Magnetic Resonance Spectroscopy of Hypoxic-Ischemic Encephalopathy After Cardiac Arrest

Objective:To correlate brain metabolites to clinical outcome using magnetic resonance spectroscopy (MRS) in patients undergoing targeted temperature management (TTM) after cardiac arrest, and assess their relationships to MRI and EEG variables.Methods:A prospective cohort of 50 patients was studied. The primary outcome was coma recovery to follow commands. Comparison of MRS measures in the posterior cingulate gyrus, parietal white matter, basal ganglia, and brainstem were also made to 25 normative control subjects.Results:Fourteen of 50 achieved coma recovery before hospital discharge. There was a significant decrease in total N-acetyl-aspartate (NAA/Cr) and an increase in lactate (Lac/Cr) in patients who did not recover, with changes most prominent in the posterior cingulate gyrus. Patients who recovered had decrease in NAA/Cr as compared to control subjects. NAA/Cr had a strong monotonic relationship with MRI cortical apparent diffusion coefficient (ADC); lactate level exponentially increased with decreasing ADC. EEG suppression/burst suppression was universally associated with lactate elevation.Conclusions:NAA and lactate changes are associated with clinical/MRI/EEG changes consistent with hypoxic-ischemic encephalopathy (HIE) and are most prominent in the posterior cingulate gyrus. NAA/Cr decrease observed in patients with good outcomes suggests mild HIE in patients asymptomatic at hospital discharge. The appearance of cortical lactate represents a deterioration of aerobic energy metabolism and is associated with EEG background suppression, synaptic transmission failure, and severe, potentially irreversible HIE.Classification of Evidence:This study provides Class IV evidence that in patients undergoing TTM after cardiac arrest, brain MRS-determined decrease in total NAA/Cr and an increase in Lac/Cr are associated with an increased risk of not recovering.

31-Phosphorus Magnetic Resonance Spectroscopy in Evaluation of Glioma and Metastases in 3T MRI

Background: 31-Phosphorus magnetic resonance spectroscopy (31-P MRS) has excellent potential for clinical neurological practice because of its noninvasive in-vivo assessment of cellular energy metabolism and the indirect evaluation of the phospholipid composition of the cell membrane, intracellular pH, and intracellular Mg2+ concentration. Purpose: The aim of this study was to evaluate the metabolic characteristics of glioma and metastases using 31-P MRS and assess utility to differentiate both. Study Type: Prospective study. Population: Fifteen consecutive patients with brain tumor. Field Strength/Sequence: Three-tesla magnetic resonance imaging/three-dimensional MRS imaging sequence. Statistical Tests: Unpaired sample t-test, and one-way analysis of variance with Tukey's post-hoc test. Results: Significantly decreased values of phosphomonoesters/inorganic phosphate (PME/Pi) in the tumor group (1.22 ± 0.72) compared with controls (2.28 ± 1.44) with a p-value of 0.041 were observed. There is a significant decrease in phosphocreatine (PCr)/Pi values (energy demand) in the tumor group (2.76 ± 0.73) compared with controls (4.13 ± 1.75) with a p-value of 0.050. Significant increase in Pi/adenosine triphosphate (ATP) was noted in tumor group (0.28 ± 0.09) compared with controls (0.22 ± 0.08) with p-value 0.049. Among tumor group, PME/PCr values were significantly decreased in gliomas (0.35 ± 0.17) than metastasis (0.58 ± 0.23) compared with controls with a p-value of 0.047. A significant decrease in PME/ATP was noted in gliomas (0.25 ± 0.12) than metastasis (0.41 ± 0.14) compared with controls with a p-value of 0.034. The tumor group exhibits alkaline pH (7.12 ± 0.10) compared with the normal parenchyma (7.04 ± 0.06) with a significant p-value of 0.025. Glioma and metastasis could not be differentiated with pH. However, the perilesional edema of glioma shows alkaline pH (7.09 ± 0.06) and metastasis shows acidic pH (7.02 ± 0.05) with a significant p-value of 0.030. Conclusion: Our study provides new insight into the cellular constituents and pH of gliomas and metastases and results were significant in differentiation between these two. However, due to the additional high expense, it is available as a research tool in very few institutions in India.

Comparison of Convolutional Neural Networks Based Method and LCmodel on the Quantification of in Vivo Magnetic Resonance Spectroscopy

Quantification of metabolites concentrations in institutional unit (IU) for between subject and long-term comparison is considered important strategy in the applications of magnetic resonance spectroscopy (MRS). The aim of this study is to investigate if metabolite concentrations quantified by convolutional neuronal network (CNN) based method associated with a proposed scaling procedure can reflect variations of the metabolite concentrations in institution unit (IU) at different brain regions with different signal-to-noise-ratio (SNR) and linewidth (LW). An error index based on standard error (SE) is proposed to indicate the confidence levels on the prediction for metabolites. In vivo MRS spectra were collected at 3 brain regions from 44 subjects at 3T system. Metabolite concentrations in IU quantified by LCModel and CNN from 44 subjects were compared. For in vivo spectra characterized under different spectral quality in terms of SNR and LW, line narrowing and noise free spectra were successfully exported by CNN. Concentrations of five metabolites quantified by CNN and LCModel are in similar range with statistically significant Pearson’s correlation coefficients (0.28~0.70). SE of the metabolites show positive correlation with Cramer-Rao lower bound (CRLB) (r=0.60) and with absolute CRLB (r=0.84). In conclusion, the CNN based method with the proposed scaling procedures can be used to quantify in vivo MRS spectra. The concentrations of five major metabolites were reported in IU, which are in the same range as those quantified using a routine MRS quantification procedures by LCModel. The SE can be used as error index indicating predicted uncertainties for metabolites with the information similar to the absolute CRLB.