Introduction: Ultra-high-field magnetic resonance (MR) systems (7 T and 9.4 T) offer the ability to probe human brain metabolism with enhanced precision. Here, we present the preliminary findings from 3D MR spectroscopic imaging (MRSI) of the human brain conducted with the world's first 10.5 T whole-body MR system. Methods: Employing a custom-built 16-channel transmit and 80-channel receive MR coil at 10.5 T, we conducted MRSI acquisitions in six healthy volunteers to map metabolic compounds in the human cerebrum in vivo. Three MRSI protocols with different matrix sizes and scan times (4.4 × 4.4 × 4.4 mm³: 10 min, 3.4 × 3.4 × 3.4 mm³: 15 min, and 2.75×2.75×2.75 mm³: 25 min) were tested. Concentric ring trajectories were utilized for time-efficient encoding of a spherical 3D k-space with ∼4 kHz spectral bandwidth. B0/B1 shimming was performed based on respective field mapping sequences and anatomical T1-weighted MRI were obtained. Results: By combining the benefits of an ultra-high-field system with the advantages of free-induction-decay (FID-)MRSI, we present the first metabolic maps acquired at 10.5 T in the healthy human brain at both high (voxel size of 4.4³ mm³) and ultra-high (voxel size of 2.75³ mm³) isotropic spatial resolutions. Maps of 13 metabolic compounds (aspartate, choline compounds and creatine + phosphocreatine, γ-aminobutyric acid (GABA), glucose, glutamine, glutamate, glutathione, myo-inositol, scyllo-inositol, N-acetylaspartate (NAA), N-acetylaspartylglutamate (NAAG), taurine) and macromolecules were obtained individually. The spectral quality was outstanding in the parietal and occipital lobes, but lower in other brain regions such as the temporal and frontal lobes. The average total NAA (tNAA = NAA + NAAG) signal-to-noise ratio over the whole volume of interest was 12.1± 8.9 and the full width at half maximum of tNAA was 24.7± 9.6 Hz for the 2.75 × 2.75 × 2.75 mm³ resolution. The need for an increased spectral bandwidth in combination with spatio-spectral encoding imposed significant challenges on the gradient system, but the FID approach proved very robust to field inhomogeneities of ∆B0 = 45 ± 38 Hz (frequency offset ± spatial STD) and B1+ = 65 ± 11° within the MRSI volume of interest. Discussion: These preliminary findings highlight the potential of 10.5 T MRSI as a powerful imaging tool for probing cerebral metabolism. By providing unprecedented spatial and spectral resolution, this technology could offer a unique view into the metabolic intricacies of the human brain, but further technical developments will be necessary to optimize data quality and fully leverage the capabilities of 10.5 T MRSI.

Approximately one-third of people with epilepsy (PWE) remain drug-resistant. In these cases, surgical resection of the epileptogenic zone may significantly reduce or eliminate seizures. Sur-gery necessitates precise delineation of the epileptogenic zone which proves especially challeng-ing in the 20% of PWE that remain MRI-negative. To this end, the purpose of this study was to analyze the feasibility and robustness of ultra-high-field MRSI in identifying and characterizing pathologies in focal epilepsy. In addition, the relationship of glutamate and glutamine was evalu-ated in the epileptogenic zone (EZ) Fifty-six people with focal epilepsy were prospectively measured using 7T concentric ring tra-jectory direct acquisition of free-induction-decay MRSI which generated whole-brain metabolic maps with an isotropic resolution of 3.4mm3. After exclusion criteria were applied, we assessed metabolite ratios in 29 lesional- and MRI-negative PWE. In the lesional group, metabolic alterations in the suspected EZ were present in 86.7% of maps normalized to NAA, whereas this was reduced to 80% in creatine ratios. Metabolites with the highest stability in the lesional group included myo-inositol and choline, increased in 92.3%. In MRI-negative patients, changes were heterogeneous and less circumscribed, with a detection rate of 57.1%. We also observed a tendency towards an inverse relationship of glutamate to glu-tamine in the EZ, with relative increases of glutamine in PWE with lower seizure frequencies, contrasting relative glutamate increases in higher seizure frequencies. Our preliminary analysis suggests that 7T CRT-FID MRSI shows promise not only in identify-ing metabolic alterations in focal epilepsy but may also provide insights into disease pathomech-anisms. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was funded in whole, or in part, by the Austrian Science Fund (FWF) grant 10.55776/KLI1121. We were further supported by the Medical Scientific Fund of the Mayor of the Federal Capital Vienna (Project Number 21186). The financial support by the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and De-velopment, and the Christian Doppler Research Association, is gratefully acknowledged. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The institutional board of review of the Medical University of Vienna gave ethical approval for this work (EK 1039/2020). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript

This paper investigated the correlation between magnetic resonance spectroscopic imaging (MRSI) and magnetic resonance fingerprinting (MRF) in glioma patients by comparing neuro-oncological markers obtained from MRSI to T1/T2 maps from MRF. Data from 12 consenting patients with gliomas were analyzed by defining hotspots for T1, T2, and various metabolic ratios, and comparing them using Sørensen–Dice similarity coefficients (DSCs) and the distances between their centers of intensity (COIDs). The median DSCs between MRF and the tumor segmentation were 0.73 (T1) and 0.79 (T2). The DSCs between MRSI and MRF were the highest for Gln/tNAA (T1: 0.75, T2: 0.80, tumor: 0.78), followed by Gly/tNAA (T1: 0.57, T2: 0.62, tumor: 0.54) and tCho/tNAA (T1: 0.61, T2: 0.58, tumor: 0.45). The median values in the tumor hotspot were T1 = 1724 ms, T2 = 86 ms, Gln/tNAA = 0.61, Gly/tNAA = 0.28, Ins/tNAA = 1.15, and tCho/tNAA = 0.48, and, in the peritumoral region, were T1 = 1756 ms, T2 = 102 ms, Gln/tNAA = 0.38, Gly/tNAA = 0.20, Ins/tNAA = 1.06, and tCho/tNAA = 0.38, and, in the NAWM, were T1 = 950 ms, T2 = 43 ms, Gln/tNAA = 0.16, Gly/tNAA = 0.07, Ins/tNAA = 0.54, and tCho/tNAA = 0.20. The results of this study constitute the first comparison of 7T MRSI and 3T MRF, showing a good correspondence between these methods.

BackgroundTo date, multiple advanced magnetic resonance imaging (MRI) methods beyond conventional qualitative structural imaging for the diagnosis, prognosis, and treatment follow-up of glioma have demonstrated their utility for clinical studies. However, these methods often rely on complex off-scanner processing to yield the most information and to extract quantitative biomarkers, limiting their practical use for studies, as well as their clinical translation.While community-driven software solutions exist for these advanced MRI methods, many aspiring clinical researchers face challenges in acquiring the necessary knowledge to effectively apply these tools. This guide, an initiative of the Glioma MR imaging 2.0 network (GliMR), aims to provide an overview of existing solutions, communities, and repositories with the ultimate goal of enabling standardization, open science, and reproducible quantitative imaging studies of gliomas. Yet, most of the reviewed tools and approaches to image data analyses may also be used in the context of studies on diseases other than glioma.ContentThis guide summarizes the state-of-the-art processing software solutions and the repositories/communities for the following advanced MRI methods: DSC; DCE; ASL; diffusion MRI; relaxometry; MRF; MRS; CEST; SWI; QSM; MRE; and task-based and resting-state fMRI. For each of those, after a short introduction about the method and output parameters, the required and recommended image processing steps and quality control measures are described, and we point to further literature for more details. In addition, an overview of openly available software tools that provide these functionalities for MRI processing and exemplify workflows is given. Wherever possible, the readers are guided toward existing inventories, repositories, and communities, which offer not only a collection of these tools, but also more in-depth guidance. Each part concludes with an appraisal of the estimated required expertise and future development needs.ConclusionThis guide provides an extensive overview of the currently available processing tools that can help aspiring clinical researchers to obtain high-quality reproducible imaging data from advanced MRI scans of gliomas. While GliMR n is focused on glioma research, this guide will also be helpful for other clinical neuroimaging topics as general processing steps may not be specific to glioma only.

Motivation: Molecular and pathological diagnosis requires fresh tissues. It is important to achieve precise sampling and preservation of the tumor is for maintaining the tumor integrity. MRSI can accurately identify tumor hotspots and can be used for obtaining high quality samples. Goal(s): Develop MRSI pipeline for precise intraoperative tumor sampling. Approach: We processed 7T MRSI metabolic ratio maps and identified the tumor hotspots. We then correlated them with their quantitatively analyzed metabolic profiles. Results: We were able to achieve high quality tumor samples based on our 7T MRSI maps. Impact: Due to high resolution 7T metabolic ratio maps we could identify and define tumor hotspots which resulted in precise tumor sampling. We were also able to preserve the tissue specimens and obtain high quality results.

This guide summarizes the state-of-the-art processing software solutions and the repositories/communities for the following advanced MRI methods: DSC; DCE; ASL; diffusion MRI; relaxometry; MRF; MRS; CEST; SWI; QSM; MRE; and task-based and resting-state fMRI. For each of those, after a short introduction about the method and output parameters, the required and recommended image processing steps and quality control measures are described, and we point to further literature for more details. In addition, an overview of openly available software tools that provide these functionalities for MRI processing and exemplify workflows is given. Wherever possible, the readers are guided toward existing inventories, repositories, and communities, which offer not only a collection of these tools, but also more in-depth guidance. Each part concludes with an appraisal of the estimated required expertise and future development needs.

Abstract Objectives Diffuse gliomas, due to their infiltrative properties, still lack effective treatment options. Recent research indicates that infiltration, malignancy, and symptoms such as epilepsy are related to synaptic connections between infiltrating glioma cells and cytotoxic levels of glutamate release. We previously showed that high-resolution 7T magnetic resonance spectroscopic imaging (MRSI) can resolve metabolic heterogeneities in gliomas. With this study, we evaluated 7T MRSI-derived glutamate (Gln) and glutamine (Glu) ratio maps for their use in defining infiltrative tumor activity in the peritumoral region. Materials and Methods We analyzed 7T MRSI scans of 36 patients with low- and high-grade gliomas. Within the visible tumor and a peritumoral shell, we calculated medians and Dice similarity coefficients (DSC) for nine metabolic ratios with and without hotspot thresholding and evaluated their correlation to and statistical significance between clinical parameters (e.g., tumor-associated epilepsy, IDH status, grade). Results The Glu/tCr (total creatine) median was significantly higher in the peritumoral VOI (1.13) compared to the tumor (0.92) and normal-appearing white matter (NAWM, 0.87), while the Gln/tCr median was highest in the tumor (0.77, vs 0.44 peritumoral and 0.33 in NAWM, all significantly different). Glu/tCho (total choline) was significantly higher in the peritumor as well (3.44 vs 2.23 tumoral and 2.06 in NAWM). Peritumoral DSCs for Glu/tCr and Gln/tCr hotspots were comparable (0.53 to 0.51). Peritumoral Gln/Glu was significantly different between patients with and without tumor-associated epilepsy, and intratumoral (Glu+Gln)/tCr was significantly different between IDH mutation and wildtype. IDH mutation correlated negatively with the intratumoral (Glu+Gln)/tCr median (-0.53) and high grade correlated with intratumoral Glx/tNAA, Glx/tCr, and Gln/tCr medians (0.50/0.53/0.58). Conclusions 7T MRSI can not only map relevant metabolic information in the structurally visible tumor volume, but also detect infiltration in the peritumoral area. Gln and Glu are candidates for the development of presurgical imaging and treatment monitoring. ### Competing Interest Statement Julia Furtner has received honoraria for lectures and consultation from the following for-profit companies: Novartis; Seagen; Sanova; and Servier. Matthias Preusser has received honoraria for lectures, consultation or advisory board participation from the following for-profit companies: Bayer; Bristol-Myers Squibb; Novartis; Gerson Lehrman Group (GLG); CMC Contrast; GlaxoSmithKline; Mundipharma; Roche; BMJ Journals; MedMedia; Astra Zeneca; AbbVie; Lilly; Medahead; Daiichi Sankyo; Sanofi; Merck Sharp & Dohme; Tocagen; Adastra; Gan & Lee Pharmaceuticals; Servier; Miltenyi; Boehringer-Ingelheim; Telix; Medscape; and OncLive. ### Funding Statement This research was funded in whole, or in part, by the Austrian Science Fund (FWF) grants 10.55776/KLI646 and 10.55776/KLI1089. For open access purposes, the author has applied a CC BY public copyright license to any author-accepted manuscript version arising from this submission. Further funding was received from the City of Vienna Fund for Innovative Interdisciplinary Cancer Research project 23088. The financial support by the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development, and the Christian Doppler Research Association, is gratefully acknowledged. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Institutional Review Board of the Medical University of Vienna gave ethical approval for this work (study number 1991/2018). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Image data produced in the present study are available upon reasonable request to the authors after institutional data clearing house approval. Derived numerical values are included in the supplementary data.

Abstract Introduction With the application of high-resolution 3D 7 Tesla Magnetic Resonance Spectroscopy Imaging (MRSI) in high-grade gliomas, we previously identified intratumoral metabolic heterogeneities. In this study, we evaluated the potential of 3D 7 T-MRSI for the preoperative noninvasive classification of glioma grade and isocitrate dehydrogenase (IDH) status. We demonstrated that IDH mutation and glioma grade are detectable by ultra-high field (UHF) MRI. This technique might potentially optimize the perioperative management of glioma patients. Methods We prospectively included 36 patients with WHO 2021 grade 2–4 gliomas (20 IDH mutated, 16 IDH wildtype). Our 7 T 3D MRSI sequence provided high-resolution metabolic maps (e.g., choline, creatine, glutamine, and glycine) of these patients’ brains. We employed multivariate random forest and support vector machine models to voxels within a tumor segmentation, for classification of glioma grade and IDH mutation status. Results Random forest analysis yielded an area under the curve (AUC) of 0.86 for multivariate IDH classification based on metabolic ratios. We distinguished high- and low-grade tumors by total choline (tCho) / total N-acetyl-aspartate (tNAA) ratio difference, yielding an AUC of 0.99. Tumor categorization based on other measured metabolic ratios provided comparable accuracy. Conclusions We successfully classified IDH mutation status and high- versus low-grade gliomas preoperatively based on 7 T MRSI and clinical tumor segmentation. With this approach, we demonstrated imaging based tumor marker predictions at least as accurate as comparable studies, highlighting the potential application of MRSI for pre-operative tumor classifications.

Objective Recently, the 7 Tesla (7 T) Epilepsy Task Force published recommendations for 7 T magnetic resonance imaging (MRI) in patients with pharmaco-resistant focal epilepsy in pre-surgical evaluation. The objective of this study was to implement and evaluate this consensus protocol with respect to both its practicability and its diagnostic value/potential lesion delineation surplus effect over 3 T MRI in the pre-surgical work-up of patients with pharmaco-resistant focal onset epilepsy. Methods The 7 T MRI protocol consisted of T1-weighted, T2-weighted, high-resolution-coronal T2-weighted, fluid-suppressed, fluid-and-white-matter-suppressed, and susceptibility-weighted imaging, with an overall duration of 50 min. Two neuroradiologists independently evaluated the ability of lesion identification, the detection confidence for these identified lesions, and the lesion border delineation at 7 T compared to 3 T MRI. Results Of 41 recruited patients > 12 years of age, 38 were successfully measured and analyzed. Mean detection confidence scores were non-significantly higher at 7 T (1.95 ± 0.84 out of 3 versus 1.64 ± 1.19 out of 3 at 3 T, p = 0.050). In 50% of epilepsy patients measured at 7 T, additional findings compared to 3 T MRI were observed. Furthermore, we found improved border delineation at 7 T in 88% of patients with 3 T-visible lesions. In 19% of 3 T MR-negative cases a new potential epileptogenic lesion was detected at 7 T. Conclusions The diagnostic yield was beneficial, but with 19% new 7 T over 3 T findings, not major. Our evaluation revealed epilepsy outcomes worse than ILAE Class 1 in two out of the four operated cases with new 7 T findings.

We compared metabolic ratio maps of tCho, Gln, and Gly to tNAA and tCr derived from high-resolution 7T MRSI to clinical amino acid PET in 24 glioma patients. We achieved the highest DICE coefficient of 0.66±0.25 for Gln/tNAA, and further defined hot spot volumes and center of intensity distances between PET and MRSI. Overall, we showed that the information content of 7T MRSI results in a better correspondence to PET as clinical gold standard than previous MRSI studies.

Magnetic Resonance Spectroscopic Imaging (MRSI) of the brain enables insights into the metabolic changes and fluxes in diseases such as tumors, multiple sclerosis, epilepsy, or hepatic encephalopathy, as well as insights into general brain functionality. However, the routine application of MRSI is mostly hampered by very low signal-to-noise ratios (SNR) due to the low concentrations of metabolites, about 10000 times lower than water. Furthermore, MRSI spectra have a dense information content with many overlapping metabolite resonances, especially for proton MRSI. MRI scanners at ultra-high field strengths, like 7 T or above, offer the opportunity to increase SNR, as well as the separation between resonances, thus promising to solve both challenges. Yet, MRSI at ultra-high field strengths is challenged by decreased B0-and B1-homogeneity, shorter T2 relaxation times, stronger chemical shift displacement errors, and aggravated lipid contamination. Therefore, to capitalize on the advantages of ultra-high field strengths, these challenges must be overcome. This review focuses on the chal-lenges MRSI of the human brain faces at ultra-high field strength, as well as the possible applications to this date.

(1) Background: Recent developments in 7T magnetic resonance spectroscopic imaging (MRSI) made the acquisition of high-resolution metabolic images in clinically feasible measurement times possible. The amino acids glutamine (Gln) and glycine (Gly) were identified as potential neuro-oncological markers of importance. For the first time, we compared 7T MRSI to amino acid PET in a cohort of glioma patients. (2) Methods: In 24 patients, we co-registered 7T MRSI and routine PET and compared hotspot volumes of interest (VOI). We evaluated dice similarity coefficients (DSC), volume, center of intensity distance (CoI), median and threshold values for VOIs of PET and ratios of total choline (tCho), Gln, Gly, myo-inositol (Ins) to total N-acetylaspartate (tNAA) or total creatine (tCr). (3) Results: We found that Gln and Gly ratios generally resulted in a higher correspondence to PET than tCho. Using cutoffs of 1.6-times median values of a control region, DSCs to PET were 0.53 ± 0.36 for tCho/tNAA, 0.66 ± 0.40 for Gln/tNAA, 0.57 ± 0.36 for Gly/tNAA, and 0.38 ± 0.31 for Ins/tNAA. (4) Conclusions: Our 7T MRSI data corresponded better to PET than previous studies at lower fields. Our results for Gln and Gly highlight the importance of future research (e.g., using Gln PET tracers) into the role of both amino acids.

Abstract OBJECTIVES Neurosurgical resection in gliomas depends on the precise preoperative definition of the tumor and its margins to realize a safe maximum resection that translates into a better patient outcome. New metabolic imaging techniques could improve this delineation as well as designate targets for biopsies. We validated the performance of our fast high-resolution whole-brain 3D-magnetic resonance spectroscopic imaging (MRSI) method at 7T in high-grade gliomas (HGGs) as first step to this regard. METHODS We measured 23 patients with HGGs at 7T with MRSI covering the whole cerebrum with 3.4mm isotropic resolution in 15 min. Quantification used a basis-set of 17 neurochemical components. They were evaluated for their reliability/quality and compared to neuroradiologically segmented tumor regions-of-interest (necrosis, contrast-enhanced, non-contrast-enhanced+edema, peritumoral) and histopathology (e.g., grade, IDH-status). RESULTS We found 18/23 measurements to be usable and ten neurochemicals quantified with acceptable quality. The most common denominators were increases of glutamine, glycine, and total choline as well as decreases of N-acetyl-aspartate and total creatine over most tumor regions. Other metabolites like taurine and serine showed mixed behavior. We further found that heterogeneity in the metabolic images often continued into the peritumoral region. While 2-hydroxy-glutarate could not be satisfyingly quantified, we found a tendency for a decrease of glutamate in IDH1-mutant HGGs. DISCUSSION Our findings corresponded well to clinical tumor segmentation but were more heterogeneous and often extended into the peritumoral region. Our results corresponded to previous knowledge, but with previously not feasible resolution. Apart from glycine/glutamine and their role in glioma progression, more research on the connection of glutamate and others to specific mutations is necessary. The addition of low-grade gliomas and statistical ROI analysis in a larger cohort will be the next important steps to define the benefits of our 7T MRSI approach for the definition of spatial metabolic tumor profiles.

Purpose Recently, a 3D‐concentric ring trajectory (CRT)‐based free induction decay (FID)‐MRSI sequence was introduced for fast high‐resolution metabolic imaging at 7 T. This technique provides metabolic ratio maps of almost the entire brain within clinically feasible scan times, but its robustness has not yet been thoroughly investigated. Therefore, we have assessed quantitative concentration estimates and their variability in healthy volunteers using this approach. Methods We acquired whole‐brain 3D‐CRT‐FID‐MRSI at 7 T in 15 min with 3.4 mm nominal isometric resolution in 24 volunteers (12 male, 12 female, mean age 27 ± 6 years). Concentration estimate maps were calculated for 15 metabolites using internal water referencing and evaluated in 55 different regions of interest (ROIs) in the brain. Data quality, mean metabolite concentrations, and their inter‐subject coefficients of variation (CVs) were compared for all ROIs. Results Of 24 datasets, one was excluded due to motion artifacts. The concentrations of total choline, total creatine, glutamate, myo‐inositol, and N ‐acetylaspartate in 44 regions were estimated within quality thresholds. Inter‐subject CVs (mean over 44 ROIs/minimum/maximum) were 9%/5%/19% for total choline, 10%/6%/20% for total creatine, 11%/7%/24% for glutamate, 10%/6%/19% for myo‐inositol, and 9%/6%/19% for N ‐acetylaspartate. Discussion We defined the performance of 3D‐CRT‐based FID‐MRSI for metabolite concentration estimate mapping, showing which metabolites could be robustly quantified in which ROIs with which inter‐subject CVs expected. However, the basal brain regions and lesser‐signal metabolites in particular remain as a challenge due susceptibility effects from the proximity to nasal and auditory cavities. Further improvement in quantification and the mitigation of B 0 / B 1 ‐field inhomogeneities will be necessary to achieve reliable whole‐brain coverage.

ObjectivesSuccessful neurosurgical intervention in gliomas depends on the precision of the preoperative definition of the tumor and its margins since a safe maximum resection translates into a better patient outcome. Metabolic high-resolution imaging might result in improved presurgical tumor characterization, and thus optimized glioma resection. To this end, we validated the performance of a fast high-resolution whole-brain 3D-magnetic resonance spectroscopic imaging (MRSI) method at 7T in a patient cohort of 23 high-grade gliomas (HGG).Materials and methodsWe preoperatively measured 23 patients with histologically verified HGGs (17 male, 8 female, age 53 ± 15) with an MRSI sequence based on concentric ring trajectories with a 64 × 64 × 39 measurement matrix, and a 3.4 × 3.4 × 3.4 mm3 nominal voxel volume in 15 min. Quantification used a basis-set of 17 components including N-acetyl-aspartate (NAA), total choline (tCho), total creatine (tCr), glutamate (Glu), glutamine (Gln), glycine (Gly) and 2-hydroxyglutarate (2HG). The resultant metabolic images were evaluated for their reliability as well as their quality and compared to spatially segmented tumor regions-of-interest (necrosis, contrast-enhanced, non-contrast enhanced + edema, peritumoral) based on clinical data and also compared to histopathology (e.g., grade, IDH-status).ResultsEighteen of the patient measurements were considered usable. In these patients, ten metabolites were quantified with acceptable quality. Gln, Gly, and tCho were increased and NAA and tCr decreased in nearly all tumor regions, with other metabolites such as serine, showing mixed trends. Overall, there was a reliable characterization of metabolic tumor areas. We also found heterogeneity in the metabolic images often continued into the peritumoral region. While 2HG could not be satisfyingly quantified, we found an increase of Glu in the contrast-enhancing region of IDH-wildtype HGGs and a decrease of Glu in IDH1-mutant HGGs.ConclusionsWe successfully demonstrated high-resolution 7T 3D-MRSI in HGG patients, showing metabolic differences between tumor regions and peritumoral tissue for multiple metabolites. Increases of tCho, Gln (related to tumor metabolism), Gly (related to tumor proliferation), as well as decreases in NAA, tCr, and others, corresponded very well to clinical tumor segmentation, but were more heterogeneous and often extended into the peritumoral region.

We compared 7T MRSI-derived metabolic ratios with T1 and T2 maps from MR fingerprinting in a cohort of glioma patients by defining hotspots and calculating Dice similarity coefficients (DSCs) between them and a segmentation. Notable high DSCs were 0.799 for tCho/NAA vs T2 and 0.753 for Gln/NAA vs T1. We also investigated values for T1, T2, and metabolite ratios in tumors, hotspots, and a control region, with the median relaxation times being T1=1610 ms and T2=79 ms in the tumor, and T1=964 ms and T2=46 ms in NAWM.

Motivation: Treatment monitoring in pediatric brain tumors is often challenging. Non-invasive tools are needed to assess tumor activity accurately. Goal(s): To monitor changes in phospholipid metabolism (i.e., phosphomonoesters/phosphodiesters [PME/PDE]) in pediatric low grade gliomas. Approach: Eleven pediatric brain tumor patients underwent 31P-MRSI at 7T. PME/PDE in the tumor was compared to a normal-appearing brain voxel. Clinical outcomes were assessed, and results were compared between treatment and wait-and-scan groups. Results: Tumors selected for treatment showed higher PME/PDE ratios at baseline, hinting at potential aggressiveness. These ratios decreased during treatment but remained stable in the wait-and-scan group. Impact: Treatment monitoring in pediatric brain tumors is often challenging. Using 31P-MRSI we showed that tumors selected for treatment exhibited higher PME/PDE, signifying potential tumor aggressiveness. PME/PDE levels decreased during treatment, indicating potential for non-invasive assessment of treatment effects.