Water removal in MR spectroscopic imaging with Casorati singular value decomposition.

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.

Model-informed unsupervised deep learning approaches to frequency and phase correction of MRS 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.

Quantification of short echo time MRS signals with improved version of QUantitation based on quantum ESTimation algorithm.

Magnetic resonance spectroscopy offers information about metabolite changes in the organism, which can be used in diagnosis. While short echo time proton spectra exhibit more distinguishable metabolites compared with proton spectra acquired with long echo times, their quantification (and providing estimates of metabolite concentrations) is more challenging. They are hampered by a background signal, which originates mainly from macromolecules (MM) and mobile lipids. An improved version of the quantification algorithm QUantitation based on quantum ESTimation (QUEST), with MM prior knowledge (QUEST-MM), dedicated to proton signals and invoking appropriate prior knowledge on MM, is proposed and tested. From a single acquisition, it enables better metabolite quantification, automatic estimation of the background, and additional automatic quantification of MM components, thus improving its applicability in the clinic. The proposed algorithm may facilitate studies that involve patients with pathological MM in the brain. QUEST-MM and three QUEST-based strategies for quantifying short echo time signals are compared in terms of bias-variance trade-off and Cramér-Rao lower bound estimates. The performances of the methods are evaluated through extensive Monte Carlo studies. In particular, the histograms of the metabolite and MM amplitude distributions demonstrate the performances of the estimators. They showed that QUEST-MM works better than QUEST (Subtract approach) and is a good alternative to QUEST when measured MM signal is unavailable or unsuitable. Quantification with QUEST-MM is shown for 1 H in vivo rat brain signals obtained with the SPECIAL pulse sequence at 9.4 T, and human brain signals obtained, respectively, with STEAM at 4 T and PRESS at 3 T. QUEST-MM is implemented in jMRUI and will be available for public use from version 7.1.

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.

Processing tracking in jMRUI software for magnetic resonance spectra quantitation reproducibility assurance.

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.

Quantum-mechanical simulations for in vivo MR spectroscopy: Principles and possibilities demonstrated with the program NMRScopeB.

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.

From raw data to data-analysis for magnetic resonance spectroscopy--the missing link: jMRUI2XML.

BACKGROUND: Magnetic resonance spectroscopy provides metabolic information about living tissues in a non-invasive way. However, there are only few multi-centre clinical studies, mostly performed on a single scanner model or data format, as there is no flexible way of documenting and exchanging processed magnetic resonance spectroscopy data in digital format. This is because the DICOM standard for spectroscopy deals with unprocessed data. This paper proposes a plugin tool developed for jMRUI, namely jMRUI2XML, to tackle the latter limitation. jMRUI is a software tool for magnetic resonance spectroscopy data processing that is widely used in the magnetic resonance spectroscopy community and has evolved into a plugin platform allowing for implementation of novel features. RESULTS: jMRUI2XML is a Java solution that facilitates common preprocessing of magnetic resonance spectroscopy data across multiple scanners. Its main characteristics are: 1) it automates magnetic resonance spectroscopy preprocessing, and 2) it can be a platform for outputting exchangeable magnetic resonance spectroscopy data. The plugin works with any kind of data that can be opened by jMRUI and outputs in extensible markup language format. Data processing templates can be generated and saved for later use. The output format opens the way for easy data sharing- due to the documentation of the preprocessing parameters and the intrinsic anonymization--for example for performing pattern recognition analysis on multicentre/multi-manufacturer magnetic resonance spectroscopy data. CONCLUSIONS: jMRUI2XML provides a self-contained and self-descriptive format accounting for the most relevant information needed for exchanging magnetic resonance spectroscopy data in digital form, as well as for automating its processing. This allows for tracking the procedures the data has undergone, which makes the proposed tool especially useful when performing pattern recognition analysis. Moreover, this work constitutes a first proposal for a minimum amount of information that should accompany any magnetic resonance processed spectrum, towards the goal of achieving better transferability of magnetic resonance spectroscopy studies.

Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data.

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.

Laterality in functional and metabolic state of the bulbectomised rat brain detected by ASL and 1H MRS: A pilot study.

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.

Magnetic susceptibility and electrical conductivity of metallic dental materials and their impact on MR imaging artifacts.

OBJECTIVES: The aim of this study was to test the hypothesis that dental materials vary significantly in MR-relevant material parameters-magnetic susceptibility and electrical conductivity, and that knowledge of these parameters may be used to estimate the quality of MR imaging in the presence of devices made of such materials. METHODS: Magnetic susceptibility, electrical conductivity and artifacts were evaluated for 45 standardized cylindrical samples of dental alloys and amalgams. Magnetic susceptibility was determined by fitting the phase of gradient-echo MR images to numerically modeled data. Electrical conductivity was determined by standard electrotechnical measurements. Artifact sizes were measured in spin-echo (SE) and gradient-echo (GE) images at 1.5T according to the standards of the American Society for Testing and Materials. RESULTS: It has been confirmed that dental materials differ considerably in their magnetic susceptibility, electrical conductivity and artifacts. For typical dental devices, magnetic susceptibility differences were found of little clinical importance for diagnostic SE/GE imaging of the neck and brain, but significant for orofacial imaging. Short-TE GE imaging has been found possible even in very close distances from dental devices made of amalgams, precious alloys and titanium alloys. Nickel-chromium and cobalt-chromium artifacts were found still acceptable, but large restorations of aluminum bronzes may preclude imaging of the orofacial region. The influence of electrical conductivity on the artifact size was found negligible. SIGNIFICANCE: MR imaging is possible even close to dental devices if they are made of dental materials with low magnetic susceptibility. Not all materials in current use meet this requirement.