Which prior knowledge? Quantification of in vivo brain 13C MR spectra following 13C glucose infusion using AMARES

PyAMARES, an Open-Source Python Library for Fitting Magnetic Resonance Spectroscopy Data

Background/Objectives: Magnetic resonance spectroscopy (MRS) is a valuable tool for studying metabolic processes in vivo. While numerous quantification methods exist, the advanced method for accurate, robust, and efficient spectral fitting (AMARES) is among the most used. This study introduces pyAMARES, an open-source Python implementation of AMARES, addressing the need for a flexible, user-friendly, and versatile MRS quantification tool within the Python ecosystem. Methods: PyAMARES was developed as a Python library, implementing the AMARES algorithm with additional features such as multiprocessing capabilities and customizable objective functions. The software was validated against established AMARES implementations (OXSA and jMRUI) using both simulated and in vivo MRS data. Monte Carlo simulations were conducted to assess robustness and accuracy across various signal-to-noise ratios and parameter perturbations. Results: PyAMARES utilizes spreadsheet-based prior knowledge and fitting parameter settings, enhancing flexibility and ease of use. It demonstrated comparable performance to existing software in terms of accuracy, precision, and computational efficiency. In addition to conventional AMARES fitting, pyAMARES supports fitting without prior knowledge, frequency-selective AMARES, and metabolite residual removal from mobile macromolecule (MM) spectra. Utilizing multiple CPU cores significantly enhances the performance of pyAMARES. Conclusions: PyAMARES offers a robust, flexible, and user-friendly solution for MRS quantification within the Python ecosystem. Its open-source nature, comprehensive documentation, and integration with popular data science tools enhance reproducibility and collaboration in MRS research. PyAMARES bridges the gap between traditional MRS fitting methods and modern machine learning frameworks, potentially accelerating advancements in metabolic studies and clinical applications.

ProFit-1D-A 1D fitting software and open-source validation data sets

PURPOSE

Accurate and precise MRS fitting is crucial for metabolite concentration quantification of ¹ H-MRS spectra. LCModel, a spectral fitting software, has shown to have certain limitations to perform advanced spectral fitting by previous literature. Herein, we propose an open-source spectral fitting algorithm with adaptive spectral baseline determination and more complex cost functions.

THEORY

The MRS spectra are characterized by several parameters, which reflect the environment of the contributing metabolites, properties of the acquisition sequence, or additional disturbances. Fitting parameters should accurately describe these parameters. Baselines are also a major contributor to MRS spectra, in which smoothness of the spline baselines used for fitting can be adjusted based on the properties of the spectra. Three different cost functions used for the minimization problem were also investigated.

METHODS

The newly developed ProFit-1D fitting algorithm is systematically evaluated for simulations of several types of possible in vivo parameter variations. Although accuracy and precision are tested with simulated spectra, spectra measured in vivo at 9.4 T are used for testing precision using subsets of averages. ProFit-1D fitting results are also compared with LCModel.

RESULTS

Both ProFit-1D and LCModel fitted the spectra well with induced parameter and baseline variations. ProFit-1D proved to be more accurate than LCModel for simulated spectra. However, LCModel showed a somewhat increased precision for some spectral simulations and for in vivo data.

CONCLUSION

The open-source ProFit-1D fitting algorithm demonstrated high accuracy while maintaining precise metabolite concentration quantification. Finally, through the newly proposed cost functions, new ways to improve fitting were shown.

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.

Magnetic resonance spectroscopy in the rodent brain: Experts' consensus recommendations

In vivo MRS is a non-invasive measurement technique used not only in humans, but also in animal models using high-field magnets. MRS enables the measurement of metabolite concentrations as well as metabolic rates and their modifications in healthy animals and disease models. Such data open the way to a deeper understanding of the underlying biochemistry, related disturbances and mechanisms taking place during or prior to symptoms and tissue changes. In this work, we focus on the main preclinical 1H, 31P and 13C MRS approaches to study brain metabolism in rodent models, with the aim of providing general experts' consensus recommendations (animal models, anesthesia, data acquisition protocols). An overview of the main practical differences in preclinical compared with clinical MRS studies is presented, as well as the additional biochemical information that can be obtained in animal models in terms of metabolite concentrations and metabolic flux measurements. The properties of high-field preclinical MRS and the technical limitations are also described.

OXSA: An open-source magnetic resonance spectroscopy analysis toolbox in MATLAB

In vivo magnetic resonance spectroscopy provides insight into metabolism in the human body. New acquisition protocols are often proposed to improve the quality or efficiency of data collection. Processing pipelines must also be developed to use these data optimally. Current fitting software is either targeted at general spectroscopy fitting, or for specific protocols. We therefore introduce the MATLAB-based OXford Spectroscopy Analysis (OXSA) toolbox to allow researchers to rapidly develop their own customised processing pipelines. The toolbox aims to simplify development by: being easy to install and use; seamlessly importing Siemens Digital Imaging and Communications in Medicine (DICOM) standard data; allowing visualisation of spectroscopy data; offering a robust fitting routine; flexibly specifying prior knowledge when fitting; and allowing batch processing of spectra. This article demonstrates how each of these criteria have been fulfilled, and gives technical details about the implementation in MATLAB. The code is freely available to download from https://github.com/oxsatoolbox/oxsa.

New technologies - new insights into the pathogenesis of hepatic encephalopathy

Hepatic encephalopathy (HE) is a neuropsychiatric syndrome which frequently accompanies acute or chronic liver disease. It is characterized by a variety of symptoms of different severity such as cognitive deficits and impaired motor functions. Currently, HE is seen as a consequence of a low grade cerebral oedema associated with the formation of cerebral oxidative stress and deranged cerebral oscillatory networks. However, the pathogenesis of HE is still incompletely understood as liver dysfunction triggers exceptionally complex metabolic derangements in the body which need to be investigated by appropriate technologies. This review summarizes technological approaches presented at the ISHEN conference 2014 in London which may help to gain new insights into the pathogenesis of HE. Dynamic in vivo ¹³C nuclear magnetic resonance spectroscopy was performed to analyse effects of chronic liver failure in rats on brain energy metabolism. By using a genomics approach, microRNA expression changes were identified in plasma of animals with acute liver failure which may be involved in interorgan interactions and which may serve as organ-specific biomarkers for tissue damage during acute liver failure. Genomics were also applied to analyse glutaminase gene polymorphisms in patients with liver cirrhosis indicating that haplotype-dependent glutaminase activity is an important pathogenic factor in HE. Metabonomics represents a promising approach to better understand HE, by capturing the systems level metabolic changes associated with disease in individuals, and enabling monitoring of metabolic phenotypes in real time, over a time course and in response to treatment, to better inform clinical decision making. Targeted fluxomics allow the determination of metabolic reaction rates thereby discriminating metabolite level changes in HE in terms of production, consumption and clearance.

Experimental strategies for in vivo13C NMR spectroscopy

In vivo carbon-13 (¹³C) MRS opens unique insights into the metabolism of intact organisms, and has led to major advancements in the understanding of cellular metabolism under normal and pathological conditions in various organs such as skeletal muscles, the heart, the liver and the brain. However, the technique comes at the expense of significant experimental difficulties. In this review we focus on the experimental aspects of non-hyperpolarized ¹³C MRS in vivo. Some of the enrichment strategies which have been proposed so far are described; the various MRS acquisition paradigms to measure ¹³C labeling are then presented. Finally, practical aspects of ¹³C spectral quantification are discussed.

Refined Analysis of Brain Energy Metabolism Using In Vivo Dynamic Enrichment of 13C Multiplets

Carbon-13 nuclear magnetic resonance spectroscopy in combination with the infusion of ¹³C-labeled precursors is a unique approach to study in vivo brain energy metabolism. Incorporating the maximum information available from in vivo localized ¹³C spectra is of importance to get broader knowledge on cerebral metabolic pathways. Metabolic rates can be quantitatively determined from the rate of ¹³C incorporation into amino acid neurotransmitters such as glutamate and glutamine using suitable mathematical models. The time course of multiplets arising from ¹³C-¹³C coupling between adjacent carbon atoms was expected to provide additional information for metabolic modeling leading to potential improvements in the estimation of metabolic parameters.

The aim of the present study was to extend two-compartment neuronal/glial modeling to include dynamics of ¹³C isotopomers available from fine structure multiplets in ¹³C spectra of glutamate and glutamine measured in vivo in rats brain at 14.1 T, termed bonded cumomer approach. Incorporating the labeling time courses of ¹³C multiplets of glutamate and glutamine resulted in elevated precision of the estimated fluxes in rat brain as well as reduced correlations between them.

Metabolic Modeling of Dynamic (13)C NMR Isotopomer Data in the Brain In Vivo: Fast Screening of Metabolic Models Using Automated Generation of Differential Equations

Most current brain metabolic models are not capable of taking into account the dynamic isotopomer information available from fine structure multiplets in (13)C spectra, due to the difficulty of implementing such models. Here we present a new approach that allows automatic implementation of multi-compartment metabolic models capable of fitting any number of (13)C isotopomer curves in the brain. The new automated approach also makes it possible to quickly modify and test new models to best describe the experimental data. We demonstrate the power of the new approach by testing the effect of adding separate pyruvate pools in astrocytes and neurons, and adding a vesicular neuronal glutamate pool. Including both changes reduced the global fit residual by half and pointed to dilution of label prior to entry into the astrocytic TCA cycle as the main source of glutamine dilution. The glutamate-glutamine cycle rate was particularly sensitive to changes in the model.

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.