Observation of intramyocellular lipids by means of 1H magnetic resonance spectroscopy

Magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) are being increasingly used for investigations of human muscle physiology. While MRI reveals the morphology of muscles in great detail (e.g. for the determination of muscle volumes), MRS provides information on the chemical composition of the tissue. Depending on the observed nucleus, MRS allows the monitoring of high-energy phosphates (31P MRS), glycogen (13C MRS), or intramyocellular lipids (1H MRS), to give only a few examples. The observation of intramyocellular lipids (IMCL) by means of 1H MRS is non-invasive and, therefore, can be repeated many times and with a high temporal resolution. MRS has the potential to replace the biopsy for the monitoring of IMCL levels; however, the biopsy still has the advantage that other methods such as those used in molecular biology can be applied to the sample. The present study describes variations in the IMCL levels (expressed in mmol/kg wet weight and ml/100 ml) in three different muscles before and after (0, 1, 2, and 5 d) marathon runs for a well-trained individual who followed two different recovery protocols varying mainly in the diet. It was shown that the repletion of IMCL levels is strongly dependent on the diet post exercise. The monitoring of IMCL levels by means of 1H MRS is extremely promising, but several methodological limitations and pitfalls need to be considered, and these are addressed in the present revie

In vivo1H-MR spectroscopy of the human heart

4. Conclusions: Combined respiratory and cardiac triggering improves the localization accuracy and spectral quality in cardiac1H-MRS dramatically leading to substantially increased spectral reproducibility. The best practical realization of double triggering turned out to be the use of the ECG amplitude when making use of the fact that it is modulated by respiration. In spite of the spectral quality achieved in most subjects, we still fail to record satisfactory spectra in a minority of subjects. The reasons for this are not understood at present but must be some particulars of either a given subject or the experimental setup. The cardiac1H-MR spectra contain quantifiable contributions from creatine, TMA, lipids, and probably taurine. It is possible that the spectral contributions of creatine are subject to dipolar coupling similar to the observations for skeletal muscl

Observation of intramyocellular lipids by means of 1H magnetic resonance spectroscopy

Magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) are being increasingly used for investigations of human muscle physiology. While MRI reveals the morphology of muscles in great detail (e.g. for the determination of muscle volumes), MRS provides information on the chemical composition of the tissue. Depending on the observed nucleus, MRS allows the monitoring of high-energy phosphates (31P MRS), glycogen (13C MRS), or intramyocellular lipids (1H MRS), to give only a few examples. The observation of intramyocellular lipids (IMCL) by means of 1H MRS is non-invasive and, therefore, can be repeated many times and with a high temporal resolution. MRS has the potential to replace the biopsy for the monitoring of IMCL levels; however, the biopsy still has the advantage that other methods such as those used in molecular biology can be applied to the sample. The present study describes variations in the IMCL levels (expressed in mmol/kg wet weight and ml/100 ml) in three different muscles before and after (0, 1, 2, and 5 d) marathon runs for a well-trained individual who followed two different recovery protocols varying mainly in the diet. It was shown that the repletion of IMCL levels is strongly dependent on the diet post exercise. The monitoring of IMCL levels by means of 1H MRS is extremely promising, but several methodological limitations and pitfalls need to be considered, and these are addressed in the present review

Erratum to: Two-dimensional linear-combination model fitting of magnetic resonance spectra to define the macromolecule baseline using FiTAID, a fitting tool for arrays of interrelated datasets

Object: To propose the determination of the macromolecular baseline (MMBL) in clinical 1H MR spectra based on T1 and T2 differentiation using 2D fitting in FiTAID, a general Fitting Tool for Arrays of Interrelated Datasets. Materials and methods: Series of localized inversion-recovery (IR) and 2DJ separation spectra of the brain were recorded at 3T. The MMBL was determined by three 2D evaluation methods based on (1) IR spectra only, (2) 2DJ spectra only, (3) both IR and 2DJ spectra (2DJ-IR). Their performance was compared using synthetic spectra and based on variability and reproducibility as obtained in vivo from 12 subjects in 20 examinations. Results: All methods performed well for synthetic data. In vivo, 2DJ-only yielded larger variations than the other methods. IR-only and 2DJ-IR yielded similar performance. FiTAID is illustrated with further applications where linear-combination model fitting of interrelated arrays of spectra is advantageous. Conclusion: 2D-Fitting offers the possibility to determine the MMBL based on a range of complementary experimental spectra not relying on smoothness criteria or global assumptions on T1. Since 2DJ-IR includes information from spectra with different inversion and echo times, it is expected to be more robust in cases with more variable data quality and overlap with lipid resonance

Denoising single MR spectra by deep learning: Miracle or mirage?

PURPOSE The inherently poor SNR of MRS measurements presents a significant hurdle to its clinical application. Denoising by machine or deep learning (DL) was proposed as a remedy. It is investigated whether such denoising leads to lower estimate uncertainties or whether it essentially reduces noise in signal-free areas only. METHODS Noise removal based on supervised DL with U-nets was implemented using simulated 1 H MR spectra of human brain in two approaches: (1) via time-frequency domain spectrograms and (2) using 1D spectra as input. Quality of denoising was evaluated in three ways: (1) by an adapted fit quality score, (2) by traditional model fitting, and (3) by quantification via neural networks. RESULTS Visually appealing spectra were obtained; hinting that denoising is well-suited for MRS. However, an adapted denoising score showed that noise removal is inhomogeneous and more efficient for signal-free areas. This was confirmed by quantitative analysis of traditional fit results as well as DL quantitation following DL denoising. DL denoising, although apparently successful as judged by mean squared errors, led to substantially biased estimates in both implementations. CONCLUSION The implemented DL-based denoising techniques may be useful for display purposes, but do not help quantitative evaluations, confirming expectations based on estimation theory: Cramér Rao lower bounds defined by the original data and the appropriate fitting model cannot be circumvented in an unbiased way for single data sets, unless additional prior knowledge can be incurred in the form of parameter restrictions/relations or applicable substates

Forecasting the quality of water-suppressed (1) H MR spectra based on a single-shot water scan.

PURPOSE To investigate whether an initial non-water-suppressed acquisition that provides information about the signal-to-noise ratio (SNR) and linewidth is enough to forecast the maximally achievable final spectral quality and thus inform the operator whether the foreseen number of averages and achieved field homogeneity is adequate. METHODS A large range of spectra with varying SNR and linewidth was simulated and fitted with popular fitting programs to determine the dependence of fitting errors on linewidth and SNR. A tool to forecast variance based on a single acquisition was developed and its performance evaluated on simulated and in vivo data obtained at 3 Tesla from various brain regions and acquisition settings. RESULTS A strong correlation to real uncertainties in estimated metabolite contents was found for the forecast values and the Cramer-Rao lower bounds obtained from the water-suppressed spectra. CONCLUSION It appears to be possible to forecast the best-case errors associated with specific metabolites to be found in model fits of water-suppressed spectra based on a single water scan. Thus, nonspecialist operators will be able to judge ahead of time whether the planned acquisition can possibly be of sufficient quality to answer the targeted clinical question or whether it needs more averages or improved shimming. Magn Reson Med, 2016. © 2016 International Society for Magnetic Resonance in Medicine

Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias.

PURPOSE The aims of this work are (1) to explore deep learning (DL) architectures, spectroscopic input types, and learning designs toward optimal quantification in MR spectroscopy of simulated pathological spectra; and (2) to demonstrate accuracy and precision of DL predictions in view of inherent bias toward the training distribution. METHODS Simulated 1D spectra and 2D spectrograms that mimic an extensive range of pathological in vivo conditions are used to train and test 24 different DL architectures. Active learning through altered training and testing data distributions is probed to optimize quantification performance. Ensembles of networks are explored to improve DL robustness and reduce the variance of estimates. A set of scores compares performances of DL predictions and traditional model fitting (MF). RESULTS Ensembles of heterogeneous networks that combine 1D frequency-domain and 2D time-frequency domain spectrograms as input perform best. Dataset augmentation with active learning can improve performance, but gains are limited. MF is more accurate, although DL appears to be more precise at low SNR. However, this overall improved precision originates from a strong bias for cases with high uncertainty toward the dataset the network has been trained with, tending toward its average value. CONCLUSION MF mostly performs better compared to the faster DL approach. Potential intrinsic biases on training sets are dangerous in a clinical context that requires the algorithm to be unbiased to outliers (i.e., pathological data). Active learning and ensemble of networks are good strategies to improve prediction performances. However, data quality (sufficient SNR) has proven as a bottleneck for adequate unbiased performance-like in the case of MF

Macromolecular background signal and non-Gaussian metabolite diffusion determined in human brain using ultra-high diffusion weighting.

PURPOSE Definition of a macromolecular MR spectrum based on diffusion properties rather than relaxation time differences and characterization of non-Gaussian diffusion of brain metabolites with strongly diffusion-weighted MR spectroscopy. METHODS Short echo time MRS with strong diffusion-weighting with b-values up to 25 ms/μm2 at two diffusion times was implemented on a Connectom system and applied in combination with simultaneous spectral and diffusion decay modeling. Motion-compensation was performed with a combined method based on the simultaneously acquired water and a macromolecular signal. RESULTS The motion compensation scheme prevented spurious signal decay reflected in very small apparent diffusion constants for macromolecular signal. Macromolecular background signal patterns were determined using multiple fit strategies. Signal decay corresponding to non-Gaussian metabolite diffusion was represented by biexponential fit models yielding parameter estimates for human gray matter that are in line with published rodent data. The optimal fit strategies used constraints for the signal decay of metabolites with limited signal contributions to the overall spectrum. CONCLUSION The determined macromolecular spectrum based on diffusion properties deviates from the conventional one derived from longitudinal relaxation time differences calling for further investigation before use as experimental basis spectrum when fitting clinical MR spectra. The biexponential characterization of metabolite signal decay is the basis for investigations into pathologic alterations of microstructure

Magnetic resonance spectroscopy investigation in the right human hippocampus following spinal cord injury.

OBJECTIVE Preclinical studies have shown that cognitive impairments following spinal cord injury (SCI), such as impaired spatial memory, are linked to inflammation, neurodegeneration, and reduced neurogenesis in the right hippocampus. This cross-sectional study aims to characterize metabolic and macrostructural changes in the right hippocampus and their association to cognitive function in traumatic SCI patients. METHODS Within this cross-sectional study, cognitive function was assessed in 28 chronic traumatic SCI patients and 18 age-, sex-, and education-matched healthy controls by a visuospatial and verbal memory test. A magnetic resonance spectroscopy (MRS) and structural MRI protocol was performed in the right hippocampus of both groups to quantify metabolic concentrations and hippocampal volume, respectively. Group comparisons investigated changes between SCI patients and healthy controls and correlation analyses investigated their relationship to memory performance. RESULTS Memory performance was similar in SCI patients and healthy controls. The quality of the recorded MR spectra was excellent in comparison to the best-practice reports for the hippocampus. Metabolite concentrations and volume of the hippocampus measured based on MRS and MRI were not different between two groups. Memory performance in SCI patients and healthy controls was not correlated with metabolic or structural measures. CONCLUSION This study suggests that the hippocampus may not be pathologically affected at a functional, metabolic, and macrostructural level in chronic SCI. This points toward the absence of significant and clinically relevant trauma-induced neurodegeneration in the hippocampus

Magnetic resonance spectroscopy investigation in the right human hippocampus following spinal cord injury

OBJECTIVE Preclinical studies have shown that cognitive impairments following spinal cord injury (SCI), such as impaired spatial memory, are linked to inflammation, neurodegeneration, and reduced neurogenesis in the right hippocampus. This cross-sectional study aims to characterize metabolic and macrostructural changes in the right hippocampus and their association to cognitive function in traumatic SCI patients. METHODS Within this cross-sectional study, cognitive function was assessed in 28 chronic traumatic SCI patients and 18 age-, sex-, and education-matched healthy controls by a visuospatial and verbal memory test. A magnetic resonance spectroscopy (MRS) and structural MRI protocol was performed in the right hippocampus of both groups to quantify metabolic concentrations and hippocampal volume, respectively. Group comparisons investigated changes between SCI patients and healthy controls and correlation analyses investigated their relationship to memory performance. RESULTS Memory performance was similar in SCI patients and healthy controls. The quality of the recorded MR spectra was excellent in comparison to the best-practice reports for the hippocampus. Metabolite concentrations and volume of the hippocampus measured based on MRS and MRI were not different between two groups. Memory performance in SCI patients and healthy controls was not correlated with metabolic or structural measures. CONCLUSION This study suggests that the hippocampus may not be pathologically affected at a functional, metabolic, and macrostructural level in chronic SCI. This points toward the absence of significant and clinically relevant trauma-induced neurodegeneration in the hippocampus