Versatile frequency domain fitting using time domain models and prior knowledge

Comparison of baseline correction algorithms for in vivo 1H-MRS

Proton MRS is used clinically to collect localized, quantitative metabolic data from living tissues. However, the presence of baselines in the spectra complicates accurate MRS data quantification. The occurrence of baselines is not specific to short-echo-time MRS data. In short-echo-time MRS, the baseline consists typically of a dominating macromolecular (MM) part, and can, depending on B0 shimming, poor voxel placement, and/or localization sequences, also contain broad water and lipid resonance components, indicated by broad components (BCs). In long-echo-time MRS, the MM part is usually much smaller, but BCs may still be present. The sum of MM and BCs is denoted by the baseline. Many algorithms have been proposed over the years to tackle these artefacts. A first approach is to identify the baseline itself in a preprocessing step, and a second approach is to model the baseline in the quantification of the MRS data themselves. This paper gives an overview of baseline handling algorithms and also proposes a new algorithm for baseline correction. A subset of suitable baseline removal algorithms were tested on in vivo MRSI data (semi-LASER at TE = 40 ms) and compared with the new algorithm. The baselines in all datasets were removed using the different methods and subsequently fitted using spectrIm-QMRS with a TDFDFit fitting model that contained only a metabolite basis set and lacked a baseline model. The same spectra were also fitted using a spectrIm-QMRS model that explicitly models the metabolites and the baseline of the spectrum. The quantification results of the latter quantification were regarded as ground truth. The fit quality number (FQN) was used to assess baseline removal effectiveness, and correlations between metabolite peak areas and ground truth models were also examined. The results show a competitive performance of our new proposed algorithm, underscoring its automatic approach and efficiency. Nevertheless, none of the tested baseline correction methods achieved FQNs as good as the ground truth model. All separately applied baseline correction methods introduce a bias in the observed metabolite peak areas. We conclude that all baseline correction methods tested, when applied as a separate preprocessing step, yield poorer FQNs and biased quantification results. While they may enhance visual display, they are not advisable for use before spectral fitting.

TensorFit: A torch-based tool for ultrafast metabolite fitting of large MRSI data sets

PURPOSE

To introduce a tool (TensorFit) for ultrafast and robust metabolite fitting of MRSI data based on Torch's auto-differentiation and optimization framework.

METHODS

TensorFit was implemented in Python based on Torch's auto-differentiation to fit individual metabolites in MRS spectra. The underlying time domain and/or frequency domain fitting model is based on a linear combination of metabolite spectroscopic response. The computational time efficiency and accuracy of TensorFit were tested on simulated and in vivo MRS data and compared against TDFDFit and QUEST.

RESULTS

TensorFit demonstrates a significant improvement in computation speed, achieving a 165-times acceleration compared with TDFDFit and 115 times against QUEST. TensorFit showed smaller percentual errors on simulated data compared with TDFDFit and QUEST. When tested on in vivo data, it performed similarly to TDFDFit with a 2% better fit in terms of mean squared error while obtaining a 169-fold speedup.

CONCLUSION

TensorFit enables fast and robust metabolite fitting in large MRSI data sets compared with conventional metabolite fitting methods. This tool could boost the clinical applicability of large 3D MRSI by enabling the fitting of large MRSI data sets within computation times acceptable in a clinical environment.

Higher striatal glutamate in male youth with internet gaming disorder

Internet gaming disorder (IGD) was included in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) as a research diagnosis, but little is known about its pathophysiology. Alterations in frontostriatal circuits appear to play a critical role in the development of addiction. Glutamate is considered an essential excitatory neurotransmitter in addictive disorders. This study's aim was to investigate striatal glutamate in youth with IGD compared to healthy controls (HC). Using a cross-sectional design, 25 adolescent male subjects fulfilling DSM-5 criteria for IGD and 26 HC, matched in age, education, handedness and smoking, were included in the analysis. A structural MPRAGE T1 sequence followed by a single-voxel magnetic resonance spectroscopy MEGA-PRESS sequence (TR = 1500 ms, TE = 68 ms, 208 averages) with a voxel size of 20 mm3 were recorded on 3 T Siemens Magnetom Prisma scanner. The voxel was placed in the left striatum. Group comparison of the relative glutamate and glutamine (Glx) was calculated using regression analysis. IGD subjects met an average of 6.5 of 9 DSM-5 IGD criteria and reported an average of 29 h of weekly gaming. Regression analysis showed a significant group effect for Glx, with higher Glx levels in IGD as compared to HC (coef. = .086, t (50) = 2.17, p = .035). Our study is the first to show higher levels of Glx in the striatum in youth with IGD. The elevation of Glx in the striatum may indicate hyperactivation of the reward system in IGD. Thus, results confirm that neurochemical alterations can be identified in early stages of behavioral addictions.

Simultaneous multi-region detection of GABA+ and Glx using 3D spatially resolved SLOW-editing and EPSI-readout at 7T

GABA+ and Glx (glutamate and glutamine) are widely studied metabolites, yet the commonly used magnetic resonance spectroscopy (MRS) techniques have significant limitations, including sensitivity to B0 and B1+-inhomogeneities, limited bandwidth of MEGA-pulses, high SAR which is accentuated at 7T. To address these limitations, we propose SLOW-EPSI method, employing a large 3D MRSI coverage and achieving a high resolution down to 0.26 ml. Simulation results demonstrate the robustness of SLOW-editing for both GABA+ and Glx against B0 and B1+-inhomogeneities within the range of [-0.3, +0.3] ppm and [40 %, 250 %], respectively. Two protocols, both utilizing a 70 mm thick FOV slab, were employed to target distinct brain regions in vivo, differentiated by their orientation: transverse and tilted. Protocol 1 (n = 11) encompassed 5 locations (cortical gray matter, white matter, frontal lobe, parietal lobe, and cingulate gyrus). Protocol 2 (n = 5) involved 9 locations (cortical gray matter, white matter, frontal lobe, occipital lobe, cingulate gyrus, caudate nucleus, hippocampus, putamen, and inferior thalamus). Quantitative analysis of GABA+ and Glx was conducted in a stepwise manner. First, B1+/B1--inhomogeneities were corrected using water reference data. Next, GABA+ and Glx values were calculated employing spectral fitting. Finally, the GABA+ level for each selected region was compared to the global Glx within the same subject, generating the GABA+/Glx_global ratio. Our findings from two protocols indicate that the GABA+/Glx_global level in cortical gray matter was approximately 16 % higher than in white matter. Elevated GABA+/Glx_global levels acquired with protocol 2 were observed in specific regions such as the caudate nucleus (0.118±0.067), putamen (0.108±0.023), thalamus (0.092±0.036), and occipital cortex (0.091±0.010), when compared to the cortical gray matter (0.079±0.012). Overall, our results highlight the effectiveness of SLOW-EPSI as a robust and efficient technique for accurate measurements of GABA+ and Glx at 7T. In contrast to previous SVS and 2D-MRSI based editing sequences with which only one or a limited number of brain regions can be measured simultaneously, the method presented here measures GABA+ and Glx from any brain area and any arbitrarily shaped volume that can be flexibly selected after the examination. Quantification of GABA+ and Glx across multiple brain regions through spectral fitting is achievable with a 9-minute acquisition. Additionally, acquisition times of 18-27 min (GABA+) and 9-18 min (Glx) are required to generate 3D maps, which are constructed using Gaussian fitting and peak integration.

Information theoretic evaluation of Lorentzian, Gaussian, Voigt, and symmetric alpha-stable models of reversible transverse relaxation in cervical cancer in vivo at 3 T

OBJECTS

To better characterize cervical cancer at 3 T. MRI transverse relaxation patterns hold valuable biophysical information about cellular scale microstructure. Lorentzian modeling is typically used to represent intravoxel frequency distributions, resulting in mono-exponential decay of reversible transverse relaxation. However, deviations from mono-exponential decay are expected theoretically and observed experimentally.

MATERIALS AND METHODS

We compared the information content of four models of signal attenuation with reversible transverse relaxation. Biological phantoms and six women with cervical squamous cell carcinoma were imaged using a gradient-echo sampling of the spin-echo (GESSE) sequence. Lorentzian, Gaussian, Voigt, and Symmetric α-Stable (SAS) models were ranked using Akaike's Information Criterion (AIC), and the model retaining the highest information content was identified at each voxel as the best model.

RESULTS

The Lorentzian model resulted in information loss in large fractions of the phantoms and cervix. Gaussian and SAS models frequently had higher information content than the Lorentzian in much of the areas of interest. The Voigt model rarely surpassed the three other models in terms of information content.

DISCUSSION

Gaussian and SAS models provide better fitting of data in much of the human cervix at 3 T. Minimizing information loss through improved tissue modeling may have important implications for identifying reliable biomarkers of tumor hypoxia and iron deposition.

Results and interpretation of a fitting challenge for MR spectroscopy set up by the MRS study group of ISMRM

PURPOSE

Fitting of MRS data plays an important role in the quantification of metabolite concentrations. Many different spectral fitting packages are used by the MRS community. A fitting challenge was set up to allow comparison of fitting methods on the basis of performance and robustness.

METHODS

Synthetic data were generated for 28 datasets. Short-echo time PRESS spectra were simulated using ideal pulses for the common metabolites at mostly near-normal brain concentrations. Macromolecular contributions were also included. Modulations of signal-to-noise ratio (SNR); lineshape type and width; concentrations of γ-aminobutyric acid, glutathione, and macromolecules; and inclusion of artifacts and lipid signals to mimic tumor spectra were included as challenges to be coped with.

RESULTS

Twenty-six submissions were evaluated. Visually, most fit packages performed well with mostly noise-like residuals. However, striking differences in fit performance were found with bias problems also evident for well-known packages. In addition, often error bounds were not appropriately estimated and deduced confidence limits misleading. Soft constraints as used in LCModel were found to substantially influence the fitting results and their dependence on SNR.

CONCLUSIONS

Substantial differences were found for accuracy and precision of fit results obtained by the multiple fit packages.

Preprocessing, analysis and quantification in single-voxel magnetic resonance spectroscopy: experts' consensus recommendations

Once an MRS dataset has been acquired, several important steps must be taken to obtain the desired metabolite concentration measures. First, the data must be preprocessed to prepare them for analysis. Next, the intensity of the metabolite signal(s) of interest must be estimated. Finally, the measured metabolite signal intensities must be converted into scaled concentration units employing a quantitative reference signal to allow meaningful interpretation. In this paper, we review these three main steps in the post-acquisition workflow of a single-voxel MRS experiment (preprocessing, analysis and quantification) and provide recommendations for best practices at each step.

Unsupervised anomaly detection using generative adversarial networks in 1H-MRS of the brain

The applicability of generative adversarial networks (GANs) capable of unsupervised anomaly detection (AnoGAN) was investigated in the management of quality of ¹H-MRS human brain spectra at 3.0 T. The AnoGAN was trained in an unsupervised manner solely on simulated normal brain spectra and used for filtering out abnormal spectra with a broad range of abnormalities, which were simulated by including abnormal ranges of SNR, linewidth and metabolite concentrations and spectral artifacts such as ghost, residual water, and lipid. The AnoGAN was able to filter out those spectra with SNR less than ~11-12 dB with an accuracy of ~80% or higher (assuming a normal SNR range to be 15-18 dB). It also detected with an accuracy of ~80% or higher those spectra, in which NAA levels were reduced by ~25-30% or more from the lower bound and elevated by ~20-30% or more from the upper bound of the normal concentration range (7.5-17 mmol/L), while the concentrations of the rest of the metabolites were all within the normal ranges. Despite the fact that those spectra contaminated with ghost, residual water or lipid have never been involved in the training or optimization of the AnoGAN, they were correctly classified as abnormal regardless of the types of the artifacts, depending solely on their intensity. Although the current version of our AnoGAN requires further technical improvement particularly for the detection of linewidth-associated abnormality and validation on in vivo data, our unsupervised deep learning-based approach could be an option in addition to those previously reported supervised deep learning-based approaches in the binary classification of spectral quality with an extended abnormal spectra regime.

No Effect of Anodal tDCS on Verbal Episodic Memory Performance and Neurotransmitter Levels in Young and Elderly Participants

Healthy ageing is accompanied by cognitive decline that affects episodic memory processes in particular. Studies showed that anodal transcranial direct current stimulation (tDCS) to the left dorsolateral prefrontal cortex (DLPFC) may counteract this cognitive deterioration by increasing excitability and inducing neuroplasticity in the targeted cortical region. While stimulation gains are more consistent in initial low performers, relying solely on behavioural measures to predict treatment benefits does not suffice for a reliable implementation of this method as a therapeutic option. Hence, an exploration of the underlying neurophysiological mechanisms regarding the differential stimulation effect is warranted. Glutamatergic metabolites (Glx) and γ-aminobutyric acid (GABA) are involved in learning and memory processes and can be influenced with tDCS; wherefore, they present themselves as potential biomarkers for tDCS-induced behavioural gains, which are affiliated with neuroplasticity processes. In the present randomized, double-blind, sham-controlled, crossover study, 33 healthy young and 22 elderly participants received anodal tDCS to their left DLPFC during the encoding phase of a verbal episodic memory task. Using MEGA-PRESS edited magnetic resonance spectroscopy (MRS), Glx and GABA levels were measured in the left DLPFC before and after the stimulation period. Further, we tested whether baseline performance and neurotransmitter levels predicted subsequent gains. No beneficial group effects of tDCS emerged in either verbal retrieval performances or neurotransmitter concentrations. Moreover, baseline performance levels did not predict stimulation-induced cognitive gains, nor did Glx or GABA levels. Nevertheless, exploratory analyses suggested a predictive value of the Glx : GABA ratio, with lower ratios at baseline indicating greater tDCS-related gains in delayed recall performance. This highlights the importance of further studies investigating neurophysiological mechanisms underlying previously observed stimulation-induced cognitive benefits and their respective interindividual heterogeneity.

Terminology and concepts for the characterization of in vivo MR spectroscopy methods and MR spectra: Background and experts' consensus recommendations

With a 40-year history of use for in vivo studies, the terminology used to describe the methodology and results of magnetic resonance spectroscopy (MRS) has grown substantially and is not consistent in many aspects. Given the platform offered by this special issue on advanced MRS methodology, the authors decided to describe many of the implicated terms, to pinpoint differences in their meanings and to suggest specific uses or definitions. This work covers terms used to describe all aspects of MRS, starting from the description of the MR signal and its theoretical basis to acquisition methods, processing and to quantification procedures, as well as terms involved in describing results, for example, those used with regard to aspects of quality, reproducibility or indications of error. The descriptions of the meanings of such terms emerge from the descriptions of the basic concepts involved in MRS methods and examinations. This paper also includes specific suggestions for future use of terms where multiple conventions have emerged or coexisted in the past.

Optimizing acquisition and fitting conditions for 1 H MR spectroscopy investigations in global brain pathology

PURPOSE

To optimize acquisition and fitting conditions for nonfocal disease in terms of voxel size and use of individual coil element data. Increasing the voxel size yields a higher signal-to-noise ratio, but leads to larger linewidths and more artifacts. Several ways to improve the spectral quality for large voxels are exploited and the optimal use of individual coil signals investigated.

METHODS

Ten human subjects were measured at 3 T using a 64-channel receive head coil with a semi-LASER localization sequence under optimized and deliberately mis-set field homogeneity. Eight different voxel sizes (8 to 99 cm³ ) were probed. Spectra were fitted either as weighted sums of the individual coil elements or simultaneously without summation. Eighteen metabolites were included in the fit model that also included the lineshapes from all coil elements as reflected in water reference data. Fitting errors for creatine, myo-Inositol and glutamate are reported as representative parameters to judge optimal acquisition and evaluation conditions.

RESULTS

Minimal Cramér-Rao lower bounds and thus optimal acquisition conditions were found for a voxel size of ~ 70 cm³ for the representative upfield metabolites. Spectral quality in terms of lineshape and artifact appearance was determined to differ substantially between coil elements. Simultaneous fitting of spectra from individual coil elements instead of traditional fitting of a weighted sum spectrum reduced Cramer-Rao lower bounds by up to 17% for large voxel sizes.

CONCLUSION

The optimal voxel size for best precision in determined metabolite content is surprisingly large. Such an acquisition condition is most relevant for detection of low-concentration metabolites, like NAD⁺ or phenylalanine, but also for longitudinal studies where very small alterations in metabolite content are targeted. In addition, simultaneous fitting of single channel spectra enforcing lineshape and coil sensitivity information proved to be superior to traditional signal combination with subsequent fitting.

Effect of linewidth on estimation of metabolic concentration when using water lineshape spectral model fitting for single voxel proton spectroscopy at 7 T

Good B₀ field homogeneity is considered an essential requirement to obtain high-quality MRS data. Many commonly used spectral fitting methods assume that all metabolite signals have Lorentzian or Gaussian shapes. However, B₀ inhomogeneity can both broaden the linewidth and modify the lineshape. In this study, it is hypothesized that a realistic metabolite fitting model, which accounts for B₀ homogeneity on the basis of the water lineshape, will improve the accuracy of estimation of metabolite concentrations. In-vivo water suppressed/unsuppressed single voxel spectroscopy signals were acquired under three different B₀ field homogeneity regimes. Individual realistic basis sets were created for each acquisition. Frequency-domain spectral fitting with LCModel was used to quantify the metabolite concentrations with fitting uncertainties given in terms of the Cramer-Rao lower bound. The quantification results obtained using the water lineshape basis set yielded similar concentrations independent of linewidth and showed a larger fitting error as the linewidth increased. The conventional approach, however quantifies metabolite concentrations with greater variations despite showing a supposedly improved fitting quality. The water lineshape basis set achieved single voxel spectroscopy accuracy that is less sensitive to the linewidth compared to the conventional spectral fitting method for the range of linewidths used in this study, but the precision deteriorated with worsening B₀ field inhomogeneity. The beneficial effect was ascribed to a reduction in the number of degrees of freedom when using the water lineshape to generate the basis set.

A field-invariant method for quantitative analysis with benchtop NMR

Recently developed benchtop instruments have the potential of bringing the benefits of NMR spectroscopy to the wide variety of industrial applications. Unfortunately, their low spectral resolution poses significant challenges for traditional quantification approach. Here we present a novel model-based method designed to overcome these challenges. By defining our models in terms of quantum mechanical properties of the underlying spin system, we make our approach invariant to the spectrometer field strength and especially suitable for analyzing benchtop data. Our experimental results on prepared samples and natural fruit juices confirm the applicability of our method for quantitative analysis of medium-field ¹H NMR spectra. The developed method succeeds in accurately separating the spectra of glucose anomers and even monitoring their interconversion in non-deuterated water. Furthermore, the compositions of unbuffered natural fruit juices estimated using data from 43 MHz to 400 MHz spectrometers are in good agreement with each other and with the reference values from nutrition databases.

Reliable quantification of marrow fat content and unsaturation level using in vivo MR spectroscopy

PURPOSE

To develop a novel technique for reliable quantification of bone marrow fat content and composition using in vivo MR spectroscopy (MRS).

METHODS

An MRS quantification method combining both advantages of Voigt line shape model and time-domain analysis was developed. The proposed method was tested using computer-simulated data and in vivo data acquired at lumbar vertebral bodies of 23 subjects (age, 83.8 ± 3.7 y; male, n = 13; female, n = 10) from L1 to L4. Reliability and reproducibility were calculated for the quantification results. Comparisons between the proposed method and some conventional methods were conducted.

RESULTS

Low mean absolute percentage errors and low mean coefficients of variation for computer simulations suggest that the proposed method is accurate and precise. By using this method, marrow fat content can be quantified reliably, even for data with low spectral resolution and low signal-to-noise ratio (SNR). Unsaturation level can be reliably quantified for data with moderate spectral resolution and moderate SNR. Results obtained from in vivo data using the proposed method demonstrated better model fit than conventional methods.

CONCLUSION

The method proposed in this study has better performance than conventional methods in the quantification of bone marrow MRS data and has great potential for wide applications of studying marrow fat content and composition. Magn Reson Med 79:1722-1729, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

An experimental validation of a Bayesian model for quantification in NMR spectroscopy

The traditional peak integration method for quantitative analysis in nuclear magnetic resonance (NMR) spectroscopy is inherently limited by its ability to resolve overlapping peaks and is susceptible to noise. The alternative model-based approaches not only extend quantification capabilities to these challenging examples but also provide a means for automation of the entire process of NMR data analysis. In this paper, we present a general model for an NMR signal that, in a principled way, takes into account the effects of chemical shifts, relaxation, lineshape imperfections, phasing, and baseline distortions. We test the model using both simulations and experiments, concentrating on simple spectra with well-resolved peaks where we expect conventional analysis to be effective. Our results of quantifying mixture compositions compare favorably with the established methods. At high SNR (>40dB), all approaches usually achieve for these test systems an absolute accuracy of at least 0.01mol/mol for the concentrations of all species. Our model-based approach is successful even for SNR<20dB; it achieves 0.05-0.1mol/mol accuracy in cases where precise phasing is practically impossible due to high levels of noise in the data.

Quantification of 1 H-MRS signals based on sparse metabolite profiles in the time-frequency domain

MRS is an analytical approach used for both quantitative and qualitative analysis of human body metabolites. The accurate and robust quantification capability of proton MRS (¹ H-MRS) enables the accurate estimation of living tissue metabolite concentrations. However, such methods can be efficiently employed for quantification of metabolite concentrations only if the overlapping nature of metabolites, existing static field inhomogeneity and low signal-to-noise ratio (SNR) are taken into consideration. Representation of ¹ H-MRS signals in the time-frequency domain enables us to handle the baseline and noise better. This is possible because the MRS signal of each metabolite is sparsely represented, with only a few peaks, in the frequency domain, but still along with specific time-domain features such as distinct decay constant associated with T₂ relaxation rate. The baseline, however, has a smooth behavior in the frequency domain. In this study, we proposed a quantification method using continuous wavelet transformation of ¹ H-MRS signals in combination with sparse representation of features in the time-frequency domain. Estimation of the sparse representations of MR spectra is performed according to the dictionaries constructed from metabolite profiles. Results on simulated and phantom data show that the proposed method is able to quantify the concentration of metabolites in ¹ H-MRS signals with high accuracy and robustness. This is achieved for both low SNR (5 dB) and low signal-to-baseline ratio (-5 dB) regimes.

INFOS: spectrum fitting software for NMR analysis

Software for fitting of NMR spectra in MATLAB is presented. Spectra are fitted in the frequency domain, using Fourier transformed lineshapes, which are derived using the experimental acquisition and processing parameters. This yields more accurate fits compared to common fitting methods that use Lorentzian or Gaussian functions. Furthermore, a very time-efficient algorithm for calculating and fitting spectra has been developed. The software also performs initial peak picking, followed by subsequent fitting and refinement of the peak list, by iteratively adding and removing peaks to improve the overall fit. Estimation of error on fitting parameters is performed using a Monte-Carlo approach. Many fitting options allow the software to be flexible enough for a wide array of applications, while still being straightforward to set up with minimal user input.

ProFit revisited

PURPOSE

An enhanced version of the ProFit fitting tool was developed and validated to improve the quantification of two-dimensional JRPESS spectroscopic data.

METHODS

The proposed enhancements were achieved by flexible organization of prior knowledge, configurations for different situations, the inclusion of measured macromolecular baseline contribution, additional baseline splines and a model-free lineshape based on self-deconvolution. The new software was tested and tuned on simulated data and subsequently applied to in vivo intrasubject and intersubject data.

RESULTS

Fit results of simulated and acquired spectra show good overall quality suggesting the potential reliable detection of up to 18 metabolites on a 3T system yielding Cramer-Lower-Bounds below 20%.

CONCLUSION

The proposed enhanced version of ProFit together with two-dimensional J-resolved spectroscopy offers the opportunity to reliably detect a wide selection of important brain metabolites on 3T.

Clinical magnetic resonance spectroscopy of the central nervous system

Proton magnetic resonance spectroscopy (1H MRS) is a noninvasive imaging technique that can easily be added to the conventional magnetic resonance (MR) imaging sequences. Using MRS one can directly compare spectra from pathologic or abnormal tissue and normal tissue. Metabolic changes arising from pathology that can be visualized by MRS may not be apparent from anatomy that can be visualized by conventional MR imaging. In addition, metabolic changes may precede anatomic changes. Thus, MRS is used for diagnostics, to observe disease progression, monitor therapeutic treatments, and to understand the pathogenesis of diseases. MRS may have an important impact on patient management. The purpose of this chapter is to provide practical guidance in the clinical application of MRS of the brain. This chapter provides an overview of MRS-detectable metabolites and their significance. In addition some specific current clinical applications of MRS will be discussed, including brain tumors, inborn errors of metabolism, leukodystrophies, ischemia, epilepsy, and neurodegenerative diseases. The chapter concludes with technical considerations and challenges of clinical MRS.

Quantification of in vivo ³¹P NMR brain spectra using LCModel

Quantification of (31)P NMR spectra is commonly performed using line-fitting techniques with prior knowledge. Currently available time- and frequency-domain analysis software includes AMARES (in jMRUI) and CFIT respectively. Another popular frequency-domain approach is LCModel, which has been successfully used to fit both (1)H and (13)C in vivo NMR spectra. To the best of our knowledge LCModel has not been used to fit (31)P spectra. This study demonstrates the feasibility of using LCModel to quantify in vivo (31)P MR spectra, provided that adequate prior knowledge and LCModel control parameters are used. Both single-voxel and MRSI data are presented, and similar results are obtained with LCModel and with AMARES. This provides a new method for automated, operator-independent analysis of (31)P NMR spectra.

Improving the spectral resolution and spectral fitting of (1) H MRSI data from human calf muscle by the SPREAD technique

Proton magnetic resonance spectroscopic imaging ((1) H MRSI) has been used for the in vivo measurement of intramyocellular lipids (IMCLs) in human calf muscle for almost two decades, but the low spectral resolution between extramyocellular lipids (EMCLs) and IMCLs, partially caused by the magnetic field inhomogeneity, has hindered the accuracy of spectral fitting. The purpose of this paper was to enhance the spectral resolution of (1) H MRSI data from human calf muscle using the SPREAD (spectral resolution amelioration by deconvolution) technique and to assess the influence of improved spectral resolution on the accuracy of spectral fitting and on in vivo measurement of IMCLs. We acquired MRI and (1) H MRSI data from calf muscles of three healthy volunteers. We reconstructed spectral lineshapes of the (1) H MRSI data based on field maps and used the lineshapes to deconvolve the measured MRS spectra, thereby eliminating the line broadening caused by field inhomogeneities and improving the spectral resolution of the (1) H MRSI data. We employed Monte Carlo (MC) simulations with 200 noise realizations to measure the variations of spectral fitting parameters and used an F-test to evaluate the significance of the differences of the variations between the spectra before SPREAD and after SPREAD. We also used Cramer-Rao lower bounds (CRLBs) to assess the improvements of spectral fitting after SPREAD. The use of SPREAD enhanced the separation between EMCL and IMCL peaks in (1) H MRSI spectra from human calf muscle. MC simulations and F-tests showed that the use of SPREAD significantly reduced the standard deviations of the estimated IMCL peak areas (p < 10(-8) ), and the CRLBs were strongly reduced (by ~37%).

Optimized radiofrequency coil setup for MR examination of living isolated rat hearts in a horizontal 9.4T magnet

PURPOSE

(i) To optimize an MR-compatible organ perfusion setup for the nondestructive investigation of isolated rat hearts by placing the radiofrequency (RF) coil inside the perfusion chamber; (ii) to characterize the benefit of this system for diffusion tensor imaging and proton ((1) H-) MR spectroscopy.

METHODS

Coil quality assessment was conducted both on the bench, and in the magnet. The benefit of the new RF-coil was quantified by measuring signal-to-noise ratio (SNR), accuracy, and precision of diffusion tensor imaging/error in metabolite amplitude estimation, and compared to an RF-coil placed externally to the perfusion chamber.

RESULTS

The new design provided a 59% gain in signal-to-noise ratio on a fixed rat heart compared to using an external resonator, which found reflection in an improvement of living heart data quality, compared to previous external resonator studies. This resulted in 14-29% improvement in accuracy and precision of diffusion tensor imaging. The Cramer-Rao lower bounds for metabolite amplitude estimations were up to 5-fold smaller.

CONCLUSION

Optimization of MR-compatible perfusion equipment advances the study of rat hearts with improved signal-to-noise ratio performance, and thus improved accuracy/precision.

The use of magnetic resonance spectroscopy in the evaluation of epilepsy

Magnetic resonance spectroscopy (MRS) is indicated in the imaging protocol of the patient with epilepsy to screen for metabolic derangements such as inborn errors of metabolism and to characterize masses that may be equivocal on conventional magnetic resonance imaging for dysplasia versus neoplasia. Single-voxel MRS with echo time of 35 milliseconds may be used for this purpose as a quick screening tool in the epilepsy imaging protocol. MRS is useful in the evaluation of both focal and generalized epilepsy.

Hypoxia refines plasticity of mitochondrial respiration to repeated muscle work

Purpose

We explored whether altered expression of factors tuning mitochondrial metabolism contributes to muscular adaptations with endurance training in the condition of lowered ambient oxygen concentration (hypoxia) and whether these adaptations relate to oxygen transfer as reflected by subsarcolemmal mitochondria and oxygen metabolism in muscle.

Methods

Male volunteers completed 30 bicycle exercise sessions in normoxia or normobaric hypoxia (4,000 m above sea level) at 65 % of the respective peak aerobic power output. Myoglobin content, basal oxygen consumption, and re-oxygenation rates upon reperfusion after 8 min of arterial occlusion were measured in vastus muscles by magnetic resonance spectroscopy. Biopsies from vastus lateralis muscle, collected pre and post a single exercise bout, and training, were assessed for levels of transcripts and proteins being associated with mitochondrial metabolism.

Results

Hypoxia specifically lowered the training-induced expression of markers of respiratory complex II and IV (i.e. SDHA and isoform 1 of COX-4; COX4I1) and preserved fibre cross-sectional area. Concomitantly, trends (p < 0.10) were found for a hypoxia-specific reduction in the basal oxygen consumption rate, and improvements in oxygen repletion, and aerobic performance in hypoxia. Repeated exercise in hypoxia promoted the biogenesis of subsarcolemmal mitochondria and this was co-related to expression of isoform 2 of COX-4 with higher oxygen affinity after single exercise, de-oxygenation time and myoglobin content (r ≥ 0.75). Conversely, expression in COX4I1 with training correlated negatively with changes of subsarcolemmal mitochondria (r < −0.82).

Conclusion

Hypoxia-modulated adjustments of aerobic performance with repeated muscle work are reflected by expressional adaptations within the respiratory chain and modified muscle oxygen metabolism.

Smoothness of in vivo spectral baseline determined by mean-square error

PURPOSE

A nonparametric smooth line is usually added to the spectral model to account for background signals in vivo magnetic resonance spectroscopy (MRS). The assumed smoothness of the baseline significantly influences quantitative spectral fitting. In this paper, a method is proposed to minimize baseline influences on the estimated spectral parameters.

METHODS

The nonparametric baseline function with a given smoothness was treated as a function of spectral parameters. Its uncertainty was measured by root-mean-square error (RMSE). The proposed method was demonstrated with a simulated spectrum and in vivo spectra of both short echo time and averaged echo times. The estimated in vivo baselines were compared with the metabolite-nulled spectra and the LCModel-estimated baselines. The accuracies of estimated baseline and metabolite concentrations were further verified via cross-validation.

RESULTS

An optimal smoothness condition was found that led to the minimal baseline RMSE. In this condition, the best fit was balanced against minimal baseline influences on metabolite concentration estimates.

CONCLUSION

Baseline RMSE can be used to indicate estimated baseline uncertainties and serve as the criterion for determining the baseline smoothness of in vivo MRS.

J-refocused 1H PRESS DEPT for localized 13C MR spectroscopy

Proton point-resolved spectroscopy (PRESS) localization has been combined with distortionless enhanced polarization transfer (DEPT) in multinuclear MRS to overcome the signal contamination problem in image-selected in vivo spectroscopy (ISIS)-combined DEPT, especially for lipid detection. However, homonuclear proton scalar couplings reduce the DEPT enhancement by modifying the spin coherence distribution under J modulation during proton PRESS localization. Herein, a J-refocused proton PRESS-localized DEPT sequence is presented to obtain simultaneously enhanced and localized signals from a large number of metabolites by in vivo (13) C MRS. The suppression of J modulation during PRESS and the substantial recovery of signal enhancement by J-refocused PRESS-localized DEPT were demonstrated theoretically by product operator formalism, numerically by the spin density matrix simulations for different scalar coupling conditions, and experimentally with a glutamate phantom at various TEs, as well as a colza oil phantom. The application of the sequence for localized detection of saturated and unsaturated fatty acids in the calf bone marrow and skeletal muscle of healthy subjects yielded high signal enhancements simultaneously obtained for all components.

Optimized quantitative magnetic resonance spectroscopy for clinical routine

Several practical obstacles in data handling and evaluation complicate the use of quantitative localized magnetic resonance spectroscopy (qMRS) in clinical routine MR examinations. To overcome these obstacles, a clinically feasible MR pulse sequence protocol based on standard available MR pulse sequences for qMRS has been implemented along with newly added functionalities to the free software package jMRUI-v5.0 to make qMRS attractive for clinical routine. This enables (a) easy and fast DICOM data transfer from the MR console and the qMRS-computer, (b) visualization of combined MR spectroscopy and imaging, (c) creation and network transfer of spectroscopy reports in DICOM format, (d) integration of advanced water reference models for absolute quantification, and (e) setup of databases containing normal metabolite concentrations of healthy subjects. To demonstrate the work-flow of qMRS using these implementations, databases for normal metabolite concentration in different regions of brain tissue were created using spectroscopic data acquired in 55 normal subjects (age range 6-61 years) using 1.5T and 3T MR systems, and illustrated in one clinical case of typical brain tumor (primitive neuroectodermal tumor). The MR pulse sequence protocol and newly implemented software functionalities facilitate the incorporation of qMRS and reference to normal value metabolite concentration data in daily clinical routine.

Soft constraints in nonlinear spectral fitting with regularized lineshape deconvolution

This article presents a novel method for incorporating a priori knowledge into regularized nonlinear spectral fitting as soft constraints. Regularization was recently introduced to lineshape deconvolution as a method for correcting spectral distortions. Here, the deconvoluted lineshape was described by a new type of lineshape model and applied to spectral fitting. The nonlinear spectral fitting was carried out in two steps that were subject to hard constraints and soft constraints, respectively. The hard constraints step provided a starting point and, therefore, only the changes of the relevant variables were constrained in the soft constraints step and incorporated into the linear substeps of the Levenberg-Marquardt algorithm. The method was demonstrated using localized averaged echo time point resolved spectroscopy proton spectroscopy of human brains.

Skeletal muscle ¹H MRSI before and after prolonged exercise. I. muscle specific depletion of intramyocellular lipids

Aim of the study was to determine distribution and depletion patterns of intramyocellular lipids (IMCL) in leg muscles before and after two types of standardized endurance exercise. ¹H-magnetic resonance spectroscopic imaging was performed (1) in the thigh of eight-trained cyclists after exercising on an ergometer for 3 h at 52 ± 8% of maximal speed and (2) in the lower leg of eight-trained runners after exercising on a treadmill for 3 h at 49 ± 3% of maximal workload. Pre-exercise IMCL contents were reduced postexercise in 11 out of 13 investigated upper and lower leg muscles (P < 0.015 for all). A strong linear correlation with a slope of ∼0.5 between pre-exercise IMCL content and IMCL depletion was found. IMCL depletion differed strongly between muscles. Absolute and also relative IMCL reduction was significantly higher in muscles with predominantly slow fibers compared to those with fast fibers. Creatine levels and fiber orientation were stable and unchanged after exercise, while trimethyl-ammonium groups increased. This is presented in the accompanying paper. In conclusion, a systematic comparison of metabolic changes in cross sections of the upper and lower leg was performed. The results imply that pre-exercise IMCL levels determine the degree of IMCL depletion after exercise.

Quantitative proton magnetic resonance spectroscopy and spectroscopic imaging of the brain: a didactic review

This article presents background information related to methodology for estimating brain metabolite concentration from magnetic resonance spectroscopy (MRS) and magnetic resonance spectroscopic imaging measurements of living human brain tissue. It reviews progress related to this methodology, with emphasis placed on progress reported during the past 10 years. It is written for a target audience composed of radiologists and magnetic resonance imaging technologists. It describes in general terms the relationship between MRS signal amplitude and concentration. It then presents an overview of the many practical problems associated with deriving concentration solely from absolute measured signal amplitudes and demonstrates how a various signal calibration approaches can be successfully used. The concept of integrated signal amplitude is presented with examples that are helpful for qualitative reading of MRS data as well as for understanding the methodology used for quantitative measurements. The problems associated with the accurate measurement of individual signal amplitudes in brain spectra having overlapping signals from other metabolites and overlapping nuisance signals from water and lipid are presented. Current approaches to obtaining accurate amplitude estimates with least-squares fitting software are summarized.

Improved initial value estimation for short echo time magnetic resonance spectroscopy spectral analysis using short T2 signal attenuation

Robust spectral analysis of magnetic resonance spectroscopy data frequently uses a spectral model with prior metabolite signal information within a nonlinear least squares optimization algorithm. Starting values for the spectral model greatly influence the final results. Short echo time magnetic resonance spectroscopy contains broad signals that overlap with metabolite signals, complicating the estimation of starting values. We describe a method for more robust initial value estimation using a filter to attenuate short T(2) signal contributions (e.g., macromolecules or residual lipids). The method attenuates signals by truncating early points in the data set. Metabolite peak estimation is simplified by the removal of broad, short T(2) signals, and corrections for metabolite signal truncation are described. Short echo time simulated Monte Carlo data and in vivo data were used to validate the method. Areas for metabolite signals in the Monte Carlo data with singlet (N-acetylaspartate, creatine, choline) and singlet-like (myo-inositol) resonances were estimated within 10% of actual value for various metabolite line widths, signal-to-noise ratios, and underlying broad signal contributions. Initial value estimates of in vivo magnetic resonance spectroscopy data were within 14% of metabolite area ratios relative to the creatine peak fitted using established magnetic resonance spectroscopy spectral analysis software.

Exploiting spatial information to estimate metabolite levels in two-dimensional MRSI of heterogeneous brain lesions

MRSI provides MR spectra from multiple adjacent voxels within a body volume represented as a two- or three-dimensional matrix, allowing the measurement of the distribution of metabolites over this volume. The spectra of these voxels are usually analyzed one by one, without exploiting their spatial context. In this article, we present an advanced metabolite quantification method for MRSI data, in which the available spatial information is considered. A nonlinear least-squares algorithm is proposed in which prior knowledge is included in the form of proximity constraints on the spectral parameters within a grid and optimized starting values. A penalty term that promotes a spatially smooth spectral parameter map is added to the fitting algorithm. This method is adaptive, in the sense that several sweeps through the grid are performed and each solution may tune some hyperparameters at run-time. Simulation studies of MRSI data showed significantly improved metabolite estimates after the inclusion of spatial information. Improved metabolite maps were also demonstrated by applying the method to in vivo MRSI data. Overlapping peaks or peaks of compounds present at low concentration can be better quantified with the proposed method than with single-voxel approaches. The new approach compares favorably against the multivoxel approach embedded in the well-known quantification software LCModel.

A new experimental approach and signal processing scheme for the detection and quantitation of ³¹P brain neurochemicals from in vivo MRS studies using dual tuned (¹H/³¹P) head coil

Brain (31)P-neurometabolites play an important role in energy and membrane metabolism. Unambiguous identification and quantification of these neurochemicals in different brain regions would be a great aid in advancing the understanding of metabolic processes in the nervous system. Phosphomonoester (PME), consisting of phosphoethanolamine (PE) and phosphocholine (PC), is the "building block" for membranes, while phosphodiesters (PDE), consisting of glycerophosphocholine (GPC) and glycerophosphoethanolamine (GPE) metabolites are involved in the membrane breakdown process. In the clinical setting, generating well-resolved spectra for PC, PE, GPC, and GPE could be crucial phospholipids in providing information regarding membrane metabolism. We present here a new experimental approach for generating well-resolved (31)P spectra for PC and PE as well as for GPC, GPE, and other (31)P metabolites. Our results (based on uni-dimensional (1D) and multi-voxel (31)P studies) indicate that an intermediate excitation pulse angle (35°) is best suited to obtain well-resolved PC/PE and GPC/GPE resonance peaks. Our novel signal processing scheme allows generating metabolite maps of different phospholipids include PC/PE and GPC/GPE using the 'time-domain-frequency-domain' method as referred to in the MATLAB programming language.

Mitochondrial capacity is affected by glycemic status in young untrained women with type 1 diabetes but is not impaired relative to healthy untrained women

In this study, we examined whether glycemic status influences aerobic function in women with type 1 diabetes and whether aerobic function is reduced relative to healthy women. To this end, we compared several factors determining aerobic function of 29 young sedentary asymptomatic women (CON) with 9 women of similar age and activity level with type 1 diabetes [DIA, HbA1c range = 6.9–8.2%]. Calf muscle mitochondrial capacity was estimated by 31P-magnetic resonance spectroscopy. Capillarization and muscle fiber oxidative enzyme activity were assessed from vastus lateralis and soleus muscle biopsies. Oxygen uptake and cardiac output were evaluated by ergospirometry and N2O/SF6 rebreathing. Calf muscle mitochondrial capacity was not different between CON and DIA, as indicated by the identical calculated maximal rates of oxidative ATP synthesis [0.0307 (0.0070) vs. 0.0309 (0.0058) s−1, P = 0.930]. Notably, HbA1c was negatively correlated with mitochondrial capacity in DIA ( R2 = 0.475, P = 0.040). Although HbA1c was negatively correlated with cardiac output ( R2 = 0.742, P = 0.013) in DIA, there was no difference between CON and DIA in maximal oxygen consumption [2.17 (0.34) vs. 2.21 (0.32) l/min, P = 0.764], cardiac output [12.1 (1.9) vs. 12.3 (1.8) l/min, P = 0.783], and endurance capacity [532 (212) vs. 471 (119) s, P = 0.475]. There was also no difference between the two groups either in the oxidative enzyme activity or capillary-to-fiber ratio. We conclude that mitochondrial capacity depends on HbA1c in untrained women with type 1 diabetes but is not reduced relative to untrained healthy women.

Noninvasive assessment of exercise-related intramyocellular acetylcarnitine in euglycemia and hyperglycemia in patients with type 1 diabetes using ¹H magnetic resonance spectroscopy: a randomized single-blind crossover study

OBJECTIVE

Intramyocellular acetylcarnitine (IMAC) is involved in exercise-related fuel metabolism. It is not known whether levels of systemic glucose influence IMAC levels in type 1 diabetes.

RESEARCH DESIGN AND METHODS

Seven male individuals with type 1 diabetes performed 120 min of aerobic exercise at 55-60% of Vo(2max) randomly on two occasions (glucose clamped to 5 or 11 mmol/l, identical insulinemia). Before and after exercise, IMAC was detected by ¹H magnetic resonance spectroscopy in musculus vastus intermedius.

RESULTS

Postexercise levels of IMAC were significantly higher than pre-exercise values in euglycemia (4.30 ± 0.54 arbitrary units [a.u.], P < 0.001) and in hyperglycemia (2.44 ± 0.53 a.u., P = 0.01) and differed significantly according to glycemia (P < 0.01). The increase in exercise-related levels of IMAC was significantly higher in euglycemia (3.97 ± 0.45 a.u.) than in hyperglycemia (1.71 ± 0.50 a.u.; P < 0.01).

CONCLUSIONS

The increase in IMAC associated with moderate aerobic exercise in individuals with type 1 diabetes was significantly higher in euglycemia than in hyperglycemia.

Standardized protocol for a depletion of intramyocellular lipids (IMCL)

Intramyocellular lipids (IMCL) are flexible fuel stores that are depleted by physical exercise and replenished by fat intake. IMCL or their degradation products are thought to interfere with insulin signaling thereby contributing to insulin resistance. From a practical point of view it is desirable to deplete IMCL prior to replenishing them. So far, it is not clear for how long and at which intensity subjects have to exercise in order to deplete IMCL. We therefore aimed at developing a standardized exercise protocol that is applicable to subjects over a broad range of exercise capacity and insulin sensitivity and allows measuring reliably reduced IMCL levels.Twelve male subjects, including four diabetes type 2 patients, with wide ranges of exercise capacity (VO(2)peak per total body weight 27.9-55.8 ml x kg(-1) x min(-1)), insulin sensitivity (glucose infusion rate per lean body mass 4.7-15.3 mg x min(-1) x kg(-1)), and BMI (21.7-31.5 kg x m(-2)), respectively, were enrolled. Using (1)H magnetic resonance spectroscopy ((1)H-MRS), IMCL was measured in m.tibialis anterior and m.vastus intermedius before and during a depletion protocol of a week, consisting of a moderate additional physical activity (1 h daily at 60% VO(2)peak) and modest low-fat (10-15%) diet.Absolute IMCL-levels were significantly reduced in both muscles during the first 3 days and stayed constant for the next 3 days of an identical diet/exercise-scheme. These reduced IMCL levels were independent of insulin sensitivity, yet a tendency to lower depleted IMCL levels has been observed in subjects with higher VO(2)peak.The proposed protocol is feasible in subjects with large differences in exercise capacity, insulin sensitivity, and BMI, leading to reduced IMCL levels that neither depend on the exact duration of the depletion protocol nor on insulin sensitivity. This allows for a standardized preparation of IMCL levels either for correlation with other physiological parameters or for replenishment studies.

In-vivo assessment of tissue metabolite levels using 1H MRS and the Electric REference To access In vivo Concentrations (ERETIC) method

Quantitative values of metabolite concentrations in (1)H magnetic resonance spectroscopy have been obtained using the Electric REference To access In vivo Concentrations (ERETIC) method, whereby a synthetic reference signal is injected during the acquisition of spectra. The method has been improved to enable quantification of metabolite concentrations in vivo. Optical signal transmission was used to eliminate random fluctuations in ERETIC signal coupling to the receiver coil due to changes in position of cables and highly dielectric human tissue. Stability and reliability of the signal were tested in vitro, achieving stability with a mean error of 2.83%. Scaling of the signal in variable loading conditions was demonstrated and in-vivo measurements of brain were acquired on a 3T Philips system using a transmit/receive coil. The quantitative brain water and metabolite concentration values are in good agreement with those in the literature.