Magnetic Resonance Spectroscopic Imaging Research Articles

Microstructural and Metabolic Changes in Normal Aging Human Brain Studied with Combined Whole-Brain MR Spectroscopic Imaging and Quantitative MR Imaging

PurposeThis study aimed to detect age-related brain metabolic and microstructural changes in healthy human brains by the use of whole-brain proton magnetic resonance spectroscopic imaging (1H‑MRSI) and quantitative MR imaging (qMRI).MethodsIn this study, 60 healthy participants with evenly distributed ages (between 21 and 69 years) and sex underwent MRI examinations at 3T including whole-brain 1H‑MRSI. The concentrations of the metabolites N‑acetylaspartate (NAA), choline-containing compounds (Cho), total creatine and phosphocreatine (tCr), glutamine and glutamate (Glx), and myo-inositol (mI), as well as the brain relaxation times T2, T2’ and T1 were measured in 12 regions of interest (ROI) in each hemisphere. Correlations between measured parameters and age were estimated with linear regression analysis and Pearsonʼs correlation test.ResultsSignificant age-related changes of brain regional metabolite concentrations and tissue relaxation times were found: NAA decreased in eight of twelve ROIs, Cho increased in three ROIs, tCr in four ROIs, and mI in three ROIs. Glx displayed a significant decrease in one ROI and an increase in another ROI. T1 increased in four ROIs and T2 in one ROI, while T2’ decreased in two ROIs. A negative correlation of tCr concentrations with T2’ relaxation time was found in one ROI as well as the positive correlations of age-related T1 relaxation time with concentrations of tCr, mI, Glx and Cho in another ROI.ConclusionNormal aging in human brain is associated with coexistent brain regional metabolic alterations and microstructural changes, which may be related to age-related decline in cognitive, affective and psychomotor domains of life in the older population.

SLIPMAT: A pipeline for extracting tissue-specific spectral profiles from 1H MR spectroscopic imaging data

1H Magnetic Resonance Spectroscopy (MRS) is an important non-invasive tool for measuring brain metabolism, with numerous applications in the neuroscientific and clinical domains. In this work we present a new analysis pipeline (SLIPMAT), designed to extract high-quality, tissue-specific, spectral profiles from MR spectroscopic imaging data (MRSI). Spectral decomposition is combined with spatially dependant frequency and phase correction to yield high SNR white and grey matter spectra without partial-volume contamination. A subsequent series of spectral processing steps are applied to reduce unwanted spectral variation, such as baseline correction and linewidth matching, before direct spectral analysis with machine learning and traditional statistical methods. The method is validated using a 2D semi-LASER MRSI sequence, with a 5-minute duration, from data acquired in triplicate across 8 healthy participants. Reliable spectral profiles are confirmed with principal component analysis, revealing the importance of total-choline and scyllo-inositol levels in distinguishing between individuals – in good agreement with our previous work. Furthermore, since the method allows the simultaneous measurement of metabolites in grey and white matter, we show the strong discriminative value of these metabolites in both tissue types for the first time. In conclusion, we present a novel and time efficient MRSI acquisition and processing pipeline, capable of detecting reliable neuro-metabolic differences between healthy individuals, and suitable for the sensitive neurometabolic profiling of in-vivo brain tissue.

Conductor Losses in Radiofrequency Coils for Magnetic Resonance below 3T: Estimation Methods and Minimization Strategies

The design of optimized radiofrequency (RF) coils is a fundamental task for maximizing the signal-to-noise ratio (SNR) in Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) applications. An efficient coil should be designed by minimizing the coil noise with respect to the sample noise, since coil conductor resistance affects data quality by reducing the SNR, especially for coils tuned to a low frequency. Such conductor losses strongly depend on the frequency (due to the skin effect) and on the conductor cross-sectional shape (strip or wire). This paper reviews the different methods for estimating conductor losses in RF coils for MRI/MRS applications, comprising analytical formulations, theoretical/experimental hybrid approaches and full-wave simulations. Moreover, the different strategies for minimizing such losses, including the use of Litz wire, cooled and superconducting coils, are described. Finally, recent emerging technologies in RF coil design are briefly reviewed.

Spatio-temporal reconstruction of substance dynamics using compressed sensing in multi-spectral magnetic resonance spectroscopic imaging

The objective of our study is to observe dynamics of multiple substances in vivo with high temporal resolution from multi-spectral magnetic resonance spectroscopic imaging (MRSI) data. The multi-spectral MRSI can effectively separate spectral peaks of multiple substances and is useful to measure spatial distributions of substances. However it is difficult to measure time-varying substance distributions directly by ordinary full sampling because the measurement requires a significantly long time. In this study, we propose a novel method to reconstruct the spatio-temporal distributions of substances from randomly undersampled multi-spectral MRSI data on the basis of compressed sensing (CS) and the partially separable function model with base spectra of substances. In our method, we have employed spatio-temporal sparsity and temporal smoothness of the substance distributions as prior knowledge to perform CS. By directly reconstructing the spatio-temporal distributions of the substances themselves without reconstructing the spectra, this method significantly reduces the amount of MRSI data required per single time frame. We have formulated a regularized minimization problem for reconstruction and solved it by the alternating direction method of multipliers (ADMM). The effectiveness of our method has been evaluated using phantom data sets of glass tubes filled with glucose or lactate solution in increasing amounts over time and animal data sets of a tumor-bearing mouse to observe the metabolic dynamics involved in the Warburg effect in vivo. The reconstructed results are consistent with the expected behaviors, showing that our method can reconstruct the spatio-temporal distribution of substances with a temporal resolution of four seconds which is extremely short time scale compared with that of full sampling. Since this method utilizes only prior knowledge naturally assumed for the spatio-temporal distributions of substances and is independent of the number of the spectral and spatial dimensions or the acquisition sequence of MRSI, it is expected to contribute to revealing the underlying substance dynamics in MRSI data already acquired or to be acquired in the future.

Biot–Savart-Based Design and Workbench Validation at 100 MHz of Transverse Field Surface RF Coils

Radiofrequency (RF) surface coils are extensively used as receivers in magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) systems thanks to their high signal-to-noise ratio (SNR). For specific magnetic resonance applications, the design of dedicated RF surface coils with a transverse (to the coil’s plane) RF magnetic field pattern can be necessary. Such transverse-field RF coils are constituted by several central linear (parallel or crossing) conductor elements connected by return current paths. Typically, the outer shape of such RF coils is circular or squared, although other geometries can be used. This paper describes the implementation and validation of a transverse-field RF surface coil simulator based on magnetostatic analysis, which permits the design and optimization of square butterfly and figure-of-eight RF coils with adjustable size and mutual distance between the central linear current elements. The simulation results, compared with the ones provided by a standard square loop RF coil, were validated with 100 MHz workbench measurements performed on three home-built prototypes. Finally, two novel quadrature RF coil structures designed by overlapping two orthogonal square butterfly and figure-of-eight RF coils were simulated and theoretically characterized. The RF coils described here should be suitable for a wide range of MRI/MRS preclinical/clinical applications, mainly at fields below 3 T.

Vascular disease risk factors in multiple sclerosis: Effect on metabolism and brain volumes.

Vascular disease risk factors (VDRF) such as hypertension, hyperlipidemia, obesity, diabetes and heart disease likely play a role in disease progression in people with multiple sclerosis (PwMS) (Marrie, Rudick etal. 2010). Studies exploring the mechanistic connection between vascular disease and MS disease progression are scant. We hypothesized that phosphate energy metabolism impairment in PwMS with VDRFs (VDRF+) will be greater compared to PwMS without VDRFs (VDRF-) and is related to increased brain atrophy in VDRF+. To test this hypothesis, we planned to study the differences in the high energy phosphate (HEP) metabolites in cerebral gray matter as assessed by 31P magnetic resonance spectroscopic imaging (MRSI) and MRI brain volumetric in the VDRF+ and VDRF- PwMS at four different timepoints over a 3 yearlong period using a 7T MR system. We present here the results from the cross-sectional evaluation of HEP metabolites and brain volumes. We also evaluated the differences in clinical impairment, blood metabolic biomarkers and quality of life in VDRF+ and VDRF- PwMS in this cohort. Group differences in high energy phosphate metabolites were assessed from a volume of interest in the occipital region using linear mixed models. Brain parenchymal and white matter lesion volumes were determined from MR anatomic images. We present here the cross-sectional analysis of the baseline data collected as part of a longitudinal 3 yearlong study where we obtained baseline and subsequent 6-monthly clinical and laboratory data and annual 7T MRI volumetric and 31P MR spectroscopic imaging (MRSI) data on 52 PwMS with and without VDRF. Key clinical and laboratory outcomes included: body mass index (BMI), waist and thigh circumferences and disability [Expanded Disability Status Scale (EDSS)], safety (complete blood count with differential, complete metabolic), lipid panel including total cholesterol and HbA1C. We analyzed clinical and laboratory data for the group differences using student's t or χ2 test. We investigated relationship between phosphate metabolites and VDRF using mixed effect linear regression. Complete MRI data were available for 29 VDRF+, age 56.3 (6.8) years [mean (SD)] (83% female), and 23 VDRF-, age 52.5 (7.5) years (57% female) individuals with MS. The mean value of normalized adenosine triphosphate (ATP) (calculated as the ratio of ATP to total phosphate signal in a voxel) was decreased by 4.5% (p < .05) in VDRF+ compared to VDRF- MS group. White matter lesion (WML) volume fraction in VDRF+ individuals {0.007 (0.007)} was more than doubled compared to VDRF- participants {0.003 (0.006), p= .02}. We found significantly lower brain ATP and higher inorganic phosphate (Pi) in those PwMS with VDRFs compared to those without. ATP depletion may reflect mitochondrial dysfunction. Ongoing longitudinal data analysis from this study, not presented here, will evaluate the relationship of phosphate metabolites, brain atrophy and disease progression in PwMS with and without vascular disease.

Early biomarkers of neurodevelopmental disorders in preterm infants: protocol for a longitudinal cohort study

IntroductionPreterm (PT) infants are at high likelihood for poor neurodevelopmental outcomes, including autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD) and other neurodevelopmental disorders (NDDs), which could considerably impair the...

Gut and oral microbiome modulate molecular and clinical markers of schizophrenia-related symptoms: A transdiagnostic, multilevel pilot study

Although increasing evidence links microbial dysbiosis with the risk for psychiatric symptoms through the microbiome-gut-brain axis (MGBA), the specific mechanisms remain poorly characterized. In a diagnostically heterogeneous group of treated psychiatric cases and nonpsychiatric controls, we characterized the gut and oral microbiome, plasma cytokines, and hippocampal inflammatory processes via proton magnetic resonance spectroscopic imaging (1H-MRSI). Using a transdiagnostic approach, these data were examined in association with schizophrenia-related symptoms measured by the Positive and Negative Syndrome Scale (PANSS). Psychiatric cases had significantly greater heterogeneity of gut alpha diversity and an enrichment of pathogenic taxa, like Veillonella and Prevotella, in the oral microbiome, which was an accurate classifier of phenotype. Cases exhibited significantly greater positive, negative, and general PANSS scores that uniquely correlated with bacterial taxa. Strong, positive correlations of bacterial taxa were also found with cytokines and hippocampal gliosis, dysmyelination, and excitatory neurotransmission. This pilot study supports the hypothesis that the MGBA influences psychiatric symptomatology in a transdiagnostic manner. The relative importance of the oral microbiome in peripheral and hippocampal inflammatory pathways was highlighted, suggesting opportunities for probiotics and oral health to diagnose and treat psychiatric conditions.

Evaluation of Key Molecular Markers in Adult Diffuse Gliomas Based on a Novel Combination of Diffusion and Perfusion MRI and MR Spectroscopy.

Preoperative identification of isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status could help clinicians select the optimal therapy in patients with diffuse glioma. Although, the value of multimodal intersection was underutilized. To evaluate the value of quantitative MRI biomarkers for the identification of IDH mutation and 1p/19q codeletion in adult patients with diffuse glioma. Retrospective. Two hundred sixteen adult diffuse gliomas with known genetic test results, divided into training (N = 130), test (N = 43), and validation (N = 43) groups. Diffusion/perfusion-weighted-imaging sequences and multivoxel MR spectroscopy (MRS), all 3.0 T using three different scanners. The apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) of the core tumor were calculated to identify IDH-mutant and 1p/19q-codeleted statuses and to determine cut-off values. ADC models were built based on the 30th percentile and lower, CBV models were built based on the 75th centile and higher (both in five centile steps). The optimal tumor region was defined and the metabolite concentrations of MRS voxels that overlapped with the ADC/CBV optimal region were calculated and added to the best-performing diagnostic models. DeLong's test, diagnostic test, and decision curve analysis were performed. A P value <0.05 was considered to be statistically significant. Almost all ADC models achieved good performance in identifying IDH mutation status, among which ADC_15th was the most valuable parameter (threshold = 1.186; Youden index = 0.734; AUC_train = 0.896). The differential power of CBV histogram metrics for predicting 1p/19q codeletion outperformed ADC histogram metrics, and the CBV_80th-related model performed best (threshold = 1.435; Youden index = 0.458; AUC_train = 0.724). The AUCs of ADC_15th and CBV_80th models in the validation set were 0.857 and 0.733. These models tended to improve after incorporation of N-acetylaspartate/total_creatine and glutamate-plus-glutamine/total_creatine, respectively. The intersection of ADC-, CBV-based histogram and MRS provide a reliable paradigm for identifying the key molecular markers in adult diffuse gliomas. 3 TECHNICAL EFFICACY: Stage 3.

Glioma grading using multiparametric MRI: head-to-head comparison among dynamic susceptibility contrast, dynamic contrast-enhancement, diffusion-weighted images, and MR spectroscopy

PurposeTo assess the diagnostic accuracy of dynamic susceptibility contrast, dynamic contrast-enhancement, MR spectroscopy (MRS), and diffusion-weighted imaging for differentiating high-grade (HGGs) from low-grade gliomas (LGGs). MethodsSeventy-two patients (16 LGGs, 56 HGGs) with pathologically confirmed gliomas were retrospectively included. From three-dimensionally segmented tumor, histogram analyses of relative cerebral blood volume (rCBV), volume transfer constant (Ktrans), and apparent diffusion coefficient (ADC) were performed. Choline-to-creatinine ratio (Cho/Cr) was calculated using MRS. Logistic regression analyses were performed to differentiate HGGs (grade ≥ 3) from LGGs (grade ≤ 2). Areas under the receiver operating characteristics curves (AUC) were plotted. Subgroup analysis was performed between IDH-wildtype glioblastomas and IDH-mutant astrocytomas. Pairwise Spearman’s correlation coefficients (ρ) were computed. ResultsHGGs had higher 95th percentile rCBV, Ktrans and Cho/Cr (P < 0.01) than LGGs. AUC of 95th percentiles of rCBV and Ktrans were 0.79 (95% CI, 0.67–0.91) and 0.74 (95% CI, 0.59–0.88), respectively. AUC of 5th percentile of ADC was 0.63 (95% CI, 0.48–0.79), and that of Cho/Cr was 0.67 (95% CI, 0.52–0.81). IDH-wildtype glioblastomas and IDH-mutant astrocytomas showed significantly different 95th percentile rCBV (P = 0.04) and Ktrans (P < 0.01), with Ktrans showing the highest AUC (0.73, 95% CI 0.57–0.89) in IDH status prediction. Moderate correlations were observed between 95th percentile rCBV and Ktrans (ρ = 0.47), Cho/Cr (ρ = 0.40), and 5th percentile ADC (ρ = -0.36) (all P < 0.01). ConclusionsThe 95th percentile rCBV may be most helpful in discriminating HGGs from LGGs. The 95th percentile Ktrans may aid predicting IDH status of diffuse gliomas.

Inductively coupled, transmit-receive coils for proton MRI and X-nucleus MRI/MRS in small animals

We report several inductively coupled RF coil designs that are very easy to construct, produce high signal-to-noise ratio (SNR) and high spatial resolution while accommodating life support, anesthesia and monitoring in small animals. Inductively coupled surface coils were designed for hyperpolarized 13 C MR spectroscopic imaging (MRSI) of mouse brain, with emphases on the simplicity of the circuit design, ease of use, whole-brain coverage, and high SNR. The simplest form was a resonant loop designed to crown the mouse head for a snug fit to achieve full coverage of the brain with high sensitivity when inductively coupled to a broadband pick-up coil. Here, we demonstrated the coil's performance in hyperpolarized 13 C MRSI of a normal mouse and a glioblastoma mouse model at 4.7 T. High SNR exceeding 70:1 was obtained in the brain with good spatial resolution (1.53 mm x 1.53 mm). Similar inductively coupled loop for other X-nuclei can be made very easily in a few minutes and achieve high performance, as demonstrated in 31 P spectroscopy. Similar design concept was expanded to splitable, inductively coupled volume coils for high-resolution proton MRI of marmoset at 3T and 9.4T, to easily accommodate head restraint, vital-sign monitoring, and anesthesia delivery.

Grading Clear Cell Renal Cell Carcinoma Grade Using Diffusion Relaxation Correlated MR Spectroscopic Imaging.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC, and accurate grading is crucial for prognosis and treatment selection. Biopsy is the reference standard for grading, but MRI methods can improve and complement the grading procedure. Assess the performance of diffusion relaxation correlation spectroscopic imaging (DR-CSI) in grading ccRCC. Prospective. 79 patients (age: 58.1 +/- 11.5 years; 55 male) with ccRCC confirmed by histopathology (grade 1, 7; grade 2, 45; grade 3, 18; grade 4, 9) following surgery. 3.0 T MRI scanner. DR-CSI with a diffusion-weighted echo-planar imaging sequence and T2-mapping with a multi-echo spin echo sequence. DR-CSI results were analyzed for the solid tumor regions of interest using spectrum segmentation with five sub-region volume fraction metrics (VA , VB , VC , VD , and VE ). The regulations for spectrum segmentation were determined based on the D-T2 spectra of distinct macro-components. Tumor size, voxel-wise T2, and apparent diffusion coefficient (ADC) values were obtained. Histopathology assessed tumor grade (G1-G4) for each case. One-way ANOVA or Kruskal-Wallis test, Spearman's correlation (coefficient, rho), multivariable logistic regression analysis, receiver operating characteristic curve analysis, and DeLong's test. Significance criteria: P < 0.05. Significant differences were found in ADC, T2, DR-CSI VB , and VD among the ccRCC grades. Correlations were found for ccRCC grade to tumor size (rho = 0.419), age (rho = 0.253), VB (rho = 0.553) and VD (rho = -0.378). AUC of VB was slightly larger than ADC in distinguishing low-grade (G1-G2) from high-grade (G3-G4) ccRCC (0.801 vs. 0.762, P = 0.406) and G1 from G2 to G4 (0.796 vs. 0.647, P = 0.175), although not significant. Combining VB , VD , and VE had better diagnostic performance than combining ADC and T2 for differentiating G1 from G2-G4 (AUC: 0.814 vs 0.643). DR-CSI parameters are correlated with ccRCC grades, and may help to differentiate ccRCC grades. 2 TECHNICAL EFFICACY STAGE: 2.

Sarcopenia, More Than Just Muscle Atrophy: Imaging Methods for the Assessment of Muscle Quantity and Quality.

Sarcopenia, a progressive reduction of muscle mass and function, is associated with adverse outcomes in the elderly. Sarcopenia and muscle atrophy are not equal processes. Low muscle strength in association with muscle quantity/quality reduction is currently the optimal method for assessing sarcopenia. There is a practical need for indirect measurement of muscle strength using state-of-the-art imaging techniques. The following provides a narrative, broad review of all current imaging techniques for evaluating muscles and identifying sarcopenia, including DEXA, CT, MRI, and high-resolution ultrasound, their main strengths, weaknesses, and possible solutions to problems regarding each technique. Well-recognized imaging methods for the assessment of muscle mass are explained, including evaluation with DEXA, CT, and MRI muscle quantity assessment, ultrasound evaluation of muscle thickness and CSA, and their correlations with established muscle mass calculation methods. A special focus is on imaging methods for muscle quality evaluation. Several innovative and promising techniques that are still in the research phase but show potential in the assessment of different properties of muscle quality, including MRI DIXON sequences, MRI spectroscopy, Diffusion Tensor Imaging, ultrasound echo intensity, ultrasound elastography, and speed-of-sound ultrasound imaging are briefly mentioned. · Sarcopenia definition includes low muscle strength and low muscle quantity/quality.. · DEXA is a low-radiation method for whole-body composition measurement in a single image.. · CT has established cut-off values for muscle quality/quantity evaluation and sarcopenia diagnosis.. · MRI is the most sophisticated muscle quality assessment method capable of evaluating myosteatosis, myofibrosis, and microstructure.. · Ultrasound can evaluate muscle quality, including tissue architecture, and elasticity with excellent spatial resolution.. · Vasilevska Nikodinovska V, Ivanoski S, . Sarcopenia, More Than Just Muscle Atrophy: Imaging Methods for the Assessment of Muscle Quantity and Quality. Fortschr Röntgenstr 2023; 195: 777 - 789.

Taming Glioblastoma in "Real Time": Integrating Multimodal Advanced Neuroimaging/AI Tools Towards Creating a Robust and Therapy Agnostic Model for Response Assessment in Neuro-Oncology.

The highly aggressive nature of glioblastoma carries a dismal prognosis despite aggressive multimodal therapy. Alternative treatment regimens, such as immunotherapies, are known to intensify the inflammatory response in the treatment field. Follow-up imaging in these scenarios often mimics disease progression on conventional MRI, making accurate evaluation extremely challenging. To this end, revised criteria for assessment of treatment response in high-grade gliomas were successfully proposed by the RANO Working Group to distinguish pseudoprogression from true progression, with intrinsic constraints related to the postcontrast T1-weighted MRI sequence. To address these existing limitations, our group proposes a more objective and quantifiable "treatment agnostic" model, integrating into the RANO criteria advanced multimodal neuroimaging techniques, such as diffusion tensor imaging (DTI), dynamic susceptibility contrast-perfusion weighted imaging (DSC-PWI), dynamic contrast enhanced (DCE)-MRI, MR spectroscopy, and amino acid-based positron emission tomography (PET) imaging tracers, along with artificial intelligence (AI) tools (radiomics, radiogenomics, and radiopathomics) and molecular information to address this complex issue of treatment-related changes versus tumor progression in "real-time", particularly in the early posttreatment window. Our perspective delineates the potential of incorporating multimodal neuroimaging techniques to improve consistency and automation for the assessment of early treatment response in neuro-oncology.

Lateralizing Ability of Magnetic Resonance Spectroscopy and Diffusion Tensor imaging in Temporal Lobe Epilepsy

Background: Temporal lobe epilepsy (TLE) is the most common type of intractable and partial epilepsies. The only standard treatment for seizure-free status with manifest TLE is surgical which includes amygdala-hippocampectomy. The majority of temporal lobe seizures originate in the mesial temporal structures, primarily in the hippocampus, para hippocampal gyrus and amygdala.

Cerebral creatine deficiency syndrome with a novel missense variant in SLC6A8 gene

AbstractCerebral creatine deficiency syndromes (CCDS) are three metabolic diseases characterized by loss of function in three proteins (GATM, GAMT, and SLC6A8) that required in creatine (Cr) synthesis pathway and transport. In this study, we aimed to identify the causal variant in a male who was 12‐year‐old manifesting intellectual disability (ID), seizures, expressive dysphasia and autism‐like behavior. Urinary Cr metabolite measurements and MRI‐spectroscopy (MRS) findings were consistent with CCDS. Molecular analysis revealed de novo hemizygous SLC6A8 (NM_005629.4): c.1400 T &gt; G (p.Met467Arg) variant. The variant was not found in ClinVar, (the date of access: April 23th, 2023) and population databases (ExAC, gnomAD, 1000 Genomes, ESP 6500, Turkish Variome, GenomeAsia, Iranome, GME Variome, TOPMed Bravo and 4.7KJPN), it alters the physicochemical properties of the amino acid, the region is moderately conserved across species and in‐silico prediction tools (REVEL, CADD, SIFT, PolyPhen2, Mutation Taster, MetaLR, MCAP, MetaRNN and MutPred) unanimously emphasize pathogenicity. Based on this evidence, the variant was interpreted as “likely pathogenic” according to the ACMG criteria (PS2, PM2,PP3, and PP4‐S). This report may further elucidate the nature and phenotypic consequences of SLC6A8 variants.

Compartment-based reconstruction of 3D acquisition-weighted 31 P cardiac magnetic resonance spectroscopic imaging at 7 T: A reproducibility study.

Even at 7 T, cardiac 31 P magnetic resonance spectroscopic imaging (MRSI) is fundamentally limited by low signal-to-noise ratio (SNR), leading to long scan times and poor temporal and spatial resolutions. Compartment-based reconstruction algorithms such as magnetic resonance spectroscopy with linear algebraic modeling (SLAM) and spectral localization by imaging (SLIM) may improve SNR or reduce scan time without changes to acquisition. Here, we compare the repeatability and SNR performance of these compartment-based methods, applied to three different acquisition schemes at 7 T. Twelve healthy volunteers were scanned twice. Each scan session consisted of a 6.5-min 3D acquisition-weighted (AW) cardiac 31 P phase encode-based MRSI acquisition and two 6.5-min truncated k-space acquisitions with increased averaging (4 × 4 × 4 central k-space phase encodes and fractional SLAM [fSLAM] optimized k-space phase encodes). Spectra were reconstructed using (i) AW Fourier reconstruction; (ii) AW SLAM; (iii) AW SLIM; (iv) 4 × 4 × 4 SLAM; (v) 4 × 4 × 4 SLIM; and (vi) fSLAM acquisition-reconstruction combinations. The phosphocreatine-to-adenosine triphosphate (PCr/ATP) ratio, the PCr SNR, and spatial response functions were computed, in addition to coefficients of reproducibility and variability. Using the compartment-based reconstruction algorithms with the AW 31 P acquisition resulted in a significant increase in SNR compared with previously published Fourier-based MRSI reconstruction methods while maintaining the measured PCr/ATP ratio and improving interscan reproducibility. The alternative acquisition strategies with truncated k-space performed no better than the common AW approach. Compartment-based spectroscopy approaches provide an attractive reconstruction method for cardiac 31 P spectroscopy at 7 T, improving reproducibility and SNR without the need for a dedicated k-space sampling strategy.

Mutant Isocitrate Dehydrogenase 1 Expression Enhances Response of Gliomas to the Histone Deacetylase Inhibitor Belinostat.

Histone deacetylase inhibitors (HDACis) are drugs that target the epigenetic state of cells by modifying the compaction of chromatin through effects on histone acetylation. Gliomas often harbor a mutation of isocitrate dehydrogenase (IDH) 1 or 2 that leads to changes in their epigenetic state presenting a hypermethylator phenotype. We postulated that glioma cells with IDH mutation, due to the presence of epigenetic changes, will show increased sensitivity to HDACis. This hypothesis was tested by expressing mutant IDH1 with a point alteration-converting arginine 132 to histidine-within glioma cell lines that contain wild-type IDH1. Glioma cells engineered to express mutant IDH1 produced D-2-hydroxyglutarate as expected. When assessed for response to the pan-HDACi drug belinostat, mutant IDH1-expressing glioma cells were subjected to more potent inhibition of growth than the corresponding control cells. Increased sensitivity to belinostat correlated with the increased induction of apoptosis. Finally, a phase I trial assessing the addition of belinostat to standard-of-care therapy for newly diagnosed glioblastoma patients included one patient with a mutant IDH1 tumor. This mutant IDH1 tumor appeared to display greater sensitivity to the addition of belinostat than the other cases with wild-type IDH tumors based on both standard magnetic resonance imaging (MRI) and advanced spectroscopic MRI criteria. These data together suggest that IDH mutation status within gliomas may serve as a biomarker of response to HDACis.

PO-1448 Perfusion MRI and MRI spectroscopy: biomarkers of memory decline in pediatric brain tumor survivors

PO-1448 Perfusion MRI and MRI spectroscopy: biomarkers of memory decline in pediatric brain tumor survivors

Enhancing Cancer Diagnosis with Real-Time Feedback: Tumor Metabolism through Hyperpolarized 1-13C Pyruvate MRSI.

This review article discusses the potential of hyperpolarized (HP) 13C magnetic resonance spectroscopic imaging (MRSI) as a noninvasive technique for identifying altered metabolism in various cancer types. Hyperpolarization significantly improves the signal-to-noise ratio for the identification of 13C-labeled metabolites, enabling dynamic and real-time imaging of the conversion of [1-13C] pyruvate to [1-13C] lactate and/or [1-13C] alanine. The technique has shown promise in identifying upregulated glycolysis in most cancers, as compared to normal cells, and detecting successful treatment responses at an earlier stage than multiparametric MRI in breast and prostate cancer patients. The review provides a concise overview of the applications of HP [1-13C] pyruvate MRSI in various cancer systems, highlighting its potential for use in preclinical and clinical investigations, precision medicine, and long-term studies of therapeutic response. The article also discusses emerging frontiers in the field, such as combining multiple metabolic imaging techniques with HP MRSI for a more comprehensive view of cancer metabolism, and leveraging artificial intelligence to develop real-time, actionable biomarkers for early detection, assessing aggressiveness, and interrogating the early efficacy of therapies.