Kimberly L. Chan

Purpose: To evaluate the effects of different MM fitting strategies on gamma-aminobutyric acid (GABA) estimates using MEGA-PRESS at 3T. Methods: Thirteen healthy individuals were scanned in the parietal lobe and centrum semiovale. GABA-edited scans were performed at TE=64ms (GABA+MMs) and TE=80ms (MM-suppressed GABA). MM-edited spectra were acquired using MEGA-PRESS with an inversion pre-pulse. MM peaks at 3.0 (MM _3.0 ), 2.05 (MM _2.05 ), and 0.94 (MM _0.94 ) ppm were parameterized to form five MM basis categories. Cohort-mean/individual MM basis sets were constructed with all MM peaks combined (MM _together ), or with MM _2.05 fit separately from MM _0.94 +MM _3.0 (MM ₂₊₁ ). Simulated-MM bases were scaled by a participant-specific MM _0.94/ MM _3.0 amplitude-ratio estimated from 64-224 transients. Fit quality numbers (FQNs) between the five MM parameterization methods were calculated to compare fit quality. Intraclass and Pearson’s correlations were calculated to assess for agreement of GABA concentrations between the MM-suppressed GABA scan and the GABA+ scan with MMs fit out. Results: GABA concentrations derived from fitting with simulated-/individual-MM bases and MM _2.05 fit separately lead to good–excellent agreements and high correlations with MM-suppressed GABA concentrations. Fit quality was significantly (51–63%) worse when fit with the MM _together methods. GABA concentrations derived from fitting with a simulated MM basis demonstrated strong agreement with MM-suppressed GABA concentrations even when estimating the MM _0.94/ MM _3.0 amplitude-ratio from a reduced number of transients (64–112). Conclusion: A participant-specific MM basis with MM _2.05 excluded/fit separately yielded accurate GABA concentrations and superior fits. An accurate estimation of the MM _0.94 /MM _3.0 amplitude-ratio can be achieved from a short 3-minute MM scan.

Purpose: To achieve the simultaneous acquisition of gamma‐aminobutyric acid (GABA) and glycine (Gly) using a MEGA‐PRESS sequence with an optimized TE at 3T. Methods: MEGA‐PRESS simulations were performed at TEs 60‐88 ms to determine the optimal TE for Gly detection with minimal myo‐Inositol (mI) overlap and maximal GABA detection sensitivity. MEGA‐PRESS data were acquired in the occipital lobe of 6 healthy subjects at TEs of 64 and 68 ms. GABA+ levels, between‐acquisition (SUM (edit‐ON+edit‐OFF) and edit‐OFF) and inter‐subject coefficient‐of‐variation (CVs) and mI, Gly, and glucose CRLBs were evaluated to assess fit reliability. The residuals of the edit‐OFF and SUM fits were compared with and without Gly in the basis set to examine the effect of Gly on fit accuracy and metabolite quantification. Results: Simulations indicated that optimal Gly detection with minimal overlap from mI is observed at a TE of 64 ms. Simulations and in vivo experiments indicate that this TE resulted in no reduction in GABA+ sensitivity relative to the commonly used TE of 68 ms. Gly between‐acquisition and inter‐subject CVs and CRLBs were substantially lower at a TE of 64 ms than at a TE 68 ms. Spectral fits with Gly excluded from the basis set resulted in a significant increase in CRLBs and fit residuals for mI and glucose at a TE of 64 ms, but not at a TE of 68 ms. Conclusion: The simultaneous detection of GABA+ from the difference spectrum and Gly from the edit‐OFF/SUM spectra is possible using a MEGA‐PRESS sequence at a TE of 64 ms.

MRSI is a non‐invasive tool for mapping metabolic distributions in multi‐focal or other diseases where the location of abnormalities may be uncertain. High‐concentration metabolites can be investigated at 3T using non‐edited MRSI, whereas lower‐concentration metabolites, such as GABA, typically require specialized editing techniques because of spectral overlap. This study reports on the reproducibility of a protocol containing both co‐localized short‐TE and GABA‐edited multi‐slice spin‐echo 2D MRSI. Multi‐slice, short‐TE (TE 20 ms) and GABA‐edited (TE 68 ms) MRSI at a nominal spatial resolution of 2.2 cm ³ was performed twice (7 to 14 days apart) at 3T on 11 healthy volunteers (age range 7 to 43 years). Data analysis was performed in the “Osprey” software package, including retrospective motion compensation, consensus‐recommended processing, and linear‐combination modeling. Metabolite estimates for six metabolites were quantified relative to total creatine (tCr) and water in 14 regions of interest. Reproducibility was assessed using intra‐ and inter‐subject coefficients of variation. Short‐TE MRSI metabolite estimates for total N‐acetylaspartate (tNAA), tCr, total choline (tCho), myo‐inositol (mI), and the sum of glutamate and glutamine (Glx) were found to be highly reproducible for both creatine‐ and water‐referenced concentration estimates, with 77% of the regions of interest meeting the quality‐control criteria for both visits and 96% for at least one visit. Average intra‐subject CVs were 5.8% and 4.8%, and inter‐subject CVs were 11.1% and 9.7% for water‐referenced and tCr‐referenced estimates, respectively. For GABA+ (GABA + macromolecules) estimates, 46% of the voxels of interest met quality‐control criteria for both visits, and 82% for at least one visit. In the remaining datasets, the average intra‐subject CVs were 13.5% for both quantification methods, and the inter‐subject CVs were 13.5% and 16.9% for water‐referenced and creatine‐referenced estimates, respectively. 3T‐MRSI sequences can achieve reproducible mapping with extended brain coverage of five major metabolites (tNAA, tCr, tCho, mI, and Glx). Reproducibility assessment for GABA+ mapping remains challenging, with 18% of the data being rejected in at least one visit, but it yielded acceptable reproducibility in datasets that met quality control criteria in both visits (46%).

Purpose: To investigate the TE dependence of the edited 2‐hydroxyglutarate (2HG) signal, its separation from co‐edited glutamate plus glutamine (Glx), and fit accuracy in the presence of nuisance signals using a MEGA‐PRESS sequence. Methods: Simulations were performed at TEs 70–160 ms to assess the signal intensity and 2HG‐Glx overlap as a function of TE. The effect of the 2HG‐Glx spectral overlap on the fit accuracy of 2HG was evaluated on simulated 2HG‐edited spectra with in vivo parameter variations. Data were acquired at TEs of 70 and 90 ms in 13 glioma patients to estimate the TE‐dependence of the 2HG and Glx signal intensity and at a TE of 120 ms in eight glioma patients to estimate the in vivo 2HG, Glx, and water T2 relaxation times. Results: A TE of 90 ms was found to produce a maximal 2HG integral, which was 23% larger than that at a TE of 70 ms in vivo without a significant increase in 2HG‐Glx overlap. Lipid and residual water were 26% and 16% lower, respectively, at a TE of 90 ms versus 70 ms. Fit‐quality numbers were 49% lower at a TE of 90 ms versus 70 ms, indicating enhanced fits at a TE of 90 ms. The in vivo T2 relaxation times of 2HG, Glx, and water were 264, 177, and 110 ms, respectively. Conclusion: A TE of 90 ms was best with a maximal 2HG signal, minimal 2HG‐Glx overlap, and minimal residual water and lipid contamination.

Reproducible and accurate fitting of the magnetic resonance spectrum is critical for estimating metabolite concentrations. We have previously developed a fitting software called ProFit-1D which was shown to fit 9.4T semi-LASER data from the human brain with high accuracy and precision. In this study, we adapted ProFit-1D to fit J-difference edited spectra acquired at a clinical field strength of 3T and to assessed its performance in simulated and in vivo data. ProFit-1D was adapted to fit J-difference edited data with alterations to the fitting range to exclude the 1.3 ppm lipid resonance, starting T ₂ relaxation constants, initial fit parameters, and adaptive spectral baseline determination. The accuracy of ProFit-1D was systematically evaluated on simulated GABA-edited and 2-hydroxyglutarate-edited (2HG-edited) data with different types of in vivo parameter variations and compared to that of LCModel and Gannet, two software commonly used to fit J-difference edited data. The precision of ProFit-1D was also evaluated in GABA-edited spectra acquired in vivo in the occipital cortex (OCC) and medial prefrontal cortex (mPFC) of healthy participants at 3T using subsets of averages and compared to that of LCModel and Gannet. The 2HG fit error was also evaluated for ProFit-1D in 2HG-edited spectra acquired in glioma patients and compared to that of LCModel. Overall, it was found that ProFit-1D generally produced fits with low parameter fit errors across a variety of parameter variations. GABA, glutamate plus glutamine (Glx), and 2HG levels were also more accurately estimated with ProFit-1D than with LCModel and Gannet across different spectral disturbances and simulated concentrations. ProFit-1D was found to be as precise as LCModel and more precise than Gannet in estimating GABA and Glx. 2HG fit errors were 45% lower with ProFit-1D than with LCModel. Thus, ProFit-1D was found to produce high-quality fits to J- difference edited data with high accuracy and precision.

This review article investigates the utilization of MRS in the setting of cervical cancer. A variety of different techniques have been used in this space including single-voxel techniques such as point-resolved spectroscopy (PRESS) and stimulated echo acquisition mode spectroscopy (STEAM). Furthermore, the experimental parameters for these acquisitions including field strength, repetition times (TR), and echo times (TE) vary greatly. This study critically examines eleven MRS studies that focus on cervical cancer. Out of the eleven studies, ten studies utilized PRESS acquisition, while the remaining study used STEAM acquisition. These studies generally showed that the choline signal is altered in cervical cancer (4/11 studies), the lipid signal is generally increased in cervical cancer or the lipid distribution is changed (5/11 studies), and that diffusion-weighted imaging (DWI) can quantitatively detect lower apparent diffusion coefficient (ADC) values in cervical cancer (2/11 studies). Two studies also investigated the role of MRS for monitoring treatment response and demonstrated mixed results regarding choline signal, and one of these studies showed increased lipid signal for non-responders. There are several new MRS technologies that have yet to be implemented for cervical cancer including advanced spectroscopic imaging and artificial intelligence, and those technologies are also discussed in the article.

Lafora disease is a fatal teenage-onset progressive myoclonus epilepsy and neurodegenerative disease associated with polyglucosan bodies. Polyglucosans are long-branched and as a result precipitation- and aggregation-prone glycogen. In mouse models, downregulation of glycogen synthase, the enzyme that elongates glycogen branches, prevents polyglucosan formation and rescues Lafora disease. Mouse work, however, has not yet revealed the mechanisms of polyglucosan generation, and few in vivo human studies have been performed. Here, non-invasive in vivo magnetic resonance spectroscopy (¹H and ³¹P) was applied to test scan feasibility and assess neurotransmitter balance and energy metabolism in Lafora disease towards a better understanding of pathogenesis. Macromolecule-suppressed gamma-aminobutyric acid (GABA)-edited ¹H magnetic resonance spectroscopy and ³¹P magnetic resonance spectroscopy at 3 and 7 tesla, respectively, were performed in 4 Lafora disease patients and a total of 21 healthy controls (12 for the ¹H magnetic resonance spectroscopy and 9 for the ³¹PMRS). Spectra were processed using in-house software and fit to extract metabolite concentrations. From the ¹H spectra, we found 33% lower GABA concentrations (P = 0.013), 34% higher glutamate + glutamine concentrations (P = 0.011) and 24% lower N-acetylaspartate concentrations (P = 0.0043) in Lafora disease patients compared with controls. From the ³¹P spectra, we found 34% higher phosphoethanolamine concentrations (P = 0.016), 23% lower nicotinamide adenine dinucleotide concentrations (P = 0.003), 50% higher uridine diphosphate glucose concentrations (P = 0.004) and 225% higher glucose 6-phosphate concentrations in Lafora disease patients versus controls (P = 0.004). Uridine diphosphate glucose is the substrate of glycogen synthase, and glucose 6-phosphate is its extremely potent allosteric activator. The observed elevated uridine diphosphate glucose and glucose 6-phosphate levels are expected to hyperactivate glycogen synthase and may underlie the generation of polyglucosans in Lafora disease. The increased glutamate + glutamine and reduced GABA indicate altered neurotransmission and energy metabolism, which may contribute to the disease’s intractable epilepsy. These results suggest a possible basis of polyglucosan formation and potential contributions to the epilepsy of Lafora disease. If confirmed in larger human and animal model studies, measurements of the dysregulated metabolites by magnetic resonance spectroscopy could be developed into non-invasive biomarkers for clinical trials.

Purpose: To further develop MultiNet GRAPPA, a neural‐network‐based reconstruction, for lower SNR proton MRSI (¹H MRSI) data using adapted undersampling schemes and improved training sets.Methods: ¹H FID‐MRSI data and an anatomical image for GRAPPA reconstruction were acquired in two slices in the human brain (n = 6) at 7T. MRSI data were retrospectively undersampled for a 4×, 6×, and 7× acceleration rate. Signal‐to‐noise, relative error (RE) between accelerated and fully sampled metabolic maps, RMS of the lipid artifacts, and fitting reliability were compared across acceleration rates, to the fully sampled data, and with different kinds and amounts of training images.Results: Training with semi‐synthetic images resulted in higher SNR and lower lipid RMS relative to training with acquired images from one or several subjects. SNR increased with the number of semi‐synthetic training images and the 4× accelerated data retains ∼30% more SNR than other accelerated data. Spectra reconstructed with 20 semi‐synthetic averages retained ∼100% more SNR and had ∼5% lower lipid RMS than those reconstructed with the center k‐space points of one image as was originally proposed for very high SNR MRSI data and had higher fitting reliability. The metabolite RE was lowest when training with 20‐semi‐synthetic training images and highest when training with the center k‐space points of one image.Conclusion: MultiNet GRAPPA is feasible with lower SNR ¹H MRSI data if 20‐semi‐synthetic training images are used at a 4× acceleration rate. This acceleration rate provided the best trade‐off between scan time and spectral SNR.

Purpose: To combine metabolite cycling with J‐difference editing (MC MEGA) to allow for prospective frequency correction at each transient without additional acquisitions and compare it to water‐suppressed MEGA‐PRESS (WS MEGA) editing with intermittent prospective frequency correction.Methods: Macromolecule‐suppressed gamma aminobutyric acid (GABA)‐edited experiments were performed in a phantom and in the occipital lobe (OCC) (n= 12) and medial prefrontal cortex (mPFC) (n= 8) of the human brain. Water frequency consistency and average offset over acquisition time were compared. GABA multiplet patterns, signal intensities, and choline subtraction artifacts were evaluated. In vivo GABA concentrations were compared and related to frequency offset in the OCC.Results: MC MEGA was more stable with 21% and 32% smaller water frequency SDs in the OCC and mPFC, respectively. MC MEGA also had 39% and 40% smaller average frequency offsets in the OCC and mPFC, respectively. Phantom GABA multiplet patterns and signal intensities were similar. In vivo GABA concentrations were smaller in MC MEGA than in WS MEGA, with median (interquartile range) of 2.52 (0.27) and 2.29 (0.19) institutional units (i.u.), respectively in the OCC scans without prior DTI, and 0.99 (0.3) and 1.72 (0.5), respectively in the mPFC. OCC WS MEGA GABA concentrations, but not MC MEGA GABA concentrations were moderately correlated with frequency offset. mPFC WS MEGA spectra contained significantly more subtraction artifacts than MC MEGA spectra.Conclusion: MC MEGA is feasible and allows for prospective frequency correction at every transient. MC MEGA GABA concentrations were not biased by frequency offsets and contained less subtraction artifacts compared to WS MEGA.

In a multisite study of brain MR spectroscopy performed at 26 sites, interindividual differences were found as the main contributor to variability, demonstrating that appropriately analyzed MR spec... Background The hardware and software differences between MR vendors and individual sites influence the quantification of MR spectroscopy data. An analysis of a large data set may help to better und...