Magnetic resonance spectroscopic imaging (MRSI) enables the simultaneous noninvasive acquisition of MR spectra from multiple spatial locations inside the brain. Although 1H-MRSI is increasingly used in the human brain, it is not yet widely applied in the preclinical setting, mostly because of difficulties specifically related to very small nominal voxel size in the rat brain and low concentration of brain metabolites, resulting in low signal-to-noise ratio (SNR). In this context, we implemented a free induction decay 1H-MRSI sequence (1H-FID-MRSI) in the rat brain at 14.1 T. We combined the advantages of 1H-FID-MRSI with the ultra-high magnetic field to achieve higher SNR, coverage, and spatial resolution in the rat brain and developed a custom dedicated processing pipeline with a graphical user interface for Bruker 1H-FID-MRSI: MRS4Brain toolbox. LCModel fit, using the simulated metabolite basis set and in vivo measured MM, provided reliable fits for the data at acquisition delays of 1.30 ms. The resulting Cramér-Rao lower bounds were sufficiently low (< 30%) for eight metabolites of interest (total creatine, N-acetylaspartate, N-acetylaspartate + N-acetylaspartylglutamate, total choline, glutamine, glutamate, myo-inositol, and taurine), leading to highly reproducible metabolic maps. Similar spectral quality and metabolic maps were obtained with one and two averages, with slightly better contrast and brain coverage due to increased SNR in the latter case. Furthermore, the obtained metabolic maps were accurate enough to confirm the previously known brain regional distribution of some metabolites. The acquisitions proved high reproducibility over time. We demonstrated that the increased SNR and spectral resolution at 14.1 T can be translated into high spatial resolution in 1H-FID-MRSI of the rat brain in 13 min using the sequence and processing pipeline described herein. High-resolution 1H-FID-MRSI at 14.1 T provided robust, reproducible, and high-quality metabolic mapping of brain metabolites with minimal technical limitations.
- MeSH
- krysa rodu rattus MeSH
- magnetická rezonanční tomografie metody MeSH
- metabolom MeSH
- mozek * metabolismus diagnostické zobrazování MeSH
- poměr signál - šum MeSH
- potkani Sprague-Dawley MeSH
- potkani Wistar MeSH
- protonová magnetická rezonanční spektroskopie metody MeSH
- zvířata MeSH
- Check Tag
- krysa rodu rattus MeSH
- mužské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
Alterations in tricarboxylic acid (TCA) cycle metabolism are associated with hepatic metabolic disorders. Elevated hepatic acetate concentrations, often attributed to high caloric intake, are recognized as a pivotal factor in the etiology of obesity and metabolic syndrome. Therefore, the assessment of acetate breakdown and TCA cycle activity plays a central role in understanding the impact of diet-induced alterations on liver metabolism. Magnetic resonance-based deuterium metabolic imaging (DMI) could help to unravel the underlying mechanisms involved in disease development and progression, however, the application of conventional deuterated glucose does not lead to substantial enrichment in hepatic glutamine and glutamate. This study aimed to demonstrate the feasibility of DMI for tracking deuterated acetate breakdown via the TCA cycle in lean and diet-induced fatty liver (FL) rats using 3D DMI after an intraperitoneal infusion of sodium acetate-d3 at 9.4T. Localized and nonlocalized liver spectra acquired at 10 time points post-injection over a 130-min study revealed similar intrahepatic acetate uptake in both animal groups (AUCFL = 717.9 ± 131.1 mM▯min-1, AUClean = 605.1 ± 119.9 mM▯min-1, p = 0.62). Metabolic breakdown could be observed in both groups with an emerging glutamine/glutamate (Glx) peak as a downstream metabolic product (AUCFL = 113.6 ± 23.8 mM▯min-1, AUClean = 136.7 ± 41.7 mM▯min-1, p = 0.68). This study showed the viability of DMI for tracking substrate flux through the TCA cycle, underscoring its methodological potential for imaging metabolic processes in the body.
- MeSH
- acetáty metabolismus MeSH
- analýza metabolického toku MeSH
- citrátový cyklus * MeSH
- deuterium * MeSH
- játra * metabolismus diagnostické zobrazování MeSH
- krysa rodu rattus MeSH
- magnetická rezonanční tomografie MeSH
- potkani Sprague-Dawley MeSH
- potkani Wistar MeSH
- zvířata MeSH
- Check Tag
- krysa rodu rattus MeSH
- mužské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
PURPOSE: Water removal is one of the computational bottlenecks in the processing of high-resolution MRSI data. The purpose of this work is to propose an approach to reduce the computing time required for water removal in large MRS data. METHODS: In this work, we describe a singular value decomposition-based approach that uses the partial position-time separability and the time-domain linear predictability of MRSI data to reduce the computational time required for water removal. Our approach arranges MRS signals in a Casorati matrix form, applies low-rank approximations utilizing singular value decomposition, removes residual water from the most prominent left-singular vectors, and finally reconstructs the water-free matrix using the processed left-singular vectors. RESULTS: We have demonstrated the effectiveness of our proposed algorithm for water removal using both simulated and in vivo data. The proposed algorithm encompasses a pip-installable tool ( https://pypi.org/project/CSVD/), available on GitHub ( https://github.com/amirshamaei/CSVD), empowering researchers to use it in future studies. Additionally, to further promote transparency and reproducibility, we provide comprehensive code for result replication. CONCLUSIONS: The findings of this study suggest that the proposed method is a promising alternative to existing water removal methods due to its low processing time and good performance in removing water signals.
PURPOSE: A supervised deep learning (DL) approach for frequency and phase correction (FPC) of MRS data recently showed encouraging results, but obtaining transients with labels for supervised learning is challenging. This work investigates the feasibility and efficiency of unsupervised deep learning-based FPC. METHODS: Two novel deep learning-based FPC methods (deep learning-based Cr referencing and deep learning-based spectral registration), which use a priori physics domain knowledge, are presented. The proposed networks were trained, validated, and evaluated using simulated, phantom, and publicly accessible in vivo MEGA-edited MRS data. The performance of our proposed FPC methods was compared with other generally used FPC methods, in terms of precision and time efficiency. A new measure was proposed in this study to evaluate the FPC method performance. The ability of each of our methods to carry out FPC at varying SNR levels was evaluated. A Monte Carlo study was carried out to investigate the performance of our proposed methods. RESULTS: The validation using low-SNR manipulated simulated data demonstrated that the proposed methods could perform FPC comparably with other methods. The evaluation showed that the deep learning-based spectral registration over a limited frequency range method achieved the highest performance in phantom data. The applicability of the proposed method for FPC of GABA-edited in vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly. CONCLUSIONS: The proposed physics-informed deep neural networks trained in an unsupervised manner with complex data can offer efficient FPC of large MRS data in a shorter time.
OBJECTIVES: Pilot study validating the animal model of depression - the bilateral olfactory bulbectomy in rats - by two nuclear magnetic resonance methods, indirectly detecting the metabolic state of the brain. Furthermore, the study focussed on potential differences in brain laterality. METHODS: Arterial spin labelling assessed cerebral brain flow in prefrontal, sensorimotor, and piriform cortices, nucleus accumbens, hippocampus, thalamus, circle of Willis, and whole brain. Proton magnetic resonance spectroscopy provided information about relative metabolite concentrations in the cortex and hippocampus. RESULTS: Arterial spin labelling found no differences in cerebral perfusion in the group comparison but revealed lateralisation in the thalamus of the control group and the sensorimotor cortex of the bulbectomized rats. Lower Cho/tCr and Cho/NAA levels were found in the right hippocampus in bulbectomized rats. The differences in lateralisation were shown in the hippocampus: mI/tCr in the control group, Cho/NAA, NAA/tCr, Tau/tCr in the model group, and in the cortex: NAA/tCr, mI/tCr in the control group. CONCLUSION: Olfactory bulbectomy affects the neuronal and biochemical profile of the rat brain laterally and, as a model of depression, was validated by two nuclear magnetic resonance methods.
- MeSH
- cholin metabolismus MeSH
- kreatin metabolismus MeSH
- krysa rodu rattus MeSH
- kyselina aspartová metabolismus MeSH
- magnetická rezonanční spektroskopie metody MeSH
- magnetická rezonanční tomografie * MeSH
- mozek * patologie MeSH
- pilotní projekty MeSH
- receptory antigenů T-buněk metabolismus MeSH
- zvířata MeSH
- Check Tag
- krysa rodu rattus MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
PURPOSE: While the recommended analysis method for magnetic resonance spectroscopy data is linear combination model (LCM) fitting, the supervised deep learning (DL) approach for quantification of MR spectroscopy (MRS) and MR spectroscopic imaging (MRSI) data recently showed encouraging results; however, supervised learning requires ground truth fitted spectra, which is not practical. Moreover, this work investigates the feasibility and efficiency of the LCM-based self-supervised DL method for the analysis of MRS data. METHOD: We present a novel DL-based method for the quantification of relative metabolite concentrations, using quantum-mechanics simulated metabolite responses and neural networks. We trained, validated, and evaluated the proposed networks with simulated and publicly accessible in-vivo human brain MRS data and compared the performance with traditional methods. A novel adaptive macromolecule fitting algorithm is included. We investigated the performance of the proposed methods in a Monte Carlo (MC) study. RESULT: The validation using low-SNR simulated data demonstrated that the proposed methods could perform quantification comparably to other methods. The applicability of the proposed method for the quantification of in-vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly. CONCLUSION: The proposed model-constrained deep neural networks trained in a self-supervised manner can offer fast and efficient quantification of MRS and MRSI data. Our proposed method has the potential to facilitate clinical practice by enabling faster processing of large datasets such as high-resolution MRSI datasets, which may have thousands of spectra.
- MeSH
- deep learning * MeSH
- lidé MeSH
- magnetická rezonanční spektroskopie MeSH
- magnetická rezonanční tomografie metody MeSH
- mozek diagnostické zobrazování metabolismus MeSH
- neuronové sítě (počítačové) MeSH
- počítačové zpracování obrazu metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
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.
- MeSH
- dospělí MeSH
- konsensus * MeSH
- lidé středního věku MeSH
- lidé MeSH
- lipidy chemie MeSH
- magnetická rezonanční tomografie MeSH
- makromolekulární látky metabolismus MeSH
- metabolom MeSH
- mladý dospělý MeSH
- mozek diagnostické zobrazování MeSH
- počítačové zpracování signálu MeSH
- protonová magnetická rezonanční spektroskopie * MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- teoretické modely MeSH
- znalecký posudek * MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
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.
- MeSH
- krysa rodu rattus MeSH
- makromolekulární látky metabolismus MeSH
- mozek - chemie * MeSH
- mozek * diagnostické zobrazování metabolismus MeSH
- zvířata MeSH
- Check Tag
- krysa rodu rattus MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
Current possibilities and limitations of the simulation of in vivo magnetic resonance spectroscopic signals are demonstrated from the point of view of a simulation software user as well as its programmer. A brief review of the quantum-mechanical background addresses the specific needs of simulation implementation and in vivo MR spectroscopy in general. Practical application examples demonstrate how flexible simulation software, such as NMRScopeB, can be utilized not only for the preparation of metabolite basis signals for quantification of metabolite concentrations, but also in pulse sequence development, assessment of artifacts and analyzing mechanism leading to unexpected signal phenomena.
BACKGROUND: Proton magnetic resonance spectroscopy is a non-invasive measurement technique which provides information about concentrations of up to 20 metabolites participating in intracellular biochemical processes. In order to obtain any metabolic information from measured spectra a processing should be done in specialized software, like jMRUI. The processing is interactive and complex and often requires many trials before obtaining a correct result. This paper proposes a jMRUI enhancement for efficient and unambiguous history tracking and file identification. RESULTS: A database storing all processing steps, parameters and files used in processing was developed for jMRUI. The solution was developed in Java, authors used a SQL database for robust storage of parameters and SHA-256 hash code for unambiguous file identification. The developed system was integrated directly in jMRUI and it will be publically available. A graphical user interface was implemented in order to make the user experience more comfortable. The database operation is invisible from the point of view of the common user, all tracking operations are performed in the background. CONCLUSIONS: The implemented jMRUI database is a tool that can significantly help the user to track the processing history performed on data in jMRUI. The created tool is oriented to be user-friendly, robust and easy to use. The database GUI allows the user to browse the whole processing history of a selected file and learn e.g. what processing lead to the results, where the original data are stored, to obtain the list of all processing actions performed on spectra.
