy subjects, voxel-wise mean absolute differences of 0.18-0.36 °C between modeled and experimentally measured temperatures were observed. ROI-based analysis showed significant correlations between model predictions and MR measurements of brain temperature when system-wide momentum is properly conserved, but no correlation was observed for model predictions without momentum conservation. A simulated scenario of middle cerebral artery occlusion highlighted the importance of momentum conservation to mimic realistic physiological changes. The agreement of model predicted temperatures with MR measurements support future clinical applications where brain temperature may serve as a biomarker for neurological disorders.

Purpose: Quantification of metabolite concentrations using MRS requires tissue‐dependent signal corrections. Accurate estimation of voxel tissue composition is therefore essential. Commonly used brain tissue segmentation tools differ in their algorithms and implementation, potentially introducing variability in MRS‐derived concentration estimates. This study investigates the impact and reliability of tissue segmentation on metabolite quantification. Methods: Three segmentation tools (ANTs, FSL, SPM) were evaluated using an in vivo test–retest MRI/MRS dataset. Voxelwise GM/WM/CSF fractions were applied to compute tissue‐corrected total creatine (tCr) concentrations. Linear mixed‐effects modeling, variance‐component partitioning, and intraclass correlation coefficients (ICCs) quantified tool‐, session‐, and participant‐related variance under permutation scenarios that isolated segmentation‐ and MRS‐related effects. As a benchmark for segmentation performance, comparisons with manually segmented data were conducted across three brain regions. Results: Segmentation tools produced systematically different tissue fractions that propagated into differences in tCr concentration estimates. Variance partitioning attributed 56.8%, 50.0%, and 51.3% of total tCr concentration variability to segmentation tool across the three permutations, with participant‐specific factors accounting for 34.7%, 36.2%, and 28.5%, respectively. When segmentation variability was held constant, test–retest reliability was high (ICC > 0.8) but dropped to ∼0.5 when both segmentation and MRS variability varied. Agreement with manual segmentation was region‐ and tool‐dependent, with the lowest agreement in the thalamus. Conclusion: Tissue segmentation contributes substantially to the variability in MRS‐derived metabolite concentration estimates. These results underscore the need for transparent segmentation reporting and data sharing to ensure reproducibility and cross‐study comparability in MRS research.

Adolescence is a period of neural development, marked by maturation of brain structure and function. While sex- and age-related markers of structural brain development are documented, neurochemical and cognitive changes are less understood. Our goal was to evaluate neurochemistry and cognition in adolescents as a function of sex and age. Magnetic resonance spectroscopy quantified brain metabolites, and attention networking, digital trail making, and cued task switching tests measured cognition in 354 healthy adolescents. Groupwise comparisons and linear regressions evaluated sex- and age-related effects, respectively. Males were differentiated from females in cognitive performance and brain metabolite concentrations, including myo-inositol, glutamate + glutamine (Glx), N-acetylaspartate, and creatine. Males performed tasks with faster speed while females demonstrated better accuracy. Decreases in Glx concentration and faster reaction times were associated with increasing age, indicative of maturing brain function during adolescence. These findings highlight adolescence as a period of active brain development.

While antiretroviral therapy is successful in conferring peripheral human immunodeficiency virus type 1 (HIV-1) suppression, eradication of HIV-1 reservoir

Background: The effect of the positron emission tomography (PET) modality in hybrid PET/MR scanners on MR spectral quality is unclear. Purpose: To evaluate spectral quality, quantification, and repeatability of brain MR spectroscopy (MRS) acquired on PET/MR and standalone MR scanners. Study Type: Prospective. Subjects: 23 healthy adults (male/female = 11/12, age = 27 ± 11 years). Field Strength/Sequence: 3T, semi‐localized by adiabatic selective refocusing. Assessment: Single voxel MR spectra were acquired with GE Signa PET/MR and Siemens Prisma Fit MR scanners in healthy volunteers, and additionally with a GE Premier MR scanner in vitro. Spectral quality, metabolite concentrations, and repeatability were evaluated. Statistical Tests: Linear mixed models assessed signal‐to‐noise ratio (SNR), full width at half maximum (FWHM), and metabolites/total creatine (tCr). Intraclass correlation coefficients (ICCs) determined repeatability; mean squared error (MSE) evaluated accuracy of quantification. Values are reported as mean ± standard deviation. Significance was determined by p ≤ 0.05. Results: In vivo spectra acquired using the Siemens standalone MR showed significantly higher SNR (PET/MR: 118.8 ± 19.1, MR: 132.2 ± 24.4) but significantly broader FWHM (PET/MR: 7.2 ± 1.8 Hz, MR: 8.4 ± 2.5 Hz) compared to PET/MR. All metabolite ratios were significantly higher in spectra acquired on the standalone MR. Within‐session repeatability was good (ICC > 0.75), and between‐session repeatability was moderate to excellent (ICC 0.5 to > 0.90) for all metabolites in vivo. In vitro spectra acquired with the PET/MR had significantly higher SNR (329.1 ± 46.1) than the Siemens standalone MR (150.8 ± 49.3) but significantly lower than the GE standalone MR (395.9 ± 33.7). The PET/MR produced narrower FWHM (2.1 ± 0.4 Hz) than the Siemens standalone MR (4.1 ± 1.7 Hz) but broader than the GE standalone MR (1.6 ± 0.1 Hz) and lower MSE for some metabolites. Data Conclusion: MR spectral quality appears uncompromised when acquired with hybrid PET/MR compared to standalone MR.

Purpose: Estimation of metabolite concentrations in brain magnetic resonance spectroscopy (MRS) requires correction for differences in tissue water content, relaxation properties, and the proportions of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). Accurate knowledge of the relative proportions of these tissue classes within the volume of interest is therefore essential for reliable quantification. Commonly used brain segmentation tools differ in their algorithms, priors, and implementation, potentially introducing variability in MRS-derived concentration estimates. This study investigates the impact of segmentation software on estimated absolute concentrations. Methods: Three segmentation software tools, ANTs, FSL, and SPM, were evaluated. Segmentations were applied to an in vivo test-retest MR dataset to assess (1) differences in estimated tissue fractions, and (2) how these differences propagate into tissue-corrected metabolite concentrations. As an additional validity check and biological benchmark of segmentation performance, age-related associations with GM and total creatine (tCr) were examined. Results: Significant differences ( p < 0.0001) were observed in tissue fraction estimates between segmentation tools, leading to differences in metabolite concentration estimates of up to 9% under identical acquisition and modeling conditions. Although the strength of the correlation varied between segmentation methods, no statistically significant differences were found. Conclusion: The choice of segmentation methodology contributed substantially to variability in MRS “absolute” metabolite concentration estimates. These results underscore the need for transparent segmentation reporting to ensure reproducibility and cross-study comparability in MRS research. Quantifying the segmentation-driven variability allows researchers to contextualize cross-study differences, helping determine whether observed effects are methodological or biologically meaningful.