Clinical proton MR spectroscopy in central nervous system disorders

Challenges and Recent Advances in Diagnosing Wilson Disease

Wilson disease (WD) is a rare autosomal recessive disorder caused by ATP7B gene mutations, leading to pathological copper accumulation that primarily affects the liver, brain, and eyes. Diagnosing WD remains a significant challenge due to its highly variable clinical presentation, which ranges from asymptomatic biochemical abnormalities to acute liver failure and severe neuropsychiatric manifestations. Traditional diagnostic markers, such as serum ceruloplasmin, urinary copper excretion, and liver biopsy, lack sufficient specificity and sensitivity, often leading to delays in diagnosis and misclassification. Additionally, the absence of a single gold-standard test and the overlap with other hepatic and neurological disorders further complicate early detection. Recent advances in diagnostic techniques offer promising solutions to overcome these limitations. Novel biomarkers, including relative exchangeable copper (REC) and ATP7B protein quantification in dried blood spots have demonstrated improved accuracy in distinguishing WD from other conditions. Advanced imaging modalities, such as anterior segment optical coherence tomography (AS-OCT), quantitative susceptibility mapping (QSM), and copper-64 positron emission tomography imaging provide noninvasive tools for detecting early disease-related changes. Furthermore, next-generation sequencing (NGS) enhances genetic screening, facilitating earlier diagnosis, and family screening. A comprehensive approach integrating conventional and emerging diagnostic methodologies is essential for improving early detection and patient outcomes. Greater awareness of the limitations of traditional tests and the incorporation of novel biomarkers and imaging techniques into clinical practice can help refine diagnostic accuracy, reduce delays, and optimize treatment strategies for WD.

The Role of Proton Magnetic Resonance Spectroscopy in Neonatal and Fetal Brain Research

The biochemical composition and structure of the brain are in a rapid change during the exuberant stage of fetal and neonatal development. 1H-MRS is a noninvasive tool that can evaluate brain metabolites in healthy fetuses and infants as well as those with neurological diseases. This review aims to provide readers with an understanding of 1) the basic principles and technical considerations relevant to 1H-MRS in the fetal-neonatal brain and 2) the role of 1H-MRS in early fetal-neonatal development brain research. We performed a PubMed search to identify original studies using 1H-MRS in neonates and fetuses to establish the clinical applications of 1H-MRS. The eligible studies for this review included original research with 1H-MRS applications to the fetal-neonatal brain in healthy and high-risk conditions. We ran our search between 2000 and 2023, then added in several high-impact landmark publications from the 1990s. A total of 366 results appeared. After, we excluded original studies that did not include fetuses or neonates, non-proton MRS and non-neurological studies. Eventually, 110 studies were included in this literature review. Overall, the function of 1H-MRS in healthy fetal-neonatal brain studies focuses on measuring the change of metabolite concentrations during neurodevelopment and the physical properties of the metabolites such as T1/T2 relaxation times. For high-risk neonates, studies in very low birth weight preterm infants and full-term neonates with hypoxic-ischemic encephalopathy, along with examining the associations between brain biochemistry and cognitive neurodevelopment are most common. Additional high-risk conditions included infants with congenital heart disease or metabolic diseases, as well as fetuses of pregnant women with hypertensive disorders were of specific interest to researchers using 1H-MRS. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Reproducibility Made Easy: A Tool for Methodological Transparency and Efficient Standardized Reporting Based on the Proposed MRSinMRS Consensus

Recent expert consensus publications have highlighted the issue of poor reproducibility in magnetic resonance spectroscopy (MRS) studies, mainly due to the lack of standardized reporting criteria, which affects their clinical applicability. To combat this, guidelines for minimum reporting standards (MRSinMRS) were introduced to aid journal editors and reviewers in ensuring the comprehensive documentation of essential MRS study parameters. Despite these efforts, the implementation of MRSinMRS standards has been slow, attributed to the diverse nomenclature used by different vendors, the variety of raw MRS data formats, and the absence of appropriate software tools for identifying and reporting necessary parameters. To overcome this obstacle, we have developed the REproducibility Made Easy (REMY) standalone toolbox. REMY software supports a range of MRS data formats from major vendors like GE (.7), Siemens (.ima, .rda, .dcm), Philips (.spar/.sdat), and Bruker (.method), and MRS-NIfTI (.nii/nii.gz/.json) files facilitating easy data import and export through a user-friendly interface. REMY employs external libraries such as spec2nii and pymapVBVD to accurately read and process these diverse data formats, translating complex header information into a comprehensive structure that adheres to consensus reporting standards, thereby ensuring compatibility and ease of use for researchers in generating reproducible MRS research outputs. Users can select and import datasets, choose the appropriate vendor and data format, and then generate an MRSinMRS table, log file, and methodological documents in both Latex and PDF formats by just clicking one button. No coding knowledge is required, making the tool accessible to a wider range of users, including researchers and clinicians without programming expertise. This eliminates technical challenges related to data formatting and reporting. REMY effectively populated key sections of the MRSinMRS table with data from all supported file types. Accurate generation of hardware parameters including field strength, manufacturer, and scanner software version were demonstrated. However, it could not input data for RF coil and additional hardware information due to their absence in the files. For the acquisition section, REMY accurately read and populated fields for the pulse sequence name, nominal voxel size, repetition time (TR), echo time (TE), number of acquisitions/excitations/shots, spectral width (Hz), and number of spectral points, significantly contributing to the completion of the "Acquisition" fields of the table. Furthermore, REMY generates a boilerplate methods text section for manuscripts. The use of REMY will facilitate more widespread adoption of the MRSinMRS checklist within the MRS community, making it easier to write and report acquisition parameters effectively.

Coil Combination Using OpTIMUS Results in Improved Signal-to-Noise Ratios of In Vivo MR Spectra Acquired at 7 T

Magnetic resonance spectroscopy (MRS) enables noninvasive quantification of metabolites, but its utility in vivo can be limited by low signal-to-noise ratios (SNRs) and long acquisition times. The use of ultrahigh-field (UHF) strengths (> 3 T) combined with multichannel phased receive arrays can improve spectral SNR. A crucial step in the use of multichannel arrays is the combination of spectra acquired from individual coil channels. We previously developed a coil combination method at 3 T, optimized truncation to integrate multichannel MRS data using rank-R singular value decomposition (OpTIMUS), which uses noise-whitened windowed spectra and iterative rank-R singular value decomposition (SVD) to combine multichannel MRS data. Here, we evaluated OpTIMUS for combination of MR spectra acquired using a multichannel phased array at 7 T and compared spectral SNR and metabolite quantification with spectra combined using whitened SVD (WSVD), signal/noise squared (S/N2), and the Brown method. Data were acquired from 14 healthy volunteers, including five with data acquired at both 3 and 7 T, and from nine people living with HIV. Spectra combined using OpTIMUS resulted in a higher SNR compared to the three other methods, consistent with our prior results at 3 T. With half the number of averages (N = 32), spectra combined with OpTIMUS had higher SNR compared to spectra using the Brown method with 64 averages. Additionally, spectra combined using OpTIMUS at 7 T were compared to spectra acquired at 3 T with the same number of averages (N = 64) or matched acquisition times (N = 110 averages), and spectral fitting was consistently improved at 7 T even when comparable SNR was obtained at 3 T. The ability to increase SNR and maintain spectral quality by optimizing spectral coil combination has the potential to reduce scan time, a key challenge for routine clinical use of MRS.

Chemical shift and relaxation regularization improve the accuracy of 1H MR spectroscopy analysis

PURPOSE

Accurate analysis of metabolite levels from 1H MRS data is a significant challenge, typically requiring the estimation of approximately 100 parameters from a single spectrum. Signal overlap, spectral noise, and common artifacts further complicate the analysis, leading to instability and reports of poor agreement between different analysis approaches. One inconsistently used method to improve analysis stability is known as regularization, where poorly determined parameters are partially constrained to take a predefined value. In this study, we examine how regularization of frequency and linewidth parameters influences analysis accuracy.

METHODS

The accuracy of three MRS analysis methods was compared: (1) ABfit, (2) ABfit-reg, and (3) LCModel, where ABfit-reg is a modified version of ABfit incorporating regularization. Accuracy was assessed on synthetic MRS data generated with random variability in the frequency shift and linewidth parameters applied to each basis signal. Spectra (N = 1000 $$ N=1000 $$ ) were generated across a range of SNR values (10, 30, 60, 100) to evaluate the impact of variable data quality.

RESULTS

Comparison between ABfit and ABfit-reg demonstrates a statistically significant (p <  0.0005) improvement in accuracy associated with regularization for each SNR regime. An approximately 10% reduction in the mean squared metabolite errors was found for ABfit-reg compared to LCModel for SNR >10 (p <  0.0005). Furthermore, Bland-Altman analysis shows that incorporating regularization into ABfit enhances its agreement with LCModel.

CONCLUSION

Regularization is beneficial for MRS fitting and accurate characterization of the frequency and linewidth variability in vivo may yield further improvements.

Association between cerebral lesions and the severity of diabetic cardiovascular disease, retinopathy, and nephropathy-new lessons to learn from neuroimaging

Diabetes is associated with cerebrovascular lesions detectable through neuroimaging. Neuroimaging is traditionally valued for its insights into the structure of the central nervous system. However, the brain is connected with other organs. The vascular system, hormones, and peripheral nerve system connect the brain to other sections of the body bidirectionaly. This interaction between the brain and other parts encourages us to look at the total body, not just its different parts separately. Growing evidence has shown the link between brain injuries and cardiac, retinal, and kidney disorders, suggesting that neuroimaging has the potential to provide valuable information about peripheral organs This is particularly crucial for a systemic disease like diabetes, which affects the entire body. In this review, we aim to first discuss the data that neuroimaging can reveal about the severity of diabetic retinopathy, nephropathy, and cardiovascular disease in diabetic patients. This interdisciplinary approach could guide the design of new randomized controlled trials, screening programs, and an integrated clinical practice. This study explores the mechanisms underlying the association between the brain and other organs in the context of diabetes. Then we will consider their implications for future research and clinical practice.

Deciphering the Role of Glial Cell-Specific Metabolites as Biomarkers in Early Cervical Myelopathy- Insights from in vivo MRS study

BACKGROUND

Early degenerative cervical myelopathy (DCM) presents a diagnostic dilemma due to its variability in presentation, overlap with other clinical conditions, and lack of specific clinical tests. Although magnetic resonance imaging (MRI) is the preferred imaging modality, its ability to detect early cervical myelopathy remains uncertain due to its inability to detect microstructural changes at an early spondylotic stage. Magnetic Resonance Spectroscopy (MRS) is a novel, non-invasive spinal imaging technique that provides metabolic and biochemical information regarding spinal cord function.

PURPOSE

This study aims to determine the diagnostic role of MRS and Diffusion Tensor Imaging (DTI) in patients with DCM. Additionally, we intend to explore the role of MRS metabolites/ratio as molecular biomarkers for the early detection of DCM.

STUDY DESIGN

Prospective observational study PATIENT SAMPLE: The study includes a sample size of 89 subjects (20 asymptomatic volunteers and 69 patients with different grades of DCM.

OUTCOME MEASURES

Predictability of MRS and DTI in identifying early DCM. The severity of myelopathy was assessed using the modified Japanese Orthopaedic Association (mJOA) score.

METHODS

The study populations were classified according to their mJOA scores: Group 1 included asymptomatic volunteers with no clinical features of cervical myelopathy. Group 2 included patients with a score of 15 to 17 (mJOA "mild") presenting with early symptoms of myelopathy, like arm pain, hand numbness and clumsiness with/ without the symptoms of radiculopathy. Group 3 included patients with mJOA "moderate" myelopathy score of 12 to 14, presenting with symptoms like gait instability and a decrease in hand dexterity. Group 4 included patients with mJOA "severe" score of less than or equal to 11, presenting with advanced symptoms like walker/wheelchair-dependence, loss of hand dexterity, and bladder disturbances. We then looked at MR Imaging in these symptomatic patients to evaluate stenosis. Single voxel MRS was placed at the C2 level, and DTI parameters were measured at the site of maximum compression. MRI parameters like the compression level, presence of signal hyperintensity, grading of stenosis, and compression ratio were also analysed in T2W MRI images.

RESULTS

Among the 89-study population, 20 asymptomatic volunteers in group 1 and 23 patients each in groups 2, 3 and 4 were included. Among the various parameters, there was a statistically significant difference between the groups for various MRS metabolite ratios, namely NAA/Cr (P= 0.008), Cho/Cr (P=0.025), Cho/NAA (P<0.001), Cr/NAA (P <0.001) and MIn/NAA (P=0.003) as well as DTI parameters namely FA (P= 0.010) and ADC (P=0.011). A significant linear correlation was observed between the severity of myelopathy (mJOA score) and the following parameters: Cho/NAA (R2=0.510, P=0.000), MIn/NAA (R2=0.393, P=0.002), Cr/NAA (R2=0.354, P=0.007), FA (R2= -0.331, P=0.012), ADC (R2=0.321, P=0.015), Cho/Cr (R2=0.289, P=0.029) and NAA (R2= -0.288, P=0.030). Among the various metabolites, we observed that the glial cell-specific metabolites (Cho, Cr and MIn) with respect to the neuron-specific metabolite, NAA, had good correlation in early identification of DCM patients presenting with mild to moderate disease severity. ROC analysis showed that glial cell-specific metabolites ratio(Cho/NAA, Cr/NAA, MIn/NAA) had good AUC for identifying both mild (0.725, 0.770, 0.765) and moderate (0.825, 0.736, 0.760) myelopathy.

CONCLUSION

Our study highlights that MRS-based Glial cell-specific metabolites ratio (Cho/NAA, Cr/NAA, and MIn/NAA) can be reliable molecular biomarkers for identifying early degenerative cervical myelopathy.

Smaller cingulate grey matter mediates the association between dual-task gait and incident dementia

Individuals with mild cognitive impairment who have high dual-task gait cost (≥20% slowing in gait speed while performing a cognitive brain-demanding task) are 3-fold more likely to progress to dementia. However, the cortical regions that might explain this association are unknown, which might identify potentially treatable areas. The aim of the present study was to investigate whether brain grey matter volume loss and motor cortex metabolite levels explain the association between dual-task cost and incident dementia in individuals with mild cognitive impairment. We included participants with mild cognitive impairment from the Gait and Brain Study Cohort, who had a baseline MRI and were followed up for 9 years with cognitive and gait assessments every 6 months. Gait performance was investigated in four conditions: usual gait, counting backwards by ones, naming animals and subtracting serial sevens. Dual-task cost was calculated as the percentage change in gait speed in dual-task conditions relative to usual gait speed. Data were collected from July 2007 to June 2023. From the 139 individuals with mild cognitive impairment included at baseline [mean (standard deviation) age, 73 (6) years; 62 (44%) female], 33 (24%) progressed to dementia. Baseline high dual-task cost (≥20%) during counting backwards by ones and naming animals conditions were associated with smaller grey matter volume in several brain structures. A higher ratio of choline to creatine in the primary motor cortex was associated with higher serial sevens dual-task cost. High dual-task cost while counting backwards by ones and naming animals was associated with a 3-fold risk of incident dementia (P = 0.02). Mediation analyses revealed that grey matter volume clusters localized in the right anterior and middle cingulate cortices mediated the association between counting backwards by ones dual-task cost and incident dementia (effect: 48%; P = 0.045) with no mediation observed in grey matter loss in other brain regions or through motor cortex metabolite levels. Smaller grey matter volume of the right anterior and middle cingulate cortices explained the association between high dual-task cost and incident dementia in mild cognitive impairment. This result sheds light on the neural mechanisms of cognitive-motor interaction linked with cognitive decline and dementia in mild cognitive impairment and supports the use of gait under dual-tasking as a motor biomarker of dementia.

Neurochemical Alterations Linked to Persistent COVID-19-Induced Anosmia: Probing Into Orbitofrontal Cortex by Magnetic Resonance Spectroscopy

BACKGROUND

While many COVID-19-induced anosmia patients recover their sense of smell within a few months, a substantial number of them continue to experience olfactory impairment. In our primary study, the metabolic patterns in orbitofrontal cortex (OFC) were observed to exhibit more alterations than other regions. Hence, this study specifically probes into alterations within OFC region in subjects with persistent COVID-19-induced anosmia.

METHODS

In a new categorization, 54 subjects were studied as two major groups of COVID-19-related anosmia and normal each of which includes 27 subjects. Iran Recognition-Smell Identification Test (IR-SIT) over a three-month follow-up period was utilized for olfactory function assessment and anosmia diagnosis. Proton Magnetic Resonance Spectroscopy (1H-MRS) was employed to examine changes of metabolites in OFC, including N-acetyl aspartate (NAA), choline (Cho), and creatine (Cr), as well as their ratios. Additionally, a linear regression was applied to investigate the potential correlation between MRS data and IR-SIT scores.

RESULTS

Patients with COVID-19-induced anosmia exhibited a significant reduction in NAA, Cho, and Cr levels in the OFC region compared to the control group. Moreover, NAA/Cho and NAA/Cr ratios were lower in the anosmia patients, whereas the Cho/Cr ratio elevated. The NAA/Cho ratio had the highest linear correlation with IR-SIT scores in anosmia.

CONCLUSION

This study highlights remarkable neurochemical patterns associated with COVID-19-induced anosmia in brain orbitofrontal cortex detectable by proton MRS, shedding light on the link between OFC function impairment and anosmia. The NAA/Cho ratio derived from MRS data emerged as a potential biomarker that correlates with anosmia severity and recovery examination.

Exploring the diagnostic potential: magnetic particle imaging for brain diseases

Brain diseases are characterized by high incidence, disability, and mortality rates. Their elusive nature poses a significant challenge for early diagnosis. Magnetic particle imaging (MPI) is a novel imaging technique with high sensitivity, high temporal resolution, and no ionizing radiation. It relies on the nonlinear magnetization response of superparamagnetic iron oxide nanoparticles (SPIONs), allowing visualization of the spatial concentration distribution of SPIONs in biological tissues. MPI is expected to become a mainstream technology for the early diagnosis of brain diseases, such as cancerous, cerebrovascular, neurodegenerative, and inflammatory diseases. This review provides an overview of the principles of MPI, explores its potential applications in brain diseases, and discusses the prospects for the diagnosis and management of these diseases.

Magnetic resonance spectroscopy for discriminating primary angiitis of the central nervous system from gliomas and lymphomas

OBJECTIVE

Conventional imaging often struggles to differentiate between primary angiitis of the central nervous system (PACNS) and intracranial tumors. This study aims to evaluate the application value of proton magnetic resonance spectroscopy (1H-MRS) in distinguishing PACNS from intracranial tumors.

METHODS

This study collected data from 10 patients with PACNS and 15 patients with intracranial tumors (10 gliomas and 5 lymphomas) confirmed by pathological biopsy. The levels of choline (Cho), creatine (Cr), and N-acetylaspartate (NAA) in the lesion areas and contralateral normal brain tissue were measured and analyzed using 1H-MRS, and the ratios of Cho/Cr, NAA/Cr, and Cho/NAA were calculated. The diagnostic efficacy of 1H-MRS in distinguishing PACNS from intracranial tumors was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC).

RESULTS

Compared to contralateral normal brain tissue, significant differences were observed in the ratios of Cho/Cr, NAA/Cr, and Cho/NAA between the lesion areas of PACNS and intracranial tumors (P < 0.001). After correcting for the corresponding normal brain tissue spectra, the NAA/Cr ratio in PACNS lesion areas was significantly higher than in intracranial tumor lesions (0.90 vs. 0.25, P < 0.001), and the Cho/NAA ratio was significantly lower in PACNS lesions (1.98 vs. 9.00, P < 0.001), while the difference in Cho/Cr was not significant (1.59 vs. 1.94, P = 0.405). When the corrected NAA/Cr ratio was ≥ 0.71, the ROC-AUC for diagnosing PACNS was 1.00, with both specificity and sensitivity at 100%. The subgroup analysis of glioma/lymphoma yielded similar results.

CONCLUSION

The ratios of NAA/Cr and NAA/Cho are significantly meaningful in differentiating PACNS from intracranial tumors, and 1H-MRS can be an effective tool for distinguishing these two conditions.

WALINET: A water and lipid identification convolutional neural network for nuisance signal removal in 1 H $$ {}^1\mathrm{H} $$ MR spectroscopic imaging

PURPOSE

Proton magnetic resonance spectroscopic imaging (  1 H $$ {}^1\mathrm{H} $$ -MRSI) provides noninvasive spectral-spatial mapping of metabolism. However, long-standing problems in whole-brain  1 H $$ {}^1\mathrm{H} $$ -MRSI are spectral overlap of metabolite peaks with large lipid signal from scalp, and overwhelming water signal that distorts spectra. Fast and effective methods are needed for high-resolution  1 H $$ {}^1\mathrm{H} $$ -MRSI to accurately remove lipid and water signals while preserving the metabolite signal. The potential of supervised neural networks for this task remains unexplored, despite their success for other MRSI processing.

METHODS

We introduce a deep learning method based on a modified Y-NET network for water and lipid removal in whole-brain  1 H $$ {}^1\mathrm{H} $$ -MRSI. The WALINET (WAter and LIpid neural NETwork) was compared with conventional methods such as the state-of-the-art lipid L2 regularization and Hankel-Lanczos singular value decomposition (HLSVD) water suppression. Methods were evaluated on simulated models and in vivo whole-brain MRSI using NMRSE, SNR, CRLB, and FWHM metrics.

RESULTS

WALINET is significantly faster and needs 8s for high-resolution whole-brain MRSI, compared with 42min for conventional HLSVD+L2. WALINET suppresses lipid and water in the brain by 25-45 and 34-53-fold, respectively. WALINET has better performance than HLSVD+L2, providing: (1) more lipid removal with 41% lower NRMSE; (2) better metabolite signal preservation with 71% lower NRMSE in simulated data; 155% higher SNR and 50% lower CRLB in in vivo data. Metabolic maps obtained by WALINET in healthy subjects and patients show better gray-/white-matter contrast with more visible structural details.

CONCLUSIONS

WALINET has superior performance for nuisance signal removal and metabolite quantification on whole-brain  1 H $$ {}^1\mathrm{H} $$ -MRSI compared with conventional state-of-the-art techniques. This represents a new application of deep learning for MRSI processing, with potential for automated high-throughput workflow.

Ex vivo ultra-high field magnetic resonance imaging of human epileptogenic specimens from primarily the temporal lobe: A systematic review

PURPOSE

Magnetic resonance imaging (MRI) is the preferred diagnostic tool for the detection of structural cerebral lesions in patients with epilepsy. Ultra-high field (UHF) MRI with field strengths ≥7 Tesla has been reported to improve the visualization and delineation of epileptogenic lesions. The use of ex vivo UHF MRI may expand our knowledge on the detection and detailed micromorphology of subtle epileptogenic lesions by bridging the gap between in vivo MRI and histopathology.

METHODS

A systematic review of available literature was conducted following PRISMA guidelines. A descriptive analysis of included articles was performed, focusing on (I) the ability of ex vivo UHF MRI to detect subtle abnormalities related to epilepsy, (II) different post-processing methods, and (III) concordance between UHF MRI and histopathology.

RESULTS

Eleven studies with focus on the depiction of focal cortical dysplasia (n = 4) or hippocampal sclerosis (n = 7) as causative lesion of drug-resistant epilepsy were included. Ex vivo UHF MRI proved its ability to visualize the anatomy of cortical and hippocampal structures in greater detail when compared to ex vivo conventional field strengths. Different MRI post-processing methods enabled differentiation between lesional subtypes and provided novel insights into (peri)lesional characteristics. Concordance between ex vivo UHF MRI findings and histopathology was high.

CONCLUSION

Acquisition of ex vivo UHF MRI and its image processing has the potential to depict epileptogenic abnormalities in greater detail with a spatial resolution approximating histological images. The translation of ex vivo UHF MRI features to in vivo clinical settings remains challenging and urges further exploration.

Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging

INTRODUCTION

Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian compressed-sense acquisition enables fast high-resolution metabolic imaging but has lengthy reconstruction times that limits throughput and needs expert user interaction. Here, we present a robust and efficient Deep Learning reconstruction embedded in a physical model within an end-to-end automated processing pipeline to obtain high-quality metabolic maps.

METHODS

Fast high-resolution whole-brain metabolic imaging was performed at 3.4 mm3 isotropic resolution with acquisition times between 4:11-9:21 min:s using ECCENTRIC pulse sequence on a 7T MRI scanner. Data were acquired in a high-resolution phantom and 27 human participants, including 22 healthy volunteers and 5 glioma patients. A deep neural network using recurring interlaced convolutional layers with joint dual-space feature representation was developed for deep learning ECCENTRIC reconstruction (Deep-ER). 21 subjects were used for training and 6 subjects for testing. Deep-ER performance was compared to iterative compressed sensing Total Generalized Variation reconstruction using image and spectral quality metrics.

RESULTS

Deep-ER demonstrated 600-fold faster reconstruction than conventional methods, providing improved spatial-spectral quality and metabolite quantification with 12%-45% (P<0.05) higher signal-to-noise and 8%-50% (P<0.05) smaller Cramer-Rao lower bounds. Metabolic images clearly visualize glioma tumor heterogeneity and boundary. Deep-ER generalizes reliably to unseen data.

CONCLUSION

Deep-ER provides efficient and robust reconstruction for sparse-sampled MRSI. The accelerated acquisition-reconstruction MRSI is compatible with high-throughput imaging workflow. It is expected that such improved performance will facilitate basic and clinical MRSI applications for neuroscience and precision medicine.

Pons metabolite alterations in narcolepsy type 1

INTRODUCTION

Narcolepsy type 1 (NT1) is a rare central sleep disorder characterized by a selective loss of hypocretin/orexin (hcrt)-producing neurons in the postero-lateral hypothalamus that project to widespread areas of the brain and brainstem. The aim of this study was to explore in a group of NT1 patients the metabolic alterations in the pons and their associations with disease features.

METHODS

Twenty-one NT1 patients (16 M) and twenty age-matched healthy controls (10 M) underwent a brain 1H MRS on a 1.5 T GE Medical Systems scanner. Metabolite content of N-acetyl-aspartate (NAA), choline (Cho), and myo-inositol (mI) were estimated relative to creatine (Cr), using LCModel 6.3. Clinical data were also collected with validated questionnaires, polysomnography, the Multiple Sleep Latency Test (MSLT), Cerebrospinal fluid hypocretin-1 (CSF hcrt-1) concentration and genetic markers.

RESULTS

NT1 patients compared with healthy controls showed lower NAA/Cr ratio (p = 0.007) and NAA/mI ratio (p = 0.011) in the pons. The Epworth Sleepiness Scale score showed a significant negative correlation with NAA/Cr content (p = 0.023), MSLT sleep latency a negative correlation with the mI/Cr ratio (p = 0.008), and sleep onset REM periods a positive correlation with the mI/Cr ratio (p = 0.027). CSF hcrt-1 levels were positively correlated with the NAA/Cr ratio (p = 0.039) and negatively with the mI/Cr ratio (p = 0.045) and the Cho/Cr ratio (p = 0.026).

CONCLUSION

The metabolic alterations found in the pons of NT1 patients using the MR Spectroscopy technique were associated with subjective and objective disease severity measures, highlighting the crucial role of this biomarker in the pathophysiology of the disease.

Deep learning and its applications in nuclear magnetic resonance spectroscopy

Nuclear Magnetic Resonance (NMR), as an advanced technology, has widespread applications in various fields like chemistry, biology, and medicine. However, issues such as long acquisition times for multidimensional spectra and low sensitivity limit the broader application of NMR. Traditional algorithms aim to address these issues but have limitations in speed and accuracy. Deep Learning (DL), a branch of Artificial Intelligence (AI) technology, has shown remarkable success in many fields including NMR. This paper presents an overview of the basics of DL and current applications of DL in NMR, highlights existing challenges, and suggests potential directions for improvement.

Test-retest precision of brain metabolites in healthy participants using 31P-MRS and 1H MEGA-PRESS on a 3T multi-nuclear MRI system

Background

Magnetic resonance spectroscopy (MRS) enables the non-invasive quantification of brain metabolites, and its reliability is crucial for accurate interpretation of disease state. This study assessed the test-retest precision of phosphorus-31 (31P)-MRS and hydrogen (1H)-MEscher-GArwood Point RESolved Spectroscopy (MEGA-PRESS) in measuring 31P metabolites, γ-aminobutyric acid (GABA), and glutathione (GSH) using a 3T multi-nucleus magnetic resonance imaging (MRI) system.

Methods

In total, 32 participants, who underwent two scanning sessions within three days, using two dimensional (2D)-chemical shift imaging (CSI)-31P-MRS and 1H-MEGA-PRESS sequences, were enrolled in the study. γ-aminobutyric acid and macromolecules (GABA+), glutamate and glutamine (Glx), GSH, and 12 31P metabolites were analyzed using the MATLAB-based tool Gannet and jMRUI software. Precision was assessed based on the coefficients of variation (CVs) and Bland-Altman plots.

Results

The results revealed that potential of hydrogen (pH) and phosphocreatine (PCr) showed the greatest stability as evidenced by low CVs, suggesting reliable measurements across sessions. The adenosine triphosphates (ATPs) showed considerable stability. Conversely, metabolites, such as phosphomonoesters (PMEs) and phosphodiesters (PDEs), located to the left of PCr, showed reduced stability, while glycerophosphatidylcholine (GPTC) had the highest CV, indicating significant variability in clinical practice. Among the various brain regions, intermediate areas such as the temporal lobe and thalamus exhibited greater stability than peripheral regions such as the frontal and occipital lobes. Single-voxel MEGA-PRESS measurements showed that Glx and GABA+ had higher precision than GSH.

Conclusions

Both the 31P-MRS and 1H-MEGA-PRESS sequences showed high precision in measuring brain metabolites, but some metabolites showed higher stability than others. These results are crucial for exploring the clinical and research applications of these methods, and provide a solid foundation for subsequent investigations.

Proton-free induction decay MRSI at 7 T in the human brain using an egg-shaped modified rosette K-space trajectory

PURPOSE

Proton (1H)-MRSI via spatial-spectral encoding poses high demands on gradient hardware at ultra-high fields and high-resolutions. Rosette trajectories help alleviate these problems, but at reduced SNR-efficiency because of their k-space densities not matching any desired k-space filter. We propose modified rosette trajectories, which more closely match a Hamming filter, and thereby improve SNR performance while still staying within gradient hardware limitations and without prolonging scan time.

METHODS

Analytical and synthetic simulations were validated with phantom and in vivo measurements at 7 T. The rosette and modified rosette trajectories were measured in five healthy volunteers in 6 min in a 2D slice in the brain. An elliptical phase-encoding sequence was measured in one volunteer in 22 min, and a 3D sequence was measured in one volunteer within 19 min. The SNR per-unit-time, linewidth, Cramer-Rao lower bounds (CRLBs), lipid contamination, and data quality of the proposed modified rosette trajectory were compared to the rosette trajectory.

RESULTS

Using the modified rosette trajectories, an improved k-space weighting function was achieved resulting in an SNR per-unit-time increase of up to 12% compared to rosette's and 23% compared to elliptical phase-encoding, dependent on the two additional trajectory parameters. Similar results were achieved for the theoretical SNR calculation based on k-space densities, as well as when using the pseudo-replica method for simulated, in vivo, and phantom data. The CRLBs of γ-aminobutyric acid and N-acetylaspartylglutamate improved non-significantly for the modified rosette trajectory, whereas the linewidths and lipid contamination remained similar.

CONCLUSION

By optimizing the shape of the rosette trajectory, the modified rosette trajectories achieved higher SNR per-unit-time and enhanced data quality at the same scan time.

Parallel detection of MRI and 1H MRSI for multi-contrast anatomical and metabolic imaging

PURPOSE

MRI and MRSI provide unique and complementary information on anatomy, structure, function, and metabolism. The default strategy for a combined MRI and MRSI study is a sequential acquisition of both modalities, leading to long scan times. As MRI and MRSI primarily detect water and metabolites, respectively, the small frequency difference between resonances can be exploited with frequency-selective RF pulses to achieve interleaved or parallel detection of MRI and MRSI, without an increase in total scan time.

METHODS

Here, we describe the pulse sequence modifications necessary to allow acquisition of T1 and T2-weighted MRI and B0/B1 mapping in parallel with MRSI. In general, the MRSI module, including water suppression, can be used unmodified. MRI methods are executed in 3D using 3- to 4-ms frequency-selective Gaussian RF pulses with acceleration along the third dimension through repetitive small-angle nutation or multi-spin-echo acquisitions.

RESULTS

Phantom experiments demonstrated artifact-free 3D MRIs. MRSIs in the absence or presence of MRI elements were identical in sensitivity and spectral resolution (line width) and showed consistent water suppression. Parallel MRI-MRSI was applied to the brains of tumor-bearing rats in vivo. High-contrast, high-sensitivity metabolic MRSI data at 8 μL nominal resolution was acquired in parallel with 3D T1-weighted, T2-weighted, and B0/B1-weighted MRIs for an overall scan duration of 30 min.

CONCLUSION

Multi-contrast MRIs and MRSI can be acquired in parallel by utilizing the small frequency difference between water and metabolites. This opens the possibility for shorter overall scans times, or the acquisition of higher-resolution or additional contrast MRIs.

NNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High-Resolution Short-Echo-Time MR Spectroscopy Datasets

Purpose To develop and evaluate the performance of NNFit, a self-supervised deep learning method for quantification of high-resolution short-echo-time (TE) echo-planar spectroscopic imaging (EPSI) datasets, with the goal of addressing the computational bottleneck of conventional spectral quantification methods in the clinical workflow. Materials and Methods This retrospective study included 89 short-TE whole-brain EPSI/generalized autocalibrating partial parallel acquisition scans from clinical trials for glioblastoma (trial 1, May 2014-October 2018) and major depressive disorder (trial 2, 2022-2023). The training dataset included 685 000 spectra from 20 participants (60 scans) in trial 1. The testing dataset included 115 000 spectra from five participants (13 scans) in trial 1 and 145 000 spectra from seven participants (16 scans) in trial 2. A comparative analysis was performed between NNFit and a widely used parametric-modeling spectral quantitation method (FITT). Metabolite maps generated by each method were compared using the structural similarity index measure (SSIM) and linear correlation coefficient (R2). Radiation treatment volumes for glioblastoma based on metabolite maps were compared using the Dice coefficient and a two-tailed t test. Results Mean SSIMs and R2 values for trial 1 test set data were 0.91 and 0.90 for choline, 0.93 and 0.93 for creatine, 0.93 and 0.93 for N-acetylaspartate, 0.80 and 0.72 for myo-inositol, and 0.59 and 0.47 for glutamate plus glutamine. Mean values for trial 2 test set data were 0.95 and 0.95, 0.98 and 0.97, 0.98 and 0.98, 0.92 and 0.92, and 0.79 and 0.81, respectively. The treatment volumes had a mean Dice coefficient of 0.92. The mean processing times were 90.1 seconds for NNFit and 52.9 minutes for FITT. Conclusion A deep learning approach to spectral quantitation offers performance similar to that of conventional quantification methods for EPSI data, but with faster processing at short TE. Keywords: MR Spectroscopy, Neural Networks, Brain/Brain Stem Supplemental material is available for this article. © RSNA, 2025.

Radiomics in glioma: emerging trends and challenges

Radiomics is a promising neuroimaging technique for extracting and analyzing quantitative glioma features. This review discusses the application, emerging trends, and challenges associated with using radiomics in glioma. Integrating deep learning algorithms enhances various radiomics components, including image normalization, region of interest segmentation, feature extraction, feature selection, and model construction and can potentially improve model accuracy and performance. Moreover, investigating specific tumor habitats of glioblastomas aids in a better understanding of glioblastoma aggressiveness and the development of effective treatment strategies. Additionally, advanced imaging techniques, such as diffusion-weighted imaging, perfusion-weighted imaging, magnetic resonance spectroscopy, magnetic resonance fingerprinting, functional MRI, and positron emission tomography, can provide supplementary information for tumor characterization and classification. Furthermore, radiomics analysis helps understand the glioma immune microenvironment by predicting immune-related biomarkers and characterizing immune responses within tumors. Integrating multi-omics data, such as genomics, transcriptomics, proteomics, and pathomics, with radiomics, aids the understanding of the biological significance of the underlying radiomics features and improves the prediction of genetic mutations, prognosis, and treatment response in patients with glioma. Addressing challenges, such as model reproducibility, model generalizability, model interpretability, and multi-omics data integration, is crucial for the clinical translation of radiomics in glioma.

Fast High-Resolution Metabolite Mapping in the rat Brain Using 1H-FID-MRSI at 14.1 T

Magnetic resonance spectroscopic imaging (MRSI) enables the simultaneous noninvasive acquisition of MR spectra from multiple spatial locations inside the brain. Although 1H-MRSI is increasingly used in the human brain, it is not yet widely applied in the preclinical setting, mostly because of difficulties specifically related to very small nominal voxel size in the rat brain and low concentration of brain metabolites, resulting in low signal-to-noise ratio (SNR). In this context, we implemented a free induction decay 1H-MRSI sequence (1H-FID-MRSI) in the rat brain at 14.1 T. We combined the advantages of 1H-FID-MRSI with the ultra-high magnetic field to achieve higher SNR, coverage, and spatial resolution in the rat brain and developed a custom dedicated processing pipeline with a graphical user interface for Bruker 1H-FID-MRSI: MRS4Brain toolbox. LCModel fit, using the simulated metabolite basis set and in vivo measured MM, provided reliable fits for the data at acquisition delays of 1.30 ms. The resulting Cramér-Rao lower bounds were sufficiently low (< 30%) for eight metabolites of interest (total creatine, N-acetylaspartate, N-acetylaspartate + N-acetylaspartylglutamate, total choline, glutamine, glutamate, myo-inositol, and taurine), leading to highly reproducible metabolic maps. Similar spectral quality and metabolic maps were obtained with one and two averages, with slightly better contrast and brain coverage due to increased SNR in the latter case. Furthermore, the obtained metabolic maps were accurate enough to confirm the previously known brain regional distribution of some metabolites. The acquisitions proved high reproducibility over time. We demonstrated that the increased SNR and spectral resolution at 14.1 T can be translated into high spatial resolution in 1H-FID-MRSI of the rat brain in 13 min using the sequence and processing pipeline described herein. High-resolution 1H-FID-MRSI at 14.1 T provided robust, reproducible, and high-quality metabolic mapping of brain metabolites with minimal technical limitations.

MRI detection of free-contrast agent nanoparticles

PURPOSE

The integration of nanotechnology into biomedical imaging has significantly advanced diagnostic and theranostic capabilities. However, nanoparticle detection in imaging relies on functionalization with appropriate probes. In this work, a new approach to visualize free-label nanoparticles using MRI and MRS techniques is described, consisting of detecting by 1H CSI specific proton signals belonging to the components naturally present in most of the nanosystems used in preclinical and clinical research.

METHODS

Three different nanosystems, namely lipid-based micelles, liposomes, and perfluorocarbon-based nanoemulsions, were synthesized, characterized by high resolution NMR and then visualized by 1H CSI at 300 MHz. Subsequently the best 1H CSI performing system was administered to murine models of cancer to evaluate the possibility of tracking the nanosystem by looking at its proton associated signal. Furthermore, an in vitro comparison between 1H CSI and 19F MRI was carried out.

RESULTS

The study successfully demonstrates the feasibility of detecting nanoparticles using MRI/MRS without probe functionalization, employing 1H CSI. Among the nanosystems tested, the perfluorocarbon-based nanoemulsion exhibited the highest SNR. Consequently, it was evaluated in vivo, where its detection was achievable within tumors and inflamed regions via 1H CSI, and in lymph nodes via PRESS.

CONCLUSIONS

These findings present a promising avenue for nanoparticle imaging in biomedical applications, offering potential enhancements to diagnostic and theranostic procedures. This non-invasive approach has the capacity to advance imaging techniques and expand the scope of nanoparticle-based biomedical research. Further exploration is necessary to fully explore the implications and applications of this method.

Functional connectivity and metabolic brain alterations in sleepwalkers

OBJECTIVE

Disorders of arousal (DoA) are characterized by an intermediate state between wakefulness and deep sleep, leading to incomplete awakenings from NREM sleep. Multimodal studies have shown subtle neurophysiologic alterations even during wakefulness in DoA. The aim of this study was to explore the brain functional connectivity in DoA and the metabolic profile of the anterior and posterior cingulate cortex, given its pivotal role in cognitive and emotional processing.

METHODS

Fifteen consecutive patients with DoA (9 males, mean age 26.3 ± 7.7) and 15 age- and sex-matched healthy controls (8 males, mean age 25.8 ± 3.6) were enrolled. All participants underwent a protocol including sleep and psychological evaluation scales and multimodal brain MRI with resting-state functional MRI and 1H-MR spectroscopy.

RESULTS

The independent component analysis disclosed an altered resting-state functional connectivity (FC) in the patients' sensory motor network, with a higher connectivity strength in opercular cortex, precuneus, occipital pole, and lingual gyrus. The seed-based analysis revealed a decreased FC between posterior cingulate cortex (PCC) and several cerebral areas. Finally, spectroscopic imaging revealed a reduced content of glutamine in the PCC (p < 0.001).

INTERPRETATION

The increased connectivity in the sensory-motor network of DoA patients could constitute a "facilitatory medium" enhancing motor circuit activation, while the connectivity and metabolic alterations of PCC might represent a trait functional feature, contributing to a dysfunctional arousal process and the difficulty to reach a complete awareness during DoA episodes. In addition, these alterations at rest might be related to daytime impairment reported by patients, requiring new strategies for DoA management.

The circulatory arrest recovery ammonia problem (CARAP) hypothesis: A proton magnetic resonance spectroscopy (1H-MRS) study of brain metabolism during neonatal cardiopulmonary bypass surgery

OBJECTIVE

Congenital heart disease affects 1% of US births, with many babies requiring major cardiothoracic surgery under cardiopulmonary bypass (CPB), exposing the more critical patients to neurodevelopmental impairment. Optimal surgical parameters to minimize neuronal injury are unknown. We used proton magnetic resonance spectroscopy (1H MRS) and blood ammonia assays in a neonatal pig model of CPB to compare 2 approaches, complete circulatory arrest (CA) versus antegrade cerebral perfusion.

METHODS

Two-week old piglets (N = 17) were put on a CPB pump and placed in a 3-T magnetic resonance imaging to study brain metabolism during CPB. Dynamic single-voxel 1H MRS brain data were acquired while animals underwent 1 of 4 CPB protocols: ∼50 minutes CA at 18 °C and 28 °C or antegrade cerebral perfusion at 18°C and 28 °C, followed by a ∼1-hour recovery period. On the basis of 1H MRS findings suggesting the presence of brain ammonia upon reperfusion, a second cohort of piglets (N = 22) underwent the same CPB conditions without MRS to allow regular venous blood sampling with ammonia assays.

RESULTS

All animals showed a transitory temperature-dependent increase in blood ammonia (P < .001) immediately after restart of whole-body perfusion. In contrast, metabolic processing of brain ammonia, as detected by an increased 1H MRS glutamine/glutamate ratio, was also temperature dependent (P = .002) but only significantly observed in the CA studies (P = .009).

CONCLUSIONS

Serial 1H-MRS and blood ammonia assays in this preclinical CPB model identified a previously unreported build-up of ammonia, hypothesized to arise from gut bacterial production, after reperfusion, that may contribute to brain injury in these pediatric surgeries.

Reproducibility Made Easy: A Tool for Methodological Transparency and Efficient Standardized Reporting based on the proposed MRSinMRS Consensus

Recent expert consensus publications have highlighted the issue of poor reproducibility in magnetic resonance spectroscopy (MRS) studies, mainly due to the lack of standardized reporting criteria, which affects their clinical applicability. To combat this, guidelines for minimum reporting standards (MRSinMRS) were introduced to aid journal editors and reviewers in ensuring the comprehensive documentation of essential MRS study parameters. Despite these efforts, the implementation of MRSinMRS standards has been slow, attributed to the diverse nomenclature used by different vendors, the variety of raw MRS data formats, and the absence of appropriate software tools for identifying and reporting necessary parameters. To overcome this obstacle, we have developed the REproducibility Made Easy (REMY) standalone toolbox. REMY software supports a range of MRS data formats from major vendors like GE (p. file), Siemens (Twix, .rda, .dcm), Philips (.spar/.sdat), and Bruker (.method), and MRS-NIfTI (.nii/.json) files (.nii/.nii.gz) facilitating easy data import and export through a user-friendly interface. REMY employs external libraries such as spec2nii and pymapVBVD to accurately read and process these diverse data formats, translating complex header information into a comprehensive structure that adheres to consensus reporting standards, thereby ensuring compatibility and ease of use for researchers in generating reproducible MRS research outputs. Users can select and import datasets, choose the appropriate vendor and data format, and then generate an MRSinMRS table, log file, and methodological documents in both Latex and PDF formats. No coding knowledge is required, making the tool accessible to a wider range of users, including researchers and clinicians without programming expertise. This eliminates technical challenges related to data formatting and reporting. REMY effectively populated key sections of the MRSinMRS table with data from all supported file types. Accurate generation of hardware parameters including field strength, manufacturer, and scanner software version were demonstrated. However, it could not input data for RF coil and additional hardware information due to their absence in the files. For the acquisition section, REMY accurately read and populated fields for the pulse sequence name, nominal voxel size, repetition time (TR), echo time (TE), number of acquisitions/excitations/shots, spectral width [Hz], and number of spectral points, significantly contributing to the completion of the 'Acquisition' fields of the table. Furthermore, REMY generates a boilerplate methods text section for manuscripts.The use of REMY will facilitate more widespread adoption of the MRSinMRS checklist within the MRS community, making it easier to write and report acquisition parameters effectively.

Imaging neuroglia

Imaging can help us understand the role neuroglia plays in health and during the course of neurologic disorders. In vivo microscopy has had a great impact on our understanding of how neuroglia behaves during health and disease. While initially the technique was hindered by the limited penetration depth in brain tissue, recent advancements lead to increasing possibilities for imaging of deeper brain structures, even at super-resolution. Unfortunately, in vivo microscopy cannot be applied in a clinical setting and thus cannot be used to study neuroglia in patient populations. However, noninvasive imaging techniques like positron emission tomography (PET) and magnetic resonance imaging (MRI) can. PET has provided valuable information on the involvement of neuroglia in neurologic disorders. To more specifically image microglia and astrocytes, many new PET biomarkers have been defined for which PET tracers are continuously developed, evaluated, and improved. A cell-type specific PET tracer with favorable imaging characteristics can have a huge impact on neuroglia research. While being less sensitive than PET, MRI is a more accessible imaging technique. Initially, only general neuroinflammation processes could be imaged with MRI, but newly developed methods and sequences allow for increasing cell-type specificity. Overall, while each imaging method comes with limitations, improvements are continuously made, all with the aim to truly understand the role that neuroglia play in health and disease.

Macromolecule Modelling for Improved Metabolite Quantification Using Short Echo Time Brain 1H-MRS at 3 T and 7 T: The PRaMM Model

To improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain 1H MRS, in which the ratios of macromolecule peak intensities are used as soft constraints. Full- and metabolite-nulled spectra were acquired in three different brain regions with different ratios of grey and white matter from six healthy volunteers, at both 3 T and 7 T. Metabolite-nulled spectra were used to identify highly correlated macromolecular signal contributions and estimate the ratios of their intensities. These ratios were then used as soft constraints in the proposed PRaMM model for quantification of full spectra. The PRaMM model was validated by comparison with a single-component macromolecule model and a macromolecule subtraction technique. Moreover, the influence of the PRaMM model on the repeatability and reproducibility compared with those other methods was investigated. The developed PRaMM model performed better than the two other approaches in all three investigated brain regions. Several estimates of metabolite concentration and their Cramér-Rao lower bounds were affected by the PRaMM model reproducibility, and repeatability of the achieved concentrations were tested by evaluating the method on a second repeated acquisitions dataset. Although the observed effects on both metrics were not significant, the fit quality metrics were improved for the PRaMM method (p ≤ 0.0001). Minimally detectable changes are in the range 0.5-1.9 mM, and the percentage coefficients of variations are lower than 10% for almost all the clinically relevant metabolites. Furthermore, potential overparameterization was ruled out. Here, the PRaMM model, a method for an improved quantification of metabolites, was developed, and a method to investigate the role of the MM background and its individual components from a clinical perspective is proposed.

Detecting Tumor-Associated Intracranial Hemorrhage Using Proton Magnetic Resonance Spectroscopy

Intracranial hemorrhage associated with primary or metastatic brain tumors is a critical condition that requires urgent intervention, often through open surgery. Nevertheless, surgical interventions may not always be feasible due to two main reasons: (1) extensive hemorrhage can obscure the underlying tumor mass, limiting radiological assessment; and (2) intracranial hemorrhage may occasionally present as the first symptom of a brain tumor without prior knowledge of its existence. The current review of case studies suggests that advanced radiological imaging techniques can improve diagnostic power for tumoral hemorrhage. Adding proton magnetic resonance spectroscopy (1H-MRS), which profiles biochemical composition of mass lesions could be valuable: it provides unique information about tumor states distinct from hemorrhagic lesions bypassing the structural obliteration caused by the hemorrhage. Recent advances in 1H-MRS techniques may enhance the modality's reliability in clinical practice. This perspective proposes that 1H-MRS can be utilized in clinical settings to enhance diagnostic power in identifying tumors underlying intracranial hemorrhage.

A study of long-term GABA and high-energy phosphate alterations in the primary motor cortex using anodal tDCS and 1H/31P MR spectroscopy

Introduction

Anodal transcranial direct current stimulation (tDCS) has been reported to modulate gamma-aminobutyric acid levels and cerebral energy consumption in the brain. This study aims to investigate long-term GABA and cerebral energy modulation following anodal tDCS over the primary motor cortex.

Method

To assess GABA and energy level changes, proton and phosphorus magnetic resonance spectroscopy data were acquired before and after anodal or sham tDCS. In anodal stimulation, a 1 mA current was applied for 20 min, and the duration of ramping the current up/down at the start and end of the intervention was 10 s. In the sham-stimulation condition, the current was first ramped up over a period of 10 s, then immediately ramped down, and the condition was maintained for the next 20 min.

Results

The GABA concentration increased significantly following anodal stimulation in the first and second post-stimulation measurements. Likewise, both ATP/Pi and PCr/Pi ratios increased after anodal stimulation in the first and second post-stimulation measurements.

Conclusion

The approach employed in this study shows the feasibility of measuring long-term modulation of GABA and high-energy phosphates following anodal tDCS targeting the left M1, offering valuable insights into the mechanisms of neuroplasticity and energy metabolism, which may have implications for applications of this intervention in clinical populations.

Advanced imaging techniques and non-invasive biomarkers in pediatric brain tumors: state of the art

In the pediatric age group, brain neoplasms are the second most common tumor category after leukemia, with an annual incidence of 6.13 per 100,000. Conventional MRI sequences, complemented by CT whenever necessary, are fundamental for the initial diagnosis and surgical planning as well as for post-operative evaluations, assessment of response to treatment, and surveillance; however, they have limitations, especially concerning histopathologic or biomolecular phenotyping and grading. In recent years, several advanced MRI sequences, including diffusion-weighted imaging, diffusion tensor imaging, arterial spin labelling (ASL) perfusion, and MR spectroscopy, have emerged as a powerful aid to diagnosis as well as prognostication; furthermore, other techniques such as diffusion kurtosis, amide proton transfer imaging, and MR elastography are being translated from the research environment to clinical practice. Molecular imaging, especially PET with amino-acid tracers, complement MRI in several aspects, including biopsy targeting and outcome prediction. Finally, radiomics with radiogenomics are opening entirely new perspectives for a quantitative approach aiming at identifying biomarkers that can be used for personalized, precision management strategies.

Improvements in precision and accuracy of complex- relative to real-domain linear combination model spectral fitting not necessarily recovered by zero filling

Although the information obtained from in vivo proton magnetic resonance spectroscopy (1H MRS) presents a complex-valued spectrum, spectral quantification generally employs linear combination model (LCM) fitting using the real spectrum alone. There is currently no known investigation comparing fit results obtained from LCM fitting over the full complex data versus the real data and how these results might be affected by common spectral preprocessing procedure zero filling. Here, we employ linear combination modeling of simulated and measured spectral data to examine two major ideas: first, whether use of the full complex rather than real-only data can provide improvements in quantification by linear combination modeling and, second, to what extent zero filling might influence these improvements. We examine these questions by evaluating the errors of linear combination model fits in the complex versus real domains against three classes of synthetic data: simulated Lorentzian singlets, simulated metabolite spectra excluding the baseline, and simulated metabolite spectra including measured in vivo baselines. We observed that complex fitting provides consistent improvements in fit accuracy and precision across all three data types. While zero filling obviates the accuracy and precision benefit of complex fitting for Lorentzian singlets and metabolite spectra lacking baselines, it does not necessarily do so for complex spectra including measured in vivo baselines. Overall, performing linear combination modeling in the complex domain can improve metabolite quantification accuracy relative to real fits alone. While this benefit can be similarly achieved via zero filling for some spectra with flat baselines, this is not invariably the case for all baseline types exhibited by measured in vivo data.

Metabolite alterations in the left dorsolateral prefrontal cortex and its association with cognitive assessments in amyotrophic lateral sclerosis: A longitudinal magnetic resonance spectroscopy study

OBJECTIVE

To characterize the longitudinal metabolite profile of the left dorsolateral prefrontal cortex (DLPFC) in amyotrophic lateral sclerosis (ALS) using magnetic resonance spectroscopy (MRS) and to examine its correlation with cognitive assessments.

METHODS

Thirteen patients at baseline and ten at follow-up, along with 14 age-, sex-, and handedness-matched healthy controls (HCs), were recruited. Three Tesla with a 64-channel coil, Point-RESolved Spectroscopy (PRESS) sequence (TR=1500 ms and TE=140 ms) was used. Metabolites in the left DLPFC were quantified using LCModel. Cognitive performance and functional impairment were assessed using the Edinburgh Cognitive and Behavioral ALS Screen (ECAS) and Revised ALS Functional Rating Scale (ALSFRS-R), respectively. Group comparisons were adjusted for multiple comparisons (p < 0.05, Bonferroni correction). The links between the brain metabolites and cognitive function were investigated using relevant correlation tests (Pearson's or Spearman's).

RESULTS

Our analysis revealed a significant difference in the choline-to-creatine ratio (tCho/tCr) among the three groups. Baseline ALS patients showed a higher tCho/tCr ratio than HCs (p = 0.033, Bonferroni-corrected). Interestingly, the total N-acetyl aspartate (tNAA)/tCr ratio, a marker of neuronal health, was strongly positively correlated with visuospatial cognitive scores at baseline and follow-up. Furthermore, at follow-up, tNAA/tCr was positively correlated with the total scores and specific sub-scores on the ECAS, encompassing both ALS-specific and non-specific cognitive domains. At follow-up, positive correlations emerged between tNAA/tCr and the total language and executive function scores.

CONCLUSIONS

Metabolite alterations and correlations with cognition were observed in the left DLPFC of ALS patients, supporting extra-motor involvement and its association with cognitive decline.

Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility

Neuroimaging plays a crucial role in understanding brain structure and function, but the lack of transparency, reproducibility, and reliability of findings is a significant obstacle for the field. To address these challenges, there are ongoing efforts to develop reporting checklists for neuroimaging studies to improve the reporting of fundamental aspects of study design and execution. In this review, we first define what we mean by a neuroimaging reporting checklist and then discuss how a reporting checklist can be developed and implemented. We consider the core values that should inform checklist design, including transparency, repeatability, data sharing, diversity, and supporting innovations. We then share experiences with currently available neuroimaging checklists. We review the motivation for creating checklists and whether checklists achieve their intended objectives, before proposing a development cycle for neuroimaging reporting checklists and describing each implementation step. We emphasize the importance of reporting checklists in enhancing the quality of data repositories and consortia, how they can support education and best practices, and how emerging computational methods, like artificial intelligence, can help checklist development and adherence. We also highlight the role that funding agencies and global collaborations can play in supporting the adoption of neuroimaging reporting checklists. We hope this review will encourage better adherence to available checklists and promote the development of new ones, and ultimately increase the quality, transparency, and reproducibility of neuroimaging research.

Model-based frequency-and-phase correction of 1H MRS data with 2D linear-combination modeling

PURPOSE

Retrospective frequency-and-phase correction (FPC) methods attempt to remove frequency-and-phase variations between transients to improve the quality of the averaged MR spectrum. However, traditional FPC methods like spectral registration struggle at low SNR. Here, we propose a method that directly integrates FPC into a 2D linear-combination model (2D-LCM) of individual transients ("model-based FPC"). We investigated how model-based FPC performs compared to the traditional approach, i.e., spectral registration followed by 1D-LCM in estimating frequency-and-phase drifts and, consequentially, metabolite level estimates.

METHODS

We created synthetic in-vivo-like 64-transient short-TE sLASER datasets with 100 noise realizations at 5 SNR levels and added randomly sampled frequency and phase variations. We then used this synthetic dataset to compare the performance of 2D-LCM with the traditional approach (spectral registration, averaging, then 1D-LCM). Outcome measures were the frequency/phase/amplitude errors, the SD of those ground-truth errors, and amplitude Cramér Rao lower bounds (CRLBs). We further tested the proposed method on publicly available in-vivo short-TE PRESS data.

RESULTS

2D-LCM estimates (and accounts for) frequency-and-phase variations directly from uncorrected data with equivalent or better fidelity than the conventional approach. Furthermore, 2D-LCM metabolite amplitude estimates were at least as accurate, precise, and certain as the conventionally derived estimates. 2D-LCM estimation of FPC and amplitudes performed substantially better at low-to-very-low SNR.

CONCLUSION

Model-based FPC with 2D linear-combination modeling is feasible and has great potential to improve metabolite level estimation for conventional and dynamic MRS data, especially for low-SNR conditions, for example, long TEs or strong diffusion weighting.

CAA-ri Masquerading as a High-Grade Glioma: A Case Report

This case describes a 76-year-old male with initial clinical concern for a high-grade glioma, who was ultimately diagnosed with cerebral amyloid angiopathy-related inflammation The patient's presentation included a tonic-clonic seizure followed by aphasia and right-sided hemiparesis. Magnetic resonance brain imaging demonstrated a large left frontal lesion with parenchymal contrast enhancement. Magnetic resonance spectroscopy indicated elevated choline to creatine and choline to N-acetyl aspartate ratios, further suggestive of high-grade glioma. However, subsequent biopsy findings revealed perivascular amyloid deposits, confirming the diagnosis of CAA-ri. To our knowledge, this is the first case in literature to report elevated choline to creatine and choline to N-acetyl aspartate ratios in cerebral amyloid angiopathy-related inflammation.

The use of 7T MRI in multiple sclerosis: review and consensus statement from the North American Imaging in Multiple Sclerosis Cooperative

The use of ultra-high-field 7-Tesla (7T) MRI in multiple sclerosis (MS) research has grown significantly over the past two decades. With recent regulatory approvals of 7T scanners for clinical use in 2017 and 2020, the use of this technology for routine care is poised to continue to increase in the coming years. In this context, the North American Imaging in MS Cooperative (NAIMS) convened a workshop in February 2023 to review the previous and current use of 7T technology for MS research and potential future research and clinical applications. In this workshop, experts were tasked with reviewing the current literature and proposing a series of consensus statements, which were reviewed and approved by the NAIMS. In this review and consensus paper, we provide background on the use of 7T MRI in MS research, highlighting this technology's promise for identification and quantification of aspects of MS pathology that are more difficult to visualize with lower-field MRI, such as grey matter lesions, paramagnetic rim lesions, leptomeningeal enhancement and the central vein sign. We also review the promise of 7T MRI to study metabolic and functional changes to the brain in MS. The NAIMS provides a series of consensus statements regarding what is currently known about the use of 7T MRI in MS, and additional statements intended to provide guidance as to what work is necessary going forward to accelerate 7T MRI research in MS and translate this technology for use in clinical practice and clinical trials. This includes guidance on technical development, proposals for a universal acquisition protocol and suggestions for research geared towards assessing the utility of 7T MRI to improve MS diagnostics, prognostics and therapeutic efficacy monitoring. The NAIMS expects that this article will provide a roadmap for future use of 7T MRI in MS.

Advances in imaging modalities for spinal tumors

The spinal cord occupies a narrow region and is tightly surrounded by osseous and ligamentous structures; spinal tumors can damage this structure and deprive patients of their ability to independently perform activities of daily living. Hence, imaging is vital for the prompt detection and accurate diagnosis of spinal tumors, as well as determining the optimal treatment and follow-up plan. However, many clinicians may not be familiar with the imaging characteristics of spinal tumors due to their rarity. In addition, spinal surgeons might not fully utilize imaging for the surgical planning and management of spinal tumors because of the complex heterogeneity of these lesions. In the present review, we focus on conventional and advanced spinal tumor imaging techniques. These imaging modalities include computed tomography, positron emission tomography, digital subtraction angiography, conventional and microstructural magnetic resonance imaging, and high-resolution ultrasound. We discuss the advantages and disadvantages of conventional and emerging imaging modalities, followed by an examination of cutting-edge medical technology to complement current needs in the field of spinal tumors. Moreover, machine learning and artificial intelligence are anticipated to impact the application of spinal imaging techniques. Through this review, we discuss the importance of conventional and advanced spinal tumor imaging, and the opportunity to combine advanced technologies with conventional modalities to better manage patients with these lesions.

ECCENTRIC: A fast and unrestrained approach for high-resolution in vivo metabolic imaging at ultra-high field MR

A novel method for fast and high-resolution metabolic imaging, called ECcentric Circle ENcoding TRajectorIes for Compressed sensing (ECCENTRIC), has been developed at 7 Tesla MRI. ECCENTRIC is a non-Cartesian spatial-spectral encoding method designed to accelerate magnetic resonance spectroscopic imaging (MRSI) with high signal-to-noise at ultra-high field. The approach provides flexible and random sampling of the Fourier space without temporal interleaving to improve spatial response function and spectral quality. ECCENTRIC enables the implementation of spatial-spectral MRSI with reduced gradient amplitudes and slew-rates, thereby mitigating electrical, mechanical, and thermal stress of the scanner hardware. Moreover, it exhibits robustness against timing imperfections and eddy-current delay. Combined with a model-based low-rank reconstruction, this approach enables simultaneous imaging of up to 14 metabolites over the whole brain at 2-3 mm isotropic resolution in 4-10 min. MRSI ECCENTRIC was performed on four healthy volunteers, yielding high-resolution spatial mappings of neurochemical profiles within the human brain. This innovative tool introduces a novel approach to neuroscience, providing new insights into the exploration of brain activity and physiology.

Sequence building block for magnetic resonance spectroscopy on Siemens VE-series scanners

We present a sequence building block (SBB) that embeds magnetic resonance spectroscopy (MRS) into another sequence on the Siemens VE platform without any custom hardware. This enables dynamic studies such as functional MRS (fMRS), dynamic shimming and frequency correction, and acquisition of navigator images for motion correction. The SBB supports nonlocalised spectroscopy (free induction decay), STimulated Echo Acquisition Mode single voxel spectroscopy, and 1D, 2D and 3D phase-encoded chemical shift imaging. It can embed 1H or X-nuclear MRS into a 1H sequence; and 1H-MRS into an X-nuclear sequence. We demonstrate integration into the vendor's gradient-recalled echo sequence. We acquire test data in phantoms with three coils (31P/1H, 13C/1H and 2H/1H) and in two volunteers on a 7-T Terra MRI scanner. Fifteen lines of code are required to insert the SBB into a sequence. Spectra and images are acquired successfully in all cases in phantoms, and in human abdomen and calf muscle. Phantom comparison of signal-to-noise ratio and linewidth showed that the SBB has negligible effects on image and spectral quality, except that it sometimes produces a nuclear Overhauser effect (NOE) signal enhancement for multinuclear applications in line with conventional 1H NOE pulses. Our new SBB embeds MRS into a host imaging or spectroscopy sequence in 15 lines of code. It allows homonuclear and heteronuclear interleaving. The package is available through the standard C2P procedure. We hope this will lower the barrier for entry to studies applying dynamic fMRS and for online motion correction and B0-shim updating.

Understanding Proton Magnetic Resonance Spectroscopy Neurochemical Changes Using Alzheimer's Disease Biofluid, PET, Postmortem Pathology Biomarkers, and APOE Genotype

In vivo proton (1H) magnetic resonance spectroscopy (MRS) is a powerful non-invasive method that can measure Alzheimer's disease (AD)-related neuropathological alterations at the molecular level. AD biomarkers include amyloid-beta (Aβ) plaques and hyperphosphorylated tau neurofibrillary tangles. These biomarkers can be detected via postmortem analysis but also in living individuals through positron emission tomography (PET) or biofluid biomarkers of Aβ and tau. This review offers an overview of biochemical abnormalities detected by 1H MRS within the biologically defined AD spectrum. It includes a summary of earlier studies that explored the association of 1H MRS metabolites with biofluid, PET, and postmortem AD biomarkers and examined how apolipoprotein e4 allele carrier status influences brain biochemistry. Studying these associations is crucial for understanding how AD pathology affects brain homeostasis throughout the AD continuum and may eventually facilitate the development of potential novel therapeutic approaches.

Brain glycogen build-up measured by magnetic resonance spectroscopy in classic infantile Pompe disease

Classic infantile Pompe disease is caused by abnormal lysosomal glycogen accumulation in multiple tissues, including the brain due to a deficit in acid α-glucosidase. Although treatment with recombinant human acid α-glucosidase has dramatically improved survival, recombinant human acid α-glucosidase does not reach the brain, and surviving classic infantile Pompe patients develop progressive cognitive deficits and white matter lesions. We investigated the feasibility of measuring non-invasively glycogen build-up and other metabolic alterations in the brain of classic infantile Pompe patients. Four classic infantile patients (8-16 years old) and 4 age-matched healthy controls were scanned on a 7 T MRI scanner. We used T2-weighted MRI to assess the presence of white matter lesions as well as 1H magnetic resonance spectroscopy and magnetic resonance spectroscopy imaging to obtain the neurochemical profile and its spatial distribution, respectively. All patients had widespread white matter lesions on T2-weighted images. Magnetic resonance spectroscopy data from a single volume of interest positioned in the periventricular white matter showed a clear shift in the neurochemical profile, particularly a significant increase in glycogen (result of acid α-glucosidase deficiency) and decrease in N-acetyl-aspartate (marker of neuronal damage) in patients. Magnetic resonance spectroscopy imaging results were in line and showed a widespread accumulation of glycogen and a significant lower level of N-acetyl-aspartate in patients. Our results illustrate the unique potential of 1H magnetic resonance spectroscopy (imaging) to provide a non-invasive readout of the disease pathology in the brain. Further study will assess its potential to monitor disease progression and the correlation with cognitive decline.

Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation

PURPOSE

The interest in applying and modeling dynamic MRS has recently grown. Two-dimensional modeling yields advantages for the precision of metabolite estimation in interrelated MRS data. However, it is unknown whether including all transients simultaneously in a 2D model without averaging (presuming a stable signal) performs similarly to one-dimensional (1D) modeling of the averaged spectrum. Therefore, we systematically investigated the accuracy, precision, and uncertainty estimation of both described model approaches.

METHODS

Monte Carlo simulations of synthetic MRS data were used to compare the accuracy and uncertainty estimation of simultaneous 2D multitransient linear-combination modeling (LCM) with 1D-LCM of the average. A total of 2,500 data sets per condition with different noise representations of a 64-transient MRS experiment at six signal-to-noise levels for two separate spin systems (scyllo-inositol and gamma-aminobutyric acid) were analyzed. Additional data sets with different levels of noise correlation were also analyzed. Modeling accuracy was assessed by determining the relative bias of the estimated amplitudes against the ground truth, and modeling precision was determined by SDs and Cramér-Rao lower bounds (CRLBs).

RESULTS

Amplitude estimates for 1D- and 2D-LCM agreed well and showed a similar level of bias compared with the ground truth. Estimated CRLBs agreed well between both models and with ground-truth CRLBs. For correlated noise, the estimated CRLBs increased with the correlation strength for the 1D-LCM but remained stable for the 2D-LCM.

CONCLUSION

Our results indicate that the model performance of 2D multitransient LCM is similar to averaged 1D-LCM. This validation on a simplified scenario serves as a necessary basis for further applications of 2D modeling.

Absolute Metabolite Quantification in Individuals with Glioma and Healthy Individuals Using Whole-Brain Three-dimensional MR Spectroscopic and Echo-planar Time-resolved Imaging

BACKGROUND

MR spectroscopic imaging (MRSI) can be used to quantify an extended brain metabolic profile but is confounded by changes in tissue water levels due to disease.

PURPOSE

To develop a fast absolute quantification method for metabolite concentrations combining whole-brain MRSI with echo-planar time-resolved imaging (EPTI) relaxometry in individuals with glioma and healthy individuals.

MATERIALS AND METHODS

In this prospective study performed from August 2022 to August 2023, using internal water as concentration reference, the MRSI-EPTI quantification method was compared with the conventional method using population-average literature relaxation values. Healthy participants and participants with mutant IDH1 gliomas underwent imaging at 3 T with a 32-channel coil. Real-time navigated adiabatic spiral three-dimensional MRSI scans were acquired in approximately 8 minutes and reconstructed with a super-resolution pipeline to obtain brain metabolic images at 2.4-mm isotropic resolution. High-spatial-resolution multiparametric EPTI was performed in 3 minutes, with 1-mm isotropic resolution, to correct the relaxation and proton density of the water reference signal. Bland-Altman analysis and the Wilcoxon signed rank test were used to compare absolute quantifications from the proposed and conventional methods.

RESULTS

Six healthy participants (four male; mean age, 37 years ± 11 [SD]) and nine participants with glioma (six male; mean age, 41 years ± 15; one with wild-type IDH1 and eight with mutant IDH1) were included. In healthy participants, there was good agreement (+4% bias) between metabolic concentrations derived using the two methods, with a CI of plus or minus 26%. In participants with glioma, there was large disagreement between the two methods (+39% bias) and a CI of plus or minus 55%. The proposed quantification method improved tumor contrast-to-noise ratio (median values) for total N-acetyl-aspartate (EPTI: 0.541 [95% CI: 0.217, 0.910]; conventional: 0.484 [95% CI: 0.199, 0.823]), total choline (EPTI: 1.053 [95% CI: 0.681, 1.713]; conventional: 0.940 [95% CI: 0.617, 1.295]), and total creatine (EPTI: 0.745 [95% CI: 0.628, 0.909]; conventional: 0.553 [95% CI: 0.444, 0.828]) (P = .03 for all).

CONCLUSION

The whole-brain MRSI-EPTI method provided fast absolute quantification of metabolic concentrations with individual-specific corrections at 2.4-mm isotropic resolution, yielding concentrations closer to the true value in disease than the conventional literature-based corrections. © RSNA, 2024 Supplemental material is available for this article.

Predicting performance in attention by measuring key metabolites in the PCC with 7T MRS

The posterior cingulate cortex (PCC) is a key hub of the default mode network and is known to play an important role in attention. Using ultra-high field 7 Tesla magnetic resonance spectroscopy (MRS) to quantify neurometabolite concentrations, this exploratory study investigated the effect of the concentrations of myo-inositol (Myo-Ins), glutamate (Glu), glutamine (Gln), aspartate or aspartic acid (Asp) and gamma-amino-butyric acid (GABA) in the PCC on attention in forty-six healthy participants. Each participant underwent an MRS scan and cognitive testing, consisting of a trail-making test (TMT A/B) and a test of attentional performance. After a multiple regression analysis and bootstrapping for correction, the findings show that Myo-Ins and Asp significantly influence (p < 0.05) attentional tasks. On one hand, Myo-Ins shows it can improve the completion times of both TMT A and TMT B. On the other hand, an increase in aspartate leads to more mistakes in Go/No-go tasks and shows a trend towards enhancing reaction time in Go/No-go tasks and stability of alertness without signal. No significant (p > 0.05) influence of Glu, Gln and GABA was observed.

GABA and glutamate response to social processing: a functional MRS feasibility study

Several studies have suggested that atypical social processing in neurodevelopmental conditions (e.g. autism) is associated with differences in excitation and inhibition, through changes in the levels of glutamate and gamma-aminobutyric acid (GABA). While associations between baseline metabolite levels and behaviours can be insightful, assessing the neurometabolic response of GABA and glutamate during social processing may explain altered neurochemical function in more depth. Thus far, there have been no attempts to determine whether changes in metabolite levels are detectable using functional MRS (fMRS) during social processing in a control population. We performed Mescher-Garwood point resolved spectroscopy edited fMRS to measure the dynamic response of GABA and glutamate in the superior temporal sulcus (STS) and visual cortex (V1) while viewing social stimuli, using a design that allows for analysis in both block and event-related approaches. Sliding window analyses were used to investigate GABA and glutamate dynamics at higher temporal resolution. The changes of GABA and glutamate levels with social stimulus were largely non-significant. A small decrease in GABA levels was observed during social stimulus presentation in V1, but no change was observed in STS. Conversely, non-social stimulus elicited changes in both GABA and glutamate levels in both regions. Our findings suggest that the current experimental design primarily captures effects of visual stimulation, not social processing. Here, we discuss the feasibility of using fMRS analysis approaches to assess changes in metabolite response.

Advances and prospects in deuterium metabolic imaging (DMI): a systematic review of in vivo studies

BACKGROUND

Deuterium metabolic imaging (DMI) has emerged as a promising non-invasive technique for studying metabolism in vivo. This review aims to summarize the current developments and discuss the futures in DMI technique in vivo.

METHODS

A systematic literature review was conducted based on the PRISMA 2020 statement by two authors. Specific technical details and potential applications of DMI in vivo were summarized, including strategies of deuterated metabolites detection, deuterium-labeled tracers and corresponding metabolic pathways in vivo, potential clinical applications, routes of tracer administration, quantitative evaluations of metabolisms, and spatial resolution.

RESULTS

Of the 2,248 articles initially retrieved, 34 were finally included, highlighting 2 strategies for detecting deuterated metabolites: direct and indirect DMI. Various deuterated tracers (e.g., [6,6'-2H2]glucose, [2,2,2'-2H3]acetate) were utilized in DMI to detect and quantify different metabolic pathways such as glycolysis, tricarboxylic acid cycle, and fatty acid oxidation. The quantifications (e.g., lactate level, lactate/glutamine and glutamate ratio) hold promise for diagnosing malignancies and assessing early anti-tumor treatment responses. Tracers can be administered orally, intravenously, or intraperitoneally, either through bolus administration or continuous infusion. For metabolic quantification, both serial time point methods (including kinetic analysis and calculation of area under the curves) and single time point quantifications are viable. However, insufficient spatial resolution remains a major challenge in DMI (e.g., 3.3-mL spatial resolution with 10-min acquisition at 3 T).

CONCLUSIONS

Enhancing spatial resolution can facilitate the clinical translation of DMI. Furthermore, optimizing tracer synthesis, administration protocols, and quantification methodologies will further enhance their clinical applicability.

RELEVANCE STATEMENT

Deuterium metabolic imaging, a promising non-invasive technique, is systematically discussed in this review for its current progression, limitations, and future directions in studying in vivo energetic metabolism, displaying a relevant clinical potential.

KEY POINTS

• Deuterium metabolic imaging (DMI) shows promise for studying in vivo energetic metabolism. • This review explores DMI's current state, limits, and future research directions comprehensively. • The clinical translation of DMI is mainly impeded by limitations in spatial resolution.

Multi-Parametric Molecular Imaging of the Brain Using Optimized Multi-TE Subspace MRSI

OBJECTIVE

To develop a novel multi-TE MR spectroscopic imaging (MRSI) approach to enable label-free, simultaneous, high-resolution mapping of several molecules and their biophysical parameters in the brain.

METHODS

The proposed method uniquely integrated an augmented molecular-component-specific subspace model for multi-TE 1H-MRSI signals, an estimation-theoretic experiment optimization (nonuniform TE selection) for molecule separation and parameter estimation, a physics-driven subspace learning strategy for spatiospectral reconstruction and molecular quantification, and a new accelerated multi-TE MRSI acquisition for generating high-resolution data in clinically relevant times. Numerical studies, phantom and in vivo experiments were conducted to validate the optimized experiment design and demonstrate the imaging capability offered by the proposed method.

RESULTS

The proposed TE optimization improved estimation of metabolites, neurotransmitters and their T2's over conventional TE choices, e.g., reducing variances of neurotransmitter concentration by  ∼  40% and metabolite T2 by  ∼  60%. Simultaneous metabolite and neurotransmitter mapping of the brain can be achieved at a nominal resolution of 3.4 × 3.4 × 6.4 mm 3. High-resolution, 3D metabolite T2 mapping was made possible for the first time. The translational potential of the proposed method was demonstrated by mapping biochemical abnormality in a post-traumatic epilepsy (PTE) patient.

CONCLUSION

The feasibility for high-resolution mapping of metabolites/neurotransmitters and metabolite T2's within clinically relevant time was demonstrated. We expect our method to offer richer information for revealing and understanding metabolic alterations in neurological diseases.

SIGNIFICANCE

A novel multi-TE MRSI approach was presented that enhanced the technological capability of multi-parametric molecular imaging of the brain. The proposed method presents new technology development and application opportunities for providing richer molecular level information to uncover and comprehend metabolic changes relevant in various neurological applications.

MR Imaging in Ataxias: Consensus Recommendations by the Ataxia Global Initiative Working Group on MRI Biomarkers

With many viable strategies in the therapeutic pipeline, upcoming clinical trials in hereditary and sporadic degenerative ataxias will benefit from non-invasive MRI biomarkers for patient stratification and the evaluation of therapies. The MRI Biomarkers Working Group of the Ataxia Global Initiative therefore devised guidelines to facilitate harmonized MRI data acquisition in clinical research and trials in ataxias. Recommendations are provided for a basic structural MRI protocol that can be used for clinical care and for an advanced multi-modal MRI protocol relevant for research and trial settings. The advanced protocol consists of modalities with demonstrated utility for tracking brain changes in degenerative ataxias and includes structural MRI, magnetic resonance spectroscopy, diffusion MRI, quantitative susceptibility mapping, and resting-state functional MRI. Acceptable ranges of acquisition parameters are provided to accommodate diverse scanner hardware in research and clinical contexts while maintaining a minimum standard of data quality. Important technical considerations in setting up an advanced multi-modal protocol are outlined, including the order of pulse sequences, and example software packages commonly used for data analysis are provided. Outcome measures most relevant for ataxias are highlighted with use cases from recent ataxia literature. Finally, to facilitate access to the recommendations by the ataxia clinical and research community, examples of datasets collected with the recommended parameters are provided and platform-specific protocols are shared via the Open Science Framework.