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Purpose: Measurements of objective response rates are critical to evaluate new glioma therapies. The hallmark metabolic alteration in gliomas with mutant isocitrate dehydrogenase (IDH) is the overproduction of oncometabolite 2-hydroxyglutarate (2HG), which plays a key role in malignant transformation. 2HG represents an ideal biomarker to probe treatment response in IDH-mutant glioma patients, and we hypothesized a decrease in 2HG levels would be measureable by in vivo magnetic resonance spectroscopy (MRS) as a result of antitumor therapy.Experimental Design: We report a prospective longitudinal imaging study performed in 25 IDH-mutant glioma patients receiving adjuvant radiation and chemotherapy. A newly developed 3D MRS imaging was used to noninvasively image 2HG. Paired Student t test was used to compare pre- and posttreatment tumor 2HG values. Test–retest measurements were performed to determine the threshold for 2HG functional spectroscopic maps (fSM). Univariate and multivariate regression were performed to correlate 2HG changes with Karnofsky performance score (KPS).Results: We found that mean 2HG (2HG/Cre) levels decreased significantly (median = 48.1%; 95% confidence interval = 27.3%–56.5%; P = 0.007) in the posttreatment scan. The volume of decreased 2HG correlates (R2 = 0.88, P = 0.002) with clinical status evaluated by KPS.Conclusions: We demonstrate that dynamic measurements of 2HG are feasible by 3D fSM, and the decrease of 2HG levels can monitor treatment response in patients with IDH-mutant gliomas. Our results indicate that quantitative in vivo 2HG imaging may be used for precision medicine and early response assessment in clinical trials of therapies targeting IDH-mutant gliomas. Clin Cancer Res; 22(7); 1632–41. ©2015 AACR.

This file contains 3 Tables and 9 Figures with additional results and details of analysis related to the main results presented in the manuscript. The complete list of figures and tables is provided below. Table 1S. Baseline metabolite levels relative to creatine. Table 2S. Demographic, genomic, molecular, histological, radiological and clinical data. Table 3S. Fractional changes for all imaging biomarkers. Table 4S. General Linear Model with change of KPS as dependent variable and grade, age and fractional volume of decreased 2HG/Cre as covariates. Figure 1S. 3D MRSI pulse sequence for 2HG imaging, detailed diagram. Figure 2S. Quantum mechanics simulations of 2HG spectral editing for MEGA-LASER, MEGA-PRESS and PRESS-97 sequences. Figure 3S. Simulations of 2HG editing for MEGA-LASER and PRESS-97 sequences in the presence of B1 field inhomogeneity. Figure 4S. Mitigation of subtraction artifacts caused by motion and scanner instability in J-difference MR spectroscopy using real-time prospective motion correction, shim update and reacquisition. Figure 5S. Metabolic maps before image interpolation. Figure 6S. Examples of OFF and difference (DIFF) MEGA-LASER spectra from 3D MRSI voxels located in the tumor and contralateral normal appearing white matter, pre- and post-treatment respectively. Figure 7S. Distributions of Δ2HG/Cre in the test-retest and post-pre longitudinal data sets. Figure 8S. Comparison of functional spectroscopic maps (fSM) and functional diffusion maps (fDM). Figure 9S. Relation between residual tumor measured by FLAIR hyperintensity volume and the SNR and CRLB of 2HG. Notes. Details related to data analysis in Figures 7S and 9S.

Supplementary Appendix, Table 1, Figure Legends 1-4 from Serial Magnetic Resonance Spectroscopy Reveals a Direct Metabolic Effect of Cediranib in Glioblastoma

Supplementary Figure 1 from Glioblastoma Recurrence after Cediranib Therapy in Patients: Lack of “Rebound” Revascularization as Mode of Escape

Supplementary Methods, Figures 1-4 from Serial Magnetic Resonance Spectroscopy Reveals a Direct Metabolic Effect of Cediranib in Glioblastoma

Supplementary Figure 2 from Glioblastoma Recurrence after Cediranib Therapy in Patients: Lack of “Rebound” Revascularization as Mode of Escape

Supplementary Table 1 from Glioblastoma Recurrence after Cediranib Therapy in Patients: Lack of “Rebound” Revascularization as Mode of Escape

Proton magnetic resonance spectroscopy is increasingly used in clinical studies of brain tumor to provide information about tissue metabolic profiles. In this study, we evaluated changes in the levels of metabolites predominant in recurrent glioblastoma multiforme (rGBM) to characterize the response of rGBM to antiangiogenic therapy. We examined 31 rGBM patients treated with daily doses of cediranib, acquiring serial chemical shift imaging data at specific time points during the treatment regimen. We defined spectra from three regions of interest (ROI)—enhancing tumor (ET), peritumoral tissue, and normal tissue on the contralateral side (cNT)—in post-contrast T1-weighted images, and normalized the concentrations of N-acetylaspartate (NAA) and choline (Cho) in each ROI to the concentration of creatine in cNT (norCre). We analyzed the ratios of these normalized metabolites (i.e., NAA/Cho, NAA/norCre, and Cho/norCre) by averaging all patients and categorizing two different survival groups. Relative to pretreatment values, NAA/Cho in ET was unchanged through day 28. However, after day 28, NAA/Cho significantly increased in relation to a significant increase in NAA/norCre and a decrease in Cho/norCre; interestingly, the observed trend was reversed after day 56, consistent with the clinical course of GBM recurrence. Notably, receiver operating characteristic analysis indicated that NAA/Cho in tumor shows a high prediction to 6-month overall survival. These metabolic changes in these rGBM patients strongly suggest a direct metabolic effect of cediranib and might also reflect an antitumor response to antiangiogenic treatment during the first 2 months of treatment. Further study is needed to confirm these findings. Cancer Res; 71(11); 3745–52. ©2011 AACR.

BACKGROUND: Patients with recurrent glioblastoma (GBM) are often treated with antiangiogenic agents, such as bevacizumab (BEV). Despite therapeutic promise, conventional MRI methods fail to help determine which patients may not benefit from this treatment. PURPOSE: To use MR spectroscopic imaging (MRSI) with intermediate and short echo time to measure corrected myo-inositol (mI)normalized by contralateral creatine (hereafter, mI/c-Cr) in participants with recurrent GBM treated with BEV and to investigate whether such measurements can help predict survivorship before BEV initiation (baseline) and at 1 day, 4 weeks, and 8 weeks thereafter. MATERIALS AND METHODS: In this prospective longitudinal study (2016–2020), spectroscopic data on mI—a glial marker and osmoregulator within the brain—normalized by contralateral creatine in the intratumoral, contralateral, and peritumoral volumes of patients with recurrent GBM were evaluated. Area under the receiver operating characteristic curve (AUC) was calculated for all volumes at baseline and 1 day, 4 weeks, and 8 weeks after treatment to determine the ability of mI/c-Cr to help predict survivorship. RESULTS: Twenty-one participants (median age ± standard deviation, 62 years ± 12; 15 men) were evaluated. Lower mI/c-Cr in the tumor before and during BEV treatment was predictive of poor survivorship, with receiver operating characteristic analyses showing an AUC of 0.75 at baseline, 0.87 at 1 day after treatment, and 1 at 8 weeks after. A similar result was observed in contralateral normal-appearing tissue and the peritumoral volume, with shorter-term survivors having lower levels of mI/c-Cr. In the contralateral volume, a lower ratio of mI to creatine (hereafter, mI/Cr) predicted shorter-term survival at baseline and all other time points. Within the peritumoral volume, lower mI/c-Cr levels were predictive of shorter-term survival at baseline (AUC, 0.80), at 1 day after treatment (AUC, 0.93), and at 4 weeks after treatment (AUC, 0.68). CONCLUSION: Lower levels of myo-inositol normalized by contralateral creatine within intratumoral, contralateral, and peritumoral volumes were predictive of poor survivorship and antiangiogenic treatment failure as early as before bevacizumab treatment. Adapting MR spectroscopic imaging alongside conventional MRI modalities conveys critical information regarding the biologic characteristics of tumors to help better treat individuals with recurrent glioblastoma. Clinical trial registration no. NCT02843230 © RSNA, 2021 Online supplemental material is available for this article.

Background Magnetic resonance spectroscopic imaging (MRSI) can be used in glioma patients to map the metabolic alterations associated with IDH1,2 mutations that are central criteria for glioma diagnosis. The aim of this study was to achieve super-resolution (SR) MRSI using deep learning to image tumor metabolism in patients with mutant IDH glioma. Methods We developed a deep learning method based on generative adversarial network (GAN) using Unet as generator network to upsample MRSI by a factor of 4. Neural networks were trained on simulated metabolic images from 75 glioma patients. The performance of deep neuronal networks was evaluated on MRSI data measured in 20 glioma patients and 10 healthy controls at 3T with a whole-brain 3D MRSI protocol optimized for detection of d-2-hydroxyglutarate (2HG). To further enhance structural details of metabolic maps we used prior information from high-resolution anatomical MR imaging. SR MRSI was compared to ground truth by Mann–Whitney U-test of peak signal-to-noise ratio (PSNR), structure similarity index measure (SSIM), feature-based similarity index measure (FSIM), and mean opinion score (MOS). Results Deep learning SR improved PSNR by 17%, SSIM by 5%, FSIM by 7%, and MOS by 30% compared to conventional interpolation methods. In mutant IDH glioma patients proposed method provided the highest resolution for 2HG maps to clearly delineate tumor margins and tumor heterogeneity. Conclusions Our results indicate that proposed deep learning methods are effective in enhancing spatial resolution of metabolite maps. Patient results suggest that this may have great clinical potential for image guided precision oncology therapy.

BACKGROUND Fibromyalgia is a centralized multidimensional chronic pain syndrome, but its pathophysiology is not fully understood. METHODS We applied 3D magnetic resonance spectroscopic imaging (MRSI), covering multiple cortical and subcortical brain regions, to investigate the association between neuro-metabolite (e.g. combined glutamate and glutamine, Glx; myo-inositol, mIno; and combined (total) N-acetylaspartate and N-acetylaspartylglutamate, tNAA) levels and multidimensional clinical/behavioural variables (e.g. pain catastrophizing, clinical pain severity and evoked pain sensitivity) in women with fibromyalgia (N = 87). RESULTS Pain catastrophizing scores were positively correlated with Glx and tNAA levels in insular cortex, and negatively correlated with mIno levels in posterior cingulate cortex (PCC). Clinical pain severity was positively correlated with Glx levels in insula and PCC, and with tNAA levels in anterior midcingulate cortex (aMCC), but negatively correlated with mIno levels in aMCC and thalamus. Evoked pain sensitivity was negatively correlated with levels of tNAA in insular cortex, MCC, PCC and thalamus. CONCLUSIONS These findings support single voxel placement targeting nociceptive processing areas in prior 1 H-MRS studies, but also highlight other areas not as commonly targeted, such as PCC, as important for chronic pain pathophysiology. Identifying target brain regions linked to multidimensional symptoms of fibromyalgia (e.g. negative cognitive/affective response to pain, clinical pain, evoked pain sensitivity) may aid the development of neuromodulatory and individualized therapies. Furthermore, efficient multi-region sampling with 3D MRSI could reduce the burden of lengthy scan time for clinical research applications of molecular brain-based mechanisms supporting multidimensional aspects of fibromyalgia. SIGNIFICANCE This large N study linked brain metabolites and pain features in fibromyalgia patients, with a better spatial resolution and brain coverage, to understand a molecular mechanism underlying pain catastrophizing and other aspects of pain transmission. Metabolite levels in self-referential cognitive processing area as well as pain-processing regions were associated with pain outcomes. These results could help the understanding of its pathophysiology and treatment strategies for clinicians.

Advanced molecular and pathophysiologic characterization of primary central nervous system lymphoma (PCNSL) has revealed insights into promising targeted therapeutic approaches. Medical imaging plays a fundamental role in PCNSL diagnosis, staging, and response assessment. Institutional imaging variation and inconsistent clinical trial reporting diminishes the reliability and reproducibility of clinical response assessment. In this context, we aimed to: (1) critically review the use of advanced positron emission tomography (PET) and magnetic resonance imaging (MRI) in the setting of PCNSL; (2) provide results from an international survey of clinical sites describing the current practices for routine and advanced imaging, and (3) provide biologically based recommendations from the International PCNSL Collaborative Group (IPCG) on adaptation of standardized imaging practices. The IPCG provides PET and MRI consensus recommendations built upon previous recommendations for standardized brain tumor imaging protocols (BTIP) in primary and metastatic disease. A biologically integrated approach is provided to addresses the unique challenges associated with the imaging assessment of PCNSL. Detailed imaging parameters facilitate the adoption of these recommendations by researchers and clinicians. To enhance clinical feasibility, we have developed both “ideal” and “minimum standard” protocols at 3T and 1.5T MR systems that will facilitate widespread adoption.

To evaluate the performance of novel spiral MRSI and tissue segmentation pipeline of the brain, to investigate neurometabolic changes in normal-appearing white matter (NAWM) and white matter lesions (WML) of stable relapsing remitting multiple sclerosis (RRMS) compared to healthy controls (HCs).Spiral 3D MRSI using LASER-GOIA-W [16,4] was undertaken on 16 RRMS patients and 9 HCs, to acquire MRSI data from a large volume of interest (VOI) 320 cmCompared to HCs, RRMS demonstrated a statistically significant reduction in all mean brain tissues and increase in CSF volume. Within VOI, WM decreased (-10%) and CSF increased (41%) in RRMS compared to HCs (p 0.001). MRSI revealed that total creatine (tCr) ratios of N-acetylaspartate and glutamate+glutamine in WML were significantly lower than NAWM-MS (-9%, -8%) and HCs (-14%, -10%), respectively. Myo-inositol/tCr in WML was significantly higher than NAWM-MS (14%) and HCs (10%). SVM of MRSI yielded accuracy, sensitivity and specificity of 86%, 95%, and 70%, respectively for HCs vs WML, which were higher than HC vs NAWM and WML vs NAWM models.This study demonstrates the benefit of MRSI in evaluating MS neurometabolic changes in NAWM. SVM of MRSI data in the MS brain may be suited for clinical monitoring and progression of MS patients. Longitudinal MRSI studies are warranted.

Metabolic imaging of the human brain by in-vivo magnetic resonance spectroscopic imaging (MRSI) can non-invasively probe neurochemistry in healthy and disease conditions. MRSI at ultra-high field (≥ 7 T) provides increased sensitivity for fast high-resolution metabolic imaging, but comes with technical challenges due to non-uniform B0 field. Here, we show that an integrated RF-receive/B0-shim (AC/DC) array coil can be used to mitigate 7 T B0 inhomogeneity, which improves spectral quality and metabolite quantification over a whole-brain slab. Our results from simulations, phantoms, healthy and brain tumor human subjects indicate improvements of global B0 homogeneity by 55%, narrower spectral linewidth by 29%, higher signal-to-noise ratio by 31%, more precise metabolite quantification by 22%, and an increase by 21% of the brain volume that can be reliably analyzed. AC/DC shimming provide the highest correlation (R2 = 0.98, P = 0.001) with ground-truth values for metabolite concentration. Clinical translation of AC/DC and MRSI is demonstrated in a patient with mutant-IDH1 glioma where it enables imaging of D-2-hydroxyglutarate oncometabolite with a 2.8-fold increase in contrast-to-noise ratio at higher resolution and more brain coverage compared to previous 7 T studies. Hence, AC/DC technology may help ultra-high field MRSI become more feasible to take advantage of higher signal/contrast-to-noise in clinical applications.

Issues with model fitting (i.e. suboptimal standard deviation, linewidth/full-width-at-half-maximum, and/or signal-to-noise ratio) in multi-voxel MRI spectroscopy, or chemical shift imaging (CSI) can result in the significant loss of usable voxels. A potential solution to minimize this problem is to estimate the value of unusable voxels by utilizing information from reliable voxels in the same image. We assessed an image restoration method called inpainting as a tool to restore unusable voxels, and compared it with traditional interpolation methods (nearest neighbor, trilinear interpolation and tricubic interpolation). In order to evaluate the performance across varying image contrasts and spatial resolutions, we applied the same techniques to a T1-weighted MRI brain dataset, and N-acetylaspartate (NAA) spectroscopy maps from a CSI dataset. For all image types, inpainting exhibited superior performance (lower normalized root-mean-square errors, NRMSE) compared to all other methods considered (p's < 0.001). Inpainting maintained an average NRMSE of less than 5% even with 50% missing voxels, whereas the other techniques demonstrated up to three times that value, depending on the nature of the image. For CSI maps, inpainting maintained its superiority whether the previously unusable voxels were randomly distributed, or located in regions most commonly affected by voxel loss in real-world data. Inpainting is a promising approach for recovering unusable or missing voxels in voxel-wise analyses, particularly in imaging modalities characterized by low SNR such as CSI. We hypothesize that this technique may also be applicable for datasets from other imaging modalities, such as positron emission tomography, or dynamic susceptibility contrast MRI.

BACKGROUND: Absolute quantification of metabolites in MR spectroscopic imaging (MRSI) requires a stable reference signal of known concentration. The Electronic REference To access In vivo Concentrations (ERETIC) has shown great promise but has not been applied in patients and 3D MRSI. ERETIC hardware has not been integrated with receive arrays due to technical challenges, such as coil combination and unwanted coupling between multiple ERETIC and receive channels, for which we developed mitigation strategies. PURPOSE: To develop absolute quantification for whole-brain MRSI in glioma patients. STUDY TYPE: Prospective. POPULATION: Five healthy volunteers and three patients with isocitrate dehydrogenase mutant glioma (27% female). Calibration and coil loading phantoms. FIELD STRENGTH/SEQUENCE: A 3T; Adiabatic spin-echo spiral 3D MRSI with real-time motion correction, Fluid Attenuated Inversion Recovery (FLAIR), Gradient Recalled Echo (GRE), Multi-echo Magnetization Prepared Rapid Acquisition of Gradient Echo (MEMPRAGE). ASSESSMENT: Absolute quantification was performed for five brain metabolites (total N-acetyl-aspartate [NAA]/creatine/choline, glutamine + glutamate, myoinositol) and the oncometabolite 2-hydroxyglutarate using a custom-built 4x-ERETIC/8x-receive array coil. Metabolite quantification was performed with both EREIC and internal water reference methods. ERETIC signal was transmitted via optical link and used to correct coil loading. Inductive and radiative coupling between ERETIC and receive channels were measured. STATISTICAL TESTS: ERETIC and internal water methods for metabolite quantification were compared using Bland–Altman (BA) analysis and the nonparametric Mann–Whitney test. P < 0.05 was considered statistically significant. RESULTS: ERETIC could be integrated in receive arrays and inductive coupling dominated (5–886 times) radiative coupling. Phantoms show proportional scaling of the ERETIC signal with coil loading. The BA analysis demonstrated very good agreement (3.3% ± 1.6%) in healthy volunteers, while there was a large difference (36.1% ± 3.8%) in glioma tumors between metabolite concentrations by ERETIC and internal water quantification. CONCLUSION: Our results indicate that ERETIC integrated with receive arrays and whole-brain MRSI is feasible for brain metabolites quantification. Further validation is required to probe that ERETIC provides more accurate metabolite concentration in glioma patients. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1

Metabolic imaging can map spatially abnormal molecular pathways with higher specificity for cancer compared to anatomical imaging. However, acquiring high resolution metabolic maps similar to anatomical MRI is challenging in patients due to low metabolite concentrations, and alternative approaches that increase resolution by post-acquisition image processing can mitigate this limitation. We developed deep learning super-resolution MR spectroscopic imaging (MRSI) to map tumor metabolism in patients with mutant IDH glioma. We used a generative adversarial network (GAN) architecture comprised of a UNet neural network as the generator network and a discriminator network for adversarial training. For initial training we simulated a large data set of 9600 images with realistic quality for acquired MRSI to effectively train the deep learning model to upsample by a factor of four. Two types of training were performed: 1) using only the MRSI data, and 2) using MRSI and prior information from anatomical MRI to further enhance structural details. The performance of super-resolution methods was evaluated by peak SNR (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM). After training on simulations, GAN was evaluated on measured MRSI metabolic maps acquired with resolution 5.2×5.2 mm2 and upsampled to 1.3×1.3 mm2. The GAN trained only on MRSI achieved PSNR = 27.94, SSIM = 0.88, FSIM = 0.89. Using prior anatomical MRI improved GAN performance to PSNR = 30.75, SSIM = 0.90, FSIM = 0.92. In the patient measured data, GAN super-resolution metabolic images provided clearer tumor margins and made apparent the tumor metabolic heterogeneity. Compared to conventional image interpolation such as bicubic or total variation, deep learning methods provided sharper edges and less blurring of structural details. Our results indicate that the proposed deep learning method is effective in enhancing the spatial resolution of metabolite maps which may better guide treatment in mutant IDH glioma patients.

Patients with recurrent glioblastoma (rGBM) are often started on anti-angiogenic therapy such as bevacizumab. However, determining treatment failure using conventional MRI methods remains challenging. We prospectively collected longitudinal MR spectroscopy data in 33 patients with rGBM and quantified various metabolites including N-acetylaspartate (NAA), Choline (Cho), and Lactate (Lac). After stratifying patients by 9 month survival, we found that longer-term survivors had decreased Lac/NAA and increased NAA/Cho compared to shorter-term survivors. ROC analyses illustrated that intratumoral changes in NAA/Cho were predictive of survival at 1 day (AUC 0.92), 2 weeks (AUC 0.75), 8 weeks (AUC 0.71) and 16 weeks (AUC 0.85) but not 4 weeks (AUC 0.60). Intratumoral changes in Lac/NAA were predictive of survival at all time points tested (AUCs > 0.76 for all time points). At 8 weeks, 90% of patients with increased Lac/NAA from baseline and 88% of patients with decreased NAA/Cho did not survive 9 months. Changes in NAA/Cho and Lac/NAA may serve as early biomarkers of anti-angiogenic treatment failure.

BACKGROUND Recurrent glioblastoma (rGBM) patients are often treated with anti-angiogenic agents such as bevacizumab (BEV). Despite therapeutic promise, conventional MR methods fail to determine which patients may not benefit. PURPOSE: The purpose of this study was to utilize magnetic resonance spectroscopic imaging (MRSI) with intermediate and short echo time to generate corrected Myo-inositol normalized by contralateral creatine (mI/c-Cr) in patients with rGBM treated with BEV and investigate whether it can predict survivorship prior to BEV initiation (baseline) and at 1-day, 4-weeks, and 8-weeks thereafter. METHODS We conducted a prospective, longitudinal study and evaluated spectroscopic data of myo-inositol (mI), a glial marker and osmoregulator within the brain, normalized to contralateral-creatine (mI/c-Cr) in the intratumoral, contralateral normal appearing white matter, and peritumoral volumes of rGBM patients. Area under the ROC curve (AUC) was calculated for all volumes at baseline, 1-day, 4-weeks, and 8-weeks after treatment to determine mI/c-Cr’s ability to predict survivorship. RESULTS 21 participants (62 ± 12 years, 15 men) were evaluated. Lower mI/c-Cr in the tumor prior to and during BEV treatment predicted poor survivorship, with ROC analyses illustrating an AUC of 0.75 at baseline, 0.87 at 1-day, and 1 at 8 weeks. Lower levels of mI/c-Cr were also observed in the contralateral and the peritumoral volumes for shorter-term survivors. In the contralateral volume, lower mI/Cr was predictive of shorter-term survival at baseline and all other timepoints. Within the peritumoral volume, lower mI/c-Cr was predictive of shorter-term survival at baseline (AUC=0.80), 1-day (AUC=0.93), and 4-weeks (AUC=0.68). CONCLUSIONS Lower levels of mI/c-Cr within intratumoral, contralateral, and peritumoral volumes were predictive of poor survivorship and anti-angiogenic treatment failure as early as one month before BEV treatment. Acquiring MRSI alongside conventional MR imaging modalities can convey critical information regarding tumor microenvironment that informs management of patients with rGBM.

MR spectroscopic imaging (MRSI) noninvasively maps the metabolism of human brains. In particular, the imaging of D-2-hydroxyglutarate (2HG) produced by glioma isocitrate dehydrogenase (IDH) mutations has become a key application in neuro-oncology. However, the performance of full field-of-view MRSI is limited by B0 spatial nonuniformity and lipid artifacts from tissues surrounding the brain. Array coils that multiplex RF-receive and B0 -shim electrical currents (AC/DC mixing) over the same conductive loops provide many degrees of freedom to improve B0 uniformity and reduce lipid artifacts. AC/DC coils are highly efficient due to compact design, requiring low shim currents (

MR spectroscopic imaging (MRSI) noninvasively maps the metabolism of human brains. In particular, the imaging of D-2-hydroxyglutarate (2HG) produced by glioma isocitrate dehydrogenase (IDH) mutations has become a key application in neuro-oncology. However, the performance of full field-of-view MRSI is limited by B(0) spatial nonuniformity and lipid artifacts from tissues surrounding the brain. Array coils that multiplex RF-receive and B(0)-shim electrical currents (AC/DC mixing) over the same conductive loops provide many degrees of freedom to improve B(0) uniformity and reduce lipid artifacts. AC/DC coils are highly efficient due to compact design, requiring low shim currents (

Highlights • Radiation-induced effects visible in Gd-EOB-DTPA enhanced MRI during proton therapy. • High inter-patient variation in early MRI signal change during therapy. • Correlation of signal change with pretreatment IL-6 concentration., Background and purpose Previous MRI studies have shown a substantial decrease in normal-tissue uptake of a hepatobiliary-directed contrast agent 6–9 weeks after liver irradiation. In this prospective clinical study, we investigated whether this effect is detectable during the course of proton therapy. Material and methods Gd-EOB-DTPA enhanced MRI was performed twice during hypo-fractionated proton therapy of liver lesions in 9 patients (plus two patients with only one scan available). Dose-correlated signal changes were qualitatively scored based on difference images from the two scans. We evaluated the correlation between the MRI signal change with the planned dose map. The GTV was excluded from all analyses. In addition, were examined timing, irradiated liver volume, changes in liver function parameters as well as circulating biomarkers of inflammation. Results Strong, moderate or no dose-correlated signal changes were detected for 2, 3 and 5 patients, respectively. Qualitative scoring was consistent with the quantitative dose to signal change correlation. In an exploratory analysis, the strongest correlation was found between the qualitative scoring and pretreatment IL-6 concentration. For all patients, a clear dose-correlated signal decrease was seen in late follow-up scans. Conclusion Radiation-induced effects can be detected with Gd-EOB-DTPA enhanced MRI in a subgroup of patients within a few days after proton irradiation. The reason for the large inter-patient variations is not yet understood and will require validation in larger studies.

Proton Magnetic Resonance Spectroscopy ((1)H MRS) provides non-invasive, quantitative metabolite profiles of tissue and has been shown to aid the clinical management of several brain diseases. Whilst most modern clinical MR scanners support MRS capabilities, routine use is largely restricted to specialized centers with good access to MR research support. Widespread adoption has been slow for several reasons, and technical challenges towards obtaining reliable good-quality results have been identified as a contributing factor. Considerable progress has been made by the research community to address many of these challenges, and in this paper a consensus is presented on deficiencies in widely available MRS methodology and validated improvements that are currently in routine use at several clinical research institutions. In particular, the localization error for the popular point resolved spectroscopy (PRESS) localization sequence was found to be unacceptably high at 3T, and the use of the semi-adiabatic localization by adiabatic selective refocusing (semi-LASER) sequence is a recommended solution. The incorporation of simulated metabolite basis-sets into analysis routines is recommended for reliably capturing the full spectral detail available from short echo time acquisitions. In addition, the importance of achieving a highly homogenous static magnetic field (B(0)) in the acquisition region is emphasized, and the limitations of current methods and hardware are discussed. Most recommendations require only software improvements, greatly enhancing the capabilities of clinical MRS on existing hardware. We anticipate the implementation of these recommendations will strengthen current clinical applications and advance progress towards developing and validating new MRS biomarkers for clinical use.

Magnetic resonance spectroscopic imaging (MRSI) offers considerable promise for monitoring metabolic alterations associated with disease or injury; however, to date, these methods have not had a significant impact on clinical care, and their use remains largely confined to the research community and a limited number of clinical sites. The MRSI methods currently implemented on clinical MRI instruments have remained essentially unchanged for two decades, with only incremental improvements in sequence implementation. During this time, a number of technological developments have taken place that have already greatly benefited the quality of MRSI measurements within the research community and which promise to bring advanced MRSI studies to the point where the technique becomes a true imaging modality, while making the traditional review of individual spectra a secondary requirement. Furthermore, the increasing use of biomedical MR spectroscopy studies has indicated clinical areas where advanced MRSI methods can provide valuable information for clinical care. In light of this rapidly changing technological environment and growing understanding of the value of MRSI studies for biomedical studies, this article presents a consensus from a group of experts in the field that reviews the state-of-the-art for clinical proton MRSI studies of the human brain, recommends minimal standards for further development of vendor-provided MRSI implementations, and identifies areas which need further technical development.

Background Determining failure to anti-angiogenic therapy in recurrent glioblastoma (GBM) (rGBM) remains a challenge. The purpose of the study was to assess treatment response to bevacizumab-based therapy in patients with rGBM using MR spectroscopy (MRS). Methods We performed longitudinal MRI/MRS in 33 patients with rGBM to investigate whether changes in N-acetylaspartate (NAA)/Choline (Cho) and Lactate (Lac)/NAA from baseline to subsequent time points after treatment can predict early failures to bevacizumab-based therapies. Results After stratifying based on 9-month survival, longer-term survivors had increased NAA/Cho and decreased Lac/NAA levels compared to shorter-term survivors. ROC analyses for intratumoral NAA/Cho correlated with survival at 1 day, 2 weeks, 8 weeks, and 16 weeks. Intratumoral Lac/NAA ROC analyses were predictive of survival at all time points tested. At the 8-week time point, 88% of patients with decreased NAA/Cho did not survive 9 months; furthermore, 90% of individuals with an increased Lac/NAA from baseline did not survive at 9 months. No other metabolic ratios tested significantly predicted survival. Conclusions Changes in metabolic levels of tumoral NAA/Cho and Lac/NAA can serve as early biomarkers for predicting treatment failure to anti-angiogenic therapy as soon as 1 day after bevacizumab-based therapy. The addition of MRS to conventional MR methods can provide better insight into how anti-angiogenic therapy affects tumor microenvironment and predict patient outcomes.

Spectral editing in in vivo 1 H-MRS provides an effective means to measure low-concentration metabolite signals that cannot be reliably measured by conventional MRS techniques due to signal overlap, for example, γ-aminobutyric acid, glutathione and D-2-hydroxyglutarate. Spectral editing strategies utilize known J-coupling relationships within the metabolite of interest to discriminate their resonances from overlying signals. This consensus recommendation paper provides a brief overview of commonly used homonuclear editing techniques and considerations for data acquisition, processing and quantification. Also, we have listed the experts' recommendations for minimum requirements to achieve adequate spectral editing and reliable quantification. These include selecting the right editing sequence, dealing with frequency drift, handling unwanted coedited resonances, spectral fitting of edited spectra, setting up multicenter clinical trials and recommending sequence parameters to be reported in publications.

Spectral editing in in vivo (1)H-MRS provides an effective means to measure low-concentration metabolite signals that cannot be reliably measured by conventional MRS techniques due to signal overlap, e.g., γ-aminobutyric acid (GABA), glutathione (GSH) and D-2-hydroxyglutarate (2HG). Spectral editing strategies utilize known J-coupling relationships within the metabolite of interest to discriminate their resonances from overlying signals. This consensus recommendation paper provides a brief overview of commonly used homo-nuclear editing techniques and considerations for data acquisition, processing, and quantification. Also, we have listed the experts’ recommendations for minimum requirements to achieve adequate spectral editing and reliable quantification. These include selecting the right editing sequence, dealing with frequency drift, handling unwanted co-edited resonances, spectral fitting of edited spectra, setting up multi-center clinical trials, and recommending sequence parameters to be reported in publications.

Long acquisition times due to intrinsically low signal-to-noise and the need for highly homogeneous B(0) field make magnetic resonance spectroscopy (MRS) particularly susceptible to motion or scanner instability compared to MRI. Motion induced changes in both localization and shimming (i.e., B(0) homogeneity) degrade MRS data quality. To mitigate the effects of motion three approaches can be employed: 1) subject immobilization, 2) retrospective correction, and 3) prospective real-time correction using internal and/or external tracking methods. Prospective real-time correction methods can simultaneously update localization and the B(0) field to improve MRS data quality. While localization errors can be corrected with both internal (navigators) and external (optical camera, NMR probes) tracking methods, the B(0) field correction requires internal navigator methods to measure the B(0) field inside the imaged volume and the possibility to update the scanner shim hardware in real time. Internal and external tracking can rapidly update the MRS localization with sub-millimeter and sub-degree precision, while scanner frequency and 1(st) order shims of scanner hardware can be updated by internal methods every sequence repetition. These approaches are most well-developed for neuroimaging, for which rigid transformation is primarily applicable. Real-time correction greatly improves the stability of MRS acquisition and quantification as shown in clinical studies on subjects prone to motion, including children and patients with movement disorders, enabling robust measurement of metabolite signals including those with low concentrations, such as gamma-aminobutyric acid (GABA) and glutathione (GSH). Thus, motion correction is recommended for MRS users and calls for tighter integration and wider availability of such methods by MR scanner manufacturers.

Low sensitivity MR techniques such as magnetic resonance spectroscopic imaging (MRSI) greatly benefit from the gain in signal-to-noise (SNR) provided by ultra-high field MR. High-resolution and whole-brain slab MRSI remains however very challenging due to lengthy acquisition, low signal, lipid contamination and field inhomogeneity. In this study, we propose an acquisition-reconstruction scheme that combines a1H-FID-MRSI sequence with compressed sensing acceleration and low-rank modeling with total-generalized-variation constraint to achieve metabolite imaging in two and three dimensions at 7 Tesla. The resulting images and volumes reveal highly detailed distributions that are specific to each metabolite and follow the underlying brain anatomy. The MRSI method was validated in a high-resolution phantom containing fine metabolite structures, and in 3 healthy volunteers. This new application of compressed sensing acceleration paves the way for high-resolution MRSI in clinical setting with acquisition times of 5 min for 2D MRSI at 2.5 mm and of 20 min for 3D MRSI at 3.3 mm isotropic.

Metabolic imaging of the human brain by in-vivo magnetic resonance spectroscopic imaging (MRSI) can non-invasively probe neurochemistry in healthy and disease conditions. MRSI at ultra-high field (≥ 7 T) provides increased sensitivity for fast high-resolution metabolic imaging, but comes with technical challenges due to non-uniform B

The purpose of this work is to develop and validate a new atlas-based metabolite quantification pipeline for edited magnetic resonance spectroscopic imaging (MEGA-MRSI) that enables group comparisons of brain-structure specific GABA levels. By using brain structure masks segmented from high-resolution MPRAGE images and co-registering these to MEGA-LASER 3D MRSI data, an automated regional quantification of neurochemical levels is demonstrated for the example of the thalamus. Thalamic gamma-aminobutyric acid + co-edited macromolecules (GABA+) levels from 21 healthy subjects scanned at 3T were cross-validated both against a single-voxel (SV) MEGA-PRESS acquisition in the same subjects and same scan sessions, as well as alternative MRSI processing techniques (ROI approach, four-voxel approach) using Pearson correlation analysis. In addition, reproducibility was compared across the MRSI processing techniques in test-retest data from 14 subjects. The atlas-based approach showed a significant correlation with SV MEGA-PRESS (correlation coefficient r (GABA+) = 0.63, p value

Issues with model fitting (i.e. suboptimal standard deviation, linewidth/full-width-at-half-maximum, and/or signal-to-noise ratio) in multi-voxel MRI spectroscopy, or chemical shift imaging (CSI), can result in the significant loss of usable voxels. A potential solution to minimize this problem is to estimate the value of unusable voxels by utilizing information from reliable voxels in the same image. We assessed an image restoration method called inpainting as a tool to restore unusable voxels and compared it with traditional interpolation methods (nearest neighbor, trilinear interpolation and tricubic interpolation). We applied these techniques to N-acetylaspartate (NAA) spectroscopy maps from a CSI dataset. Inpainting exhibited superior performance (lower normalized root-mean-square errors, NRMSE) compared to all other methods considered (p’sClinical RelevanceThe presence of missing voxels can be problematic, particularly when data are analyzed in standard space, given that only voxels that are contributed to by all participants can be interrogated in these analyses. Inpainting is a promising approach for recovering unusable or missing voxels in voxelwise analyses, particularly in imaging modalities characterized by low SNR such as CSI.

Objective To test the safety, tolerability, and urate‐elevating capability of the urate precursor inosine taken orally or by feeding tube in people with amyotrophic lateral sclerosis (ALS). Methods This was a pilot, open‐label trial in 25 participants with ALS. Treatment duration was 12 weeks. The dose of inosine was titrated at pre‐specified time points to elevate serum urate levels to 7–8 mg/dL. Primary outcomes were safety (as assessed by the occurrence of adverse events [AEs]) and tolerability (defined as the ability to complete the 12‐week study on study drug). Secondary outcomes included biomarkers of oxidative stress and damage. As an exploratory analysis, observed outcomes were compared with a virtual control arm built using prediction algorithms to estimate ALSFRS‐R scores. Results Twenty‐four out of 25 participants (96%) completed 12 weeks of study drug treatment. One participant was unable to comply with study visits and was lost to follow‐up. Serum urate rose to target levels in 6 weeks. No serious AEs attributed to study drug and no AEs of special concern, such as urolithiasis and gout, occurred. Selected biomarkers of oxidative stress and damage had significant changes during the study period. Observed changes in ALSFRS‐R did not differ from baseline predictions. Interpretation Inosine appeared safe, well tolerated, and effective in raising serum urate levels in people with ALS. These findings, together with epidemiological observations and preclinical data supporting a neuroprotective role of urate in ALS models, provide the rationale for larger clinical trials testing inosine as a potential disease‐modifying therapy for ALS.

Metabolic imaging can map spatially abnormal molecular pathways with higher specificity for cancer compared to anatomical imaging. However, acquiring high resolution metabolic maps similar to anatomical MRI is challenging due to low metabolite concentrations, and alternative approaches that increase resolution by post-acquisition image processing can mitigate this limitation. We developed deep learning super-resolution MR spectroscopic imaging (MRSI) to map tumor metabolism in patients with mutant IDH glioma. We used a generative adversarial network (GAN) architecture comprised of a UNet neural network as the generator network and a discriminator network for adversarial training. For training we simulated a large data set of 9600 images with realistic quality for acquired MRSI to effectively train the deep learning model to upsample by a factor of four. Two types of training were performed: 1) using only the MRSI data, and 2) using MRSI and prior information from anatomical MRI to further enhance structural details. The performance of super-resolution methods was evaluated by peak SNR (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM). After training on simulations, GAN was evaluated on measured MRSI metabolic maps acquired with resolution 5.2×5.2 mm2 and upsampled to 1.3×1.3 mm2. The GAN trained only on MRSI achieved PSNR = 27.94, SSIM = 0.88, FSIM = 0.89. Using prior anatomical MRI improved GAN performance to PSNR = 30.75, SSIM = 0.90, FSIM = 0.92. In the patient measured data, GAN super-resolution metabolic images provided clearer tumor margins and made apparent the tumor metabolic heterogeneity. Compared to conventional image interpolation such as bicubic or total variation, deep learning methods provided sharper edges and less blurring of structural details. Our results indicate that the proposed deep learning method is effective in enhancing the spatial resolution of metabolite maps which may better guide treatment in mutant IDH glioma patients.

Low sensitivity MR techniques such as magnetic resonance spectroscopic imaging (MRSI) greatly benefit from the gain in signal-to-noise provided by ultra-high field MR. High-resolution and whole-slab brain MRSI remains however very challenging due to lengthy acquisition, low signal, lipid contamination and field inhomogeneity. In this study, we propose an acquisition-reconstruction scheme that combines 1H free-induction-decay (FID)-MRSI sequence, short TR acquisition, compressed sensing acceleration and low-rank modeling with total-generalized-variation constraint to achieve metabolite imaging in two and three dimensions at 7 Tesla. The resulting images and volumes reveal highly detailed distributions that are specific to each metabolite and follow the underlying brain anatomy. The MRSI method was validated in a high-resolution phantom containing fine metabolite structures, and in five healthy volunteers. This new application of compressed sensing acceleration paves the way for high-resolution MRSI in clinical setting with acquisition times of 5 min for 2D MRSI at 2.5 mm and of 20 min for 3D MRSI at 3.3 mm isotropic.

Nervous system injury is a frequent result of cancer therapy involving cranial irradiation, leaving patients with marked memory and other neurobehavioral disabilities. Here, we report an unanticipated link between bone marrow and brain in the setting of radiation injury. Specifically, we demonstrate that bone marrow-derived monocytes and macrophages are essential for structural and functional repair mechanisms, including regeneration of cerebral white matter and improvement in neurocognitive function. Using a granulocyte-colony stimulating factor (G-CSF) receptor knockout mouse model in combination with bone marrow cell transplantation, MRI, and neurocognitive functional assessments, we demonstrate that bone marrow-derived G-CSF-responsive cells home to the injured brain and are critical for altering neural progenitor cells and brain repair. Additionally, compared with untreated animals, animals that received G-CSF following radiation injury exhibited enhanced functional brain repair. Together, these results demonstrate that, in addition to its known role in defense and debris removal, the hematopoietic system provides critical regenerative drive to the brain that can be modulated by clinically available agents.