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.

In vivo macromolecule signals in rat brain 1 H-MR spectra at 9.4T: Parametrization, spline baseline estimation, and T2 relaxation times

PURPOSE

Reliable detection and fitting of macromolecules (MM) are crucial for accurate quantification of brain short-echo time (TE) ¹ H-MR spectra. An experimentally acquired single MM spectrum is commonly used. Higher spectral resolution at ultra-high field (UHF) led to increased interest in using a parametrized MM spectrum together with flexible spline baselines to address unpredicted spectroscopic components. Herein, we aimed to: (1) implement an advanced methodological approach for post-processing, fitting, and parametrization of 9.4T rat brain MM spectra; (2) assess the concomitant impact of the LCModel baseline and MM model (ie, single vs parametrized); and (3) estimate the apparent T₂ relaxation times for seven MM components.

METHODS

A single inversion recovery sequence combined with advanced AMARES prior knowledge was used to eliminate the metabolite residuals, fit, and parametrize 10 MM components directly from 9.4T rat brain in vivo ¹ H-MR spectra at different TEs. Monte Carlo simulations were also used to assess the concomitant influence of parametrized MM and DKNTMN parameter in LCModel.

RESULTS

A very stiff baseline (DKNTMN ≥ 1 ppm) in combination with a single MM spectrum led to deviations in metabolite concentrations. For some metabolites the parametrized MM showed deviations from the ground truth for all DKNTMN values. Adding prior knowledge on parametrized MM improved MM and metabolite quantification. The apparent T₂ ranged between 12 and 24 ms for seven MM peaks.

CONCLUSION

Moderate flexibility in the spline baseline was required for reliable quantification of real/experimental spectra based on in vivo and Monte Carlo data. Prior knowledge on parametrized MM improved MM and metabolite quantification.

Contribution of macromolecules to brain 1 H MR spectra: Experts' consensus recommendations

Proton MR spectra of the brain, especially those measured at short and intermediate echo times, contain signals from mobile macromolecules (MM). A description of the main MM is provided in this consensus paper. These broad peaks of MM underlie the narrower peaks of metabolites and often complicate their quantification but they also may have potential importance as biomarkers in specific diseases. Thus, separation of broad MM signals from low molecular weight metabolites enables accurate determination of metabolite concentrations and is of primary interest in many studies. Other studies attempt to understand the origin of the MM spectrum, to decompose it into individual spectral regions or peaks and to use the components of the MM spectrum as markers of various physiological or pathological conditions in biomedical research or clinical practice. The aim of this consensus paper is to provide an overview and some recommendations on how to handle the MM signals in different types of studies together with a list of open issues in the field, which are all summarized at the end of the paper.

Longitudinal quantification of metabolites and macromolecules reveals age- and sex-related changes in the healthy Fischer 344 rat brain

Normal aging is associated with numerous biological changes, including altered brain metabolism and tissue chemistry. In vivo characterization of the neurochemical profile during aging is possible using magnetic resonance spectroscopy, a powerful noninvasive technique capable of quantifying brain metabolites involved in physiological processes that become impaired with age. A prominent macromolecular signal underlies those of brain metabolites and is particularly visible at high fields; parameterization of this signal into components improves quantification and expands the number of biomarkers comprising the neurochemical profile. The present study reports, for the first time, the simultaneous absolute quantification of brain metabolites and individual macromolecules in aging male and female Fischer 344 rats, measured longitudinally using proton magnetic resonance spectroscopy at 7 T. We identified age- and sex-related changes in neurochemistry, with prominent differences in metabolites implicated in anaerobic energy metabolism, antioxidant defenses, and neuroprotection, as well as numerous macromolecule changes. These findings contribute to our understanding of the neurobiological processes associated with healthy aging, critical for the proper identification and management of pathologic aging trajectories. This article is part of the Virtual Special Issue titled COGNITIVE NEUROSCIENCE OF HEALTHY AND PATHOLOGICAL AGING. The full issue can be found on ScienceDirect athttps://www.sciencedirect.com/journal/neurobiology-of-aging/special-issue/105379XPWJP.

Separation of Metabolites and Macromolecules for Short-TE 1H-MRSI Using Learned Component-Specific Representations

Short-echo-time (TE) proton magnetic resonance spectroscopic imaging (MRSI) allows for simultaneously mapping a number of molecules in the brain, and has been recognized as an important tool for studying in vivo biochemistry in various neuroscience and disease applications. However, separation of the metabolite and macromolecule (MM) signals present in the short-TE data with significant spectral overlaps remains a major technical challenge. This work introduces a new approach to solve this problem by integrating imaging physics and representation learning. Specifically, a mixed unsupervised and supervised learning-based strategy was developed to learn the metabolite and MM-specific low-dimensional representations using deep autoencoders. A constrained reconstruction formulation is proposed to integrate the MRSI spatiospectral encoding model and the learned representations as effective constraints for signal separation. An efficient algorithm was developed to solve the resulting optimization problem with provable convergence. Simulation and experimental results have been obtained to demonstrate the component-specific representation power of the learned models and the capability of the proposed method in separating metabolite and MM signals for practical short-TE [Formula: see text]-MRSI data.

Deep learning-based target metabolite isolation and big data-driven measurement uncertainty estimation in proton magnetic resonance spectroscopy of the brain

PURPOSE

The aim of this study was to develop a method for metabolite quantification with simultaneous measurement uncertainty estimation in deep learning-based proton magnetic resonance spectroscopy (¹ H-MRS).

METHODS

The reliability of metabolite quantification depends on signal-to-noise ratio (SNR), linewidth, and degree of spectral overlap (DSO), and therefore knowledge about these factors may be utilized in measurement uncertainty estimation in deep learning-based ¹ H-MRS. While SNR and linewidth are typically estimated from a representative singlet, DSO needs to be estimated metabolite-specifically. We developed convolutional neural networks (CNNs) capable of isolating target metabolite signal on simulated rat brain spectra at 9.4T, such that, in addition to metabolite content, the signal-to-background ratio (SBR) as a quantitative metric of DSO can be estimated directly from CNN-output for each metabolite. The CNN-predicted SBR was adjusted according to its pre-defined relationship to the ground-truth SBR by exploiting the big spectral data (N = 80 000), and used for measurement uncertainty estimation together with the SNR and linewidth from the CNN-input spectrum. The proposed method was tested first on the simulated spectra in comparison with LCModel and jMRUI and further on in vivo spectra.

RESULTS

The proposed method outperformed LCModel and jMRUI in both quantitative accuracy and measurement uncertainty estimation. Using in vivo data, the metabolite concentrations from the proposed method were close to the reported ranges with the measurement uncertainty of glutamine, glutamate, myo-inositol, N-acetylaspartate, and Tau less than 10%.

CONCLUSION

The proposed method may be used for metabolite quantification with measurement uncertainty estimation in rat brain at 9.4T by exploiting the spectral isolation capability of the CNNs and the availability of big spectral data.

In vivo MRS measurement of 2-hydroxyglutarate in patient-derived IDH-mutant xenograft mouse models versus glioma patients

PURPOSE

To generate a preclinical model of isocitrate dehydrogenase (IDH) mutant gliomas from glioma patients and design a MRS method to test the compatibility of 2-hydroxyglutarate (2HG) production between the preclinical model and patients.

METHODS

Five patient-derived xenograft (PDX) mice were generated from two glioma patients with IDH1 R132H mutation. A PRESS sequence was tailored at 9.4 T, with computer simulation and phantom analyses, for improving 2HG detection in mice. 2HG and other metabolites in the PDX mice were measured using the optimized MRS at 9.4 T and compared with 3 T MRS measurements of the metabolites in the parental-tumor patients. Spectral fitting was performed with LCModel using in-house basis spectra. Metabolite levels were quantified with reference to water.

RESULTS

The PRESS TE was optimized to be 96 ms, at which the 2HG 2.25 ppm signal was narrow and inverted, thereby leading to unequivocal separation of the 2HG resonance from adjacent signals from other metabolites. The optimized MRS provided precise detection of 2HG in mice compared to short-TE MRS at 9.4 T. The 2HG estimates in PDX mice were in excellent agreement with the 2HG measurements in the patients.

CONCLUSION

The similarity of 2HG production between PDX models and parental-tumor patients indicates that PDX tumors retain the parental IDH metabolic fingerprint and can serve as a preclinical model for improving our understanding of the IDH-mutation associated metabolic reprogramming.

Simultaneous mapping of metabolites and individual macromolecular components via ultra-short acquisition delay 1 H MRSI in the brain at 7T

Purpose

Short‐echo‐time proton MR spectra at 7T feature nine to 10 distinct macromolecule (MM) resonances that overlap with the signals of metabolites. Typically, a metabolite‐nulled in vivo MM spectrum is included in the quantification`s prior knowledge to provide unbiased metabolite quantification. However, this MM model may fail if MMs are pathologically altered. In addition, information about the individual MM peaks is lost. In this study, we aimed to create an improved MM model by parameterization of the in vivo MM spectrum into individual components, and to use this new model to quantify free induction decay MR spectroscopic imaging (FID‐MRSI) data.

Methods

The measured in vivo MM spectrum was parameterized using advanced method for accurate, robust, and efficient spectral fitting (AMARES) and Hankel‐Lanczos singular value decomposition algorithms from which six different MM models were derived. Soft constraints were applied to avoid over‐parameterization. All MM models were combined with simulated metabolite spectra to form complete basis sets. FID‐MRSI data from 14 healthy volunteers were quantified via LCModel, and the results were compared between all basis sets.

Results

The MM model using nine individual AMARES‐parameterized MM components with additional soft constraints achieved the most reliable results. Nine MMs and seven metabolites were mapped simultaneously over the whole slice.

Conclusion

The proposed MM model may facilitate studies that involve patients with pathologically altered MMs. Magn Reson Med 79:1231–1240, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.