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Dominant arm bioimpedance spectroscopy (BIS) and lumbar and hip dual energy X-ray absorptiometry (DXA) measurements were conducted simultaneously on 48 post-menopausal women, aged between 43 and 86 years, with no hip or arm fracture history at Department of Radiology of Istanbul University Cerrahpasa Hospital. According to lumbar DXA results, 21 women were classified as normal, 22 as osteopenia and 5 as osteoporosis; whereas hip DXA results classified 30 women as normal, 15 as osteopenia and 3 as osteoporosis. Only 26 participants had identical lumbar and hip bone mineral density (BMD) diagnostic results. Dominant arm characteristic frequencies of normal subjects were statistically significantly different from osteoporotic subjects based on both lumbar (p < 0.005) and hip classification groups (p < 0.001). Hip and lumbar spine DXA BMD values were significantly correlated (r = 0.55, p < 0.005). The dominant arm BIS characteristic frequency, considered as the single predictor in earlier diagnosis of osteoporosis, was found negatively correlated with DXA measurements for both hip and lumbar spine regions. The Spearman rank correlation coefficient of BIS values with the hip DXA values (r = -0.53, p < 0.001) was higher than that of lumbar spine (r = -0.37, p < 0.001). In receiver operating characteristic (ROC) curve analysis, the best discrimination of dominant arm characteristic frequency was made between normal and osteoporotic subjects based on the hip subgroups (p < 0.001). Both lumbar bone mineral content (BMC) (r = -0.47, p < 0.001) and hip BMC (r = -0.4340, p < 0.005) were statistically significantly correlated with dominant arm characteristic frequency.

Purpose: To investigate the signal-to-noise-ratio (SNR) and data quality of time-reduced three-dimensional (3D) proton magnetic resonance spectroscopic imaging ((1)H MRSI) techniques in the human brain at 3 Tesla.Materials and Methods: Techniques that were investigated included ellipsoidal k-space sampling, parallel imaging, and echo-planar spectroscopic imaging (EPSI). The SNR values for N-acetyl aspartate, choline, creatine, and lactate or lipid peaks were compared after correcting for effective spatial resolution and acquisition time in a phantom and in the brains of human volunteers. Other factors considered were linewidths, metabolite ratios, partial volume effects, and subcutaneous lipid contamination.Results: In volunteers, the median normalized SNR for parallel imaging data decreased by 34-42%, but could be significantly improved using regularization. The normalized signal to noise loss in flyback EPSI data was 11-18%. The effective spatial resolutions of the traditional, ellipsoidal, sensitivity encoding (SENSE) sampling scheme, and EPSI data were 1.02, 2.43, 1.03, and 1.01 cm(3), respectively. As expected, lipid contamination was variable between subjects but was highest for the SENSE data. Patient data obtained using the flyback EPSI method were of excellent quality.Conclusion: Data from all (1)H 3D-MRSI techniques were qualitatively acceptable, based upon SNR, linewidths, and metabolite ratios. The larger field of view obtained with the EPSI methods showed negligible lipid aliasing with acceptable SNR values in less than 9.5 min without compromising the point-spread function. [ABSTRACT FROM AUTHOR]

Background: Preoperative and noninvasive detection of isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutations in glioma is critical for prognosis and treatment planning. This study aims to develop deep learning classifiers to identify IDH and TERTp mutations using proton magnetic resonance spectroscopy ( 1 H-MRS) and a one-dimensional convolutional neural network (1D-CNN) architecture., Methods: This study included 1 H-MRS data from 225 adult patients with hemispheric diffuse glioma (117 IDH mutants and 108 IDH wild-type; 99 TERTp mutants and 100 TERTp wild-type). The spectra were processed using the LCModel, and multiple deep learning models, including a baseline, a deep-shallow network, and an attention deep-shallow network (ADSN), were trained to classify mutational subgroups of gliomas. The Gradient-weighted Class Activation Mapping (Grad-CAM) technique was used to interpret the models' decision-making process., Results: The ADSN model was the most effective for IDH mutation detection, achieving F1-scores of 93 % on the validation set and 88 % on the test set. For TERTp mutation detection, the ADSN model achieved F1-scores of 80 % in the validation set and 81 % in the test set, whereas TERTp-only gliomas were detected with F1-scores of 88 % in the validation set and 86 % in the test set using the same architecture., Conclusion: Deep learning models accurately predicted the IDH and TERTp mutational subgroups of hemispheric diffuse gliomas by extracting relevant information from 1 H-MRS spectra without the need for manual feature extraction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier Ltd. All rights reserved.)

S100 protein expression levels and neurofibromatosis type 2 (NF-2) mutations result in different disease courses in meningiomas. This study aimed to investigate non-invasive biomarkers of NF-2 copy number loss and S100 protein expression in meningiomas using morphological, radiomics, and deep learning-based features of susceptibility-weighted MRI (SWI). This retrospective study included 99 patients with S100 protein expression data and 92 patients with NF-2 copy number loss information. Preoperative cranial MRI was conducted using a 3T clinical MR scanner. Tumor volumes were segmented on fluid-attenuated inversion recovery (FLAIR) and subsequent registration of FLAIR to high-resolution SWI was performed. First-order textural features of SWI were extracted and assessed using Pyradiomics. Morphological features, including the tumor growth pattern, peritumoral edema, sinus invasion, hyperostosis, bone destruction, and intratumoral calcification, were semi-quantitatively assessed. Mann-Whitney U tests were utilized to assess the differences in the SWI features of meningiomas with and without S100 protein expression or NF-2 copy number loss. A logistic regression analysis was used to examine the relationship between these features and the respective subgroups. Additionally, a convolutional neural network (CNN) was used to extract hierarchical features of SWI, which were subsequently employed in a light gradient boosting machine classifier to predict the NF-2 copy number loss and S100 protein expression. NF-2 copy number loss was associated with a higher risk of developing high-grade tumors. Additionally, elevated signal intensity and a decrease in entropy within the tumoral region on SWI were observed in meningiomas with S100 protein expression. On the other hand, NF-2 copy number loss was associated with lower SWI signal intensity, a growth pattern described as "en plaque", and the presence of calcification within the tumor. The logistic regression model achieved an accuracy of 0.59 for predicting NF-2 copy number loss and an accuracy of 0.70 for identifying S100 protein expression. Deep learning features demonstrated a strong predictive capability for S100 protein expression (AUC = 0.85 ± 0.06) and had reasonable success in identifying NF-2 copy number loss (AUC = 0.74 ± 0.05). In conclusion, SWI showed promise in identifying NF-2 copy number loss and S100 protein expression by revealing neovascularization and microcalcification characteristics in meningiomas.

Introduction: Loss of blood-brain barrier (BBB) integrity is hypothesised to be one of the earliest microvascular signs of Alzheimer's disease (AD). Existing BBB integrity imaging methods involve contrast agents or ionising radiation, and pose limitations in terms of cost and logistics. Arterial spin labelling (ASL) perfusion MRI has been recently adapted to map the BBB permeability non-invasively. The DEveloping BBB-ASL as a non-Invasive Early biomarker (DEBBIE) consortium aims to develop this modified ASL-MRI technique for patient-specific and robust BBB permeability assessments. This article outlines the study design of the DEBBIE cohorts focused on investigating the potential of BBB-ASL as an early biomarker for AD (DEBBIE-AD)., Methods and Analysis: DEBBIE-AD consists of a multicohort study enrolling participants with subjective cognitive decline, mild cognitive impairment and AD, as well as age-matched healthy controls, from 13 cohorts. The precision and accuracy of BBB-ASL will be evaluated in healthy participants. The clinical value of BBB-ASL will be evaluated by comparing results with both established and novel AD biomarkers. The DEBBIE-AD study aims to provide evidence of the ability of BBB-ASL to measure BBB permeability and demonstrate its utility in AD and AD-related pathologies., Ethics and Dissemination: Ethics approval was obtained for 10 cohorts, and is pending for 3 cohorts. The results of the main trial and each of the secondary endpoints will be submitted for publication in a peer-reviewed journal., Competing Interests: Competing interests: FB is a consultant for Roche, Celltrion, Rewind Therapeutics, Merck, IXICO, Jansen, Combinostics, and has research agreements with Merck, Biogen, GE Healthcare, Roche. All other authors report no disclosures., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: Isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutations play crucial roles in glioma biology. Such genetic information is typically obtained invasively from excised tumor tissue; however, these mutations need to be identified preoperatively for better treatment planning. The relative cerebral blood volume (rCBV) information derived from dynamic susceptibility contrast MRI (DSC-MRI) has been demonstrated to correlate with tumor vascularity, functionality, and biology, and might provide some information about the genetic alterations in gliomas before surgery. Therefore, this study aims to predict IDH and TERTp mutational subgroups in gliomas using deep learning applied to rCBV images., Method: After the generation of rCBV images from DSC-MRI data, classical machine learning algorithms were applied to the features obtained from the segmented tumor volumes to classify IDH and TERTp mutation subgroups. Furthermore, pre-trained convolutional neural networks (CNNs) and CNNs enhanced with attention gates were trained using rCBV images or a combination of rCBV and anatomical images to classify the mutational subgroups., Results: The best accuracies obtained with classical machine learning algorithms were 83 %, 68 %, and 76 % for the identification of IDH mutational, TERTp mutational, and TERTp-only subgroups, respectively. On the other hand, the best-performing CNN model achieved 88 % accuracy (86 % sensitivity, 91 % specificity) for the IDH-mutational subgroups, 70 % accuracy (73 % sensitivity and 67 % specificity) for the TERTp-mutational subgroups, and 84 % accuracy (86 % sensitivity, 81 % specificity) for the TERTp-only subgroup using attention gates., Conclusions: DSC-MRI can be utilized to noninvasively classify IDH- and TERTp-based molecular subgroups of gliomas, facilitating preoperative identification of these genetic alterations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Purpose: To optimize possible combinations of echo times (TE) for multi-voxel TE-averaged Point RESolved Spectroscopy (PRESS) while reducing the total number of TEs required to separate glutamate (Glu) and glutamine (Gln) within a clinically feasible scan time., Methods: General Approach to Magnetic resonance Mathematical Analysis (GAMMA) was used to implement 2D J-resolved PRESS technique, and the spectra of 14 individual brain metabolites were simulated at 64 different TEs. Monte Carlo simulations were used for selecting the best TE combinations to separate Glu and Gln using TE-averaged PRESS with a total number of two, three, four and five TEs. Single-voxel 1 H-MRS data were acquired using 64 different TEs from a healthy volunteer on a clinical 3T MR scanner to validate the echo time combinations selected with simulations. Additionally, 2D 1 H-MRSI data of eight healthy volunteers were acquired on a clinical 3T MR scanner using four different TEs that were determined by Monte Carlo simulations. Optimized TE-averaged PRESS spectra were created by averaging the spectra acquired at selected TEs. LCModel was used for spectral quantification. A Wilcoxon signed-rank test was used to detect statistically significant differences in Glu/Gln ratios between 35 ms PRESS and optimized TE-averaged PRESS data., Results: Glu could be clearly separated from Gln at 2.35 ppm, using optimized TE-averaged PRESS with only four TEs (35, 37, 40, and 42 ms) that were selected through Monte Carlo simulations. Glu/Gln ratios were significantly higher in the optimized TE-averaged PRESS data of healthy volunteers than in the 35 ms PRESS data (P = 0.008)., Conclusion: Optimized multi-voxel TE-averaged PRESS enabled faster and unobstructed quantification of Glu at multiple voxels in the human brain in vivo at 3T., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

During the caudo-rostral progression of Lewy pathology, the amygdala is involved relatively early in Parkinson's disease (PD). However, lesser is known about the volumetric differences at the amygdala subdivisions, although the evidence mainly implicates the olfactory amygdala. We aimed to investigate the volumetric differences between the amygdala's nuclear and sectoral subdivisions in the PD cognitive impairment continuum compared to healthy controls (HC). The volumes of nine nuclei of the amygdala were estimated with FreeSurfer (nuclear parcellation-NP) from T1-weighted images of PD patients with normal cognition (PD-CN), PD with mild cognitive impairment (PD-MCI), PD with dementia (PD-D), and HC. The appropriate nuclei were then merged to obtain three sectors of the amygdala (sectoral parcellation-SP). The nuclear and sectoral volumes were compared among the four groups and between the hyposmic and normosmic PD patients. There was a significant difference in the total amygdala volume among the four groups. In terms of nuclei, the bilateral cortico-amygdaloid transition area (CAT) and sectors superficial cortex-like region (sCLR) volumes of PD-MCI and PD-D were less than those of the PD-CN and HC. A linear discriminant analysis revealed that left CAT and left sCLR volumes classified the PD-CN and cognitively impaired PD (PD-CI: PD-MCI plus PD-D) with 90.7% accuracy according to NP and 85.2% accuracy to SP. Similarly, left CAT and sCLR volumes correctly identified the hyposmic and normosmic PD with 64.8% and 61.1% accuracies. Notably, the left olfactory amygdala volume successfully discriminated cognitive impairment in PD and could be used as neuroimaging-based support for PD-CI diagnosis., Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-022-09887-y., Competing Interests: Competing interestsThe authors report no competing interests., (© The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Background: The 2021 World Health Organization (WHO) Central Nervous System (CNS) Tumor Classification has suggested that isocitrate dehydrogenase wildtype (IDH-wt) WHO grade-2/3 astrocytomas with molecular features of glioblastoma should be designated as "Glioblastoma, IDH-wildtype, WHO grade-4." This study analyzed the metabolic correlates of progression free and overall survival in "Glioblastoma, IDH-wildtype, WHO grade-4" patients using short echo time single voxel 1 H-MRS., Methods: Fifty-seven adult patients with hemispheric glioma fulfilling the 2021 WHO CNS Tumor Classification criteria for "Glioblastoma, IDH-wildtype, WHO grade-4" at presurgery time point were included. All patients were IDH1/2 -wt and TERT p-mut. 1 H-MRS was performed on a 3 T MR scanner and post-processed using LCModel. A Mann-Whitney U test was used to assess the metabolic differences between gliomas with or without contrast enhancement and necrosis. Cox regression analysis was used to assess the effects of age, extent of resection, presence of contrast enhancement and necrosis, and metabolic intensities on progression-free survival (PFS) and overall survival (OS). Machine learning algorithms were employed to discern possible metabolic patterns attributable to higher PFS or OS., Results: Contrast enhancement ( p  = 0.015), necrosis ( p  = 0.012); and higher levels of Glu/tCr ( p  = 0.007), GSH/tCr ( p  = 0.019), tCho/tCr ( p  = 0.032), and Glx/tCr ( p  = 0.010) were significantly associated with shorter PFS. Additionally, necrosis ( p  = 0.049), higher Glu/tCr ( p  = 0.039), and Glx/tCr ( p  = 0.047) were significantly associated with worse OS. Machine learning models differentiated the patients having longer than 12 months OS with 81.71% accuracy and the patients having longer than 6 months PFS with 77.41% accuracy., Conclusion: Glx and GSH have been identified as important metabolic correlates of patient survival among "IDH-wt, TERT-mut diffuse gliomas" using single-voxel 1 H-MRS on a clinical 3 T MRI scanner., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Sacli-Bilmez, Danyeli, Yakicier, Aras, Pamir, Özduman, Dinçer and Ozturk-Isik.)

Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2., (© 2023 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this second part, we review magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility-weighted imaging (SWI), MRI-PET, MR elastography (MRE), and MR-based radiomics applications. The first part of this review addresses dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF). EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2., (© 2023 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Objectives: Systemic lupus erythematosus (SLE) is a chronic inflammatory disease characterised by the presence of various autoantibodies. Mild cognitive impairment developing in patients without significant neuropsychiatric (NP) symptoms was thought to be the result of immune-mediated myelinopathy. We aimed to determine the role of myelin oligodendrocyte glycoprotein antibody (MOG-Ab) in the neurological manifestations of childhood-onset SLE (cSLE) and if there is a correlation between various metabolite peaks in magnetic resonance spectroscopy (MRS) and myelinopathy., Methods: MOG-Ab levels were studied in all healthy subjects (n=28) and in all patients with (NPSLE=9) and without (non-NPSLE=36) overt neuropsychiatric manifestations. Twenty patients (all had a normal-appearing brain on plain magnetic resonance) in non-NPSLE and 20 subjects in healthy group met the MRS imaging standards for evaluation in which normal appearing brain on plain MR., Results: A total of 45 cSLE (36 non-NPSLE and 9 NPSLE) subjects and 28 healthy children were recruited to the study. The mean age of the SLE patients at study time was 16.22±3.22 years. MOG-Ab was not detected in cSLE or in healthy group. There was no significant difference between the non-NPSLE group and healthy subjects in terms of choline, N-acetyl aspartate (NAA), creatine, NAA/creatine, and choline/creatine., Conclusions: There was no association of MOG-Ab with cSLE, whether NP manifestations were present or not. A causal relationship between immune-mediated myelinopathy and cognitive impairment could not be suggested, since there has been no patient with positive MOG-Ab and there has been no difference in choline, choline/creatine between groups.

Purpose: This study aims to evaluate qualitative and quantitative imaging metrics along with clinical features affecting overall survival in glioblastomas and to classify them into high survival and low survival groups based on 12, 19, and 24 months thresholds using machine learning., Methods: The cohort consisted of 98 adult glioblastomas. A standard brain tumor magnetic resonance (MR) imaging protocol, was performed on a 3T MR scanner. Visually Accessible REMBRANDT Images (VASARI) features were assessed. A Kaplan-Meier survival analysis followed by a log-rank test and multivariate Cox regression analysis were used to investigate the effects of VASARI features along with the age, gender, the extent of resection, pre- and post-KPS, ki67 and P53 mutation status on overall survival. Supervised machine learning algorithms were employed to predict the survival of glioblastoma patients based on 12, 19, and 24 months thresholds., Results: Tumor location (p<0.001), the proportion of non-enhancing component (p=0.0482), and proportion of necrosis (p=0.02) were significantly associated with overall survival based on Kaplan-Meier analysis. Multivariate Cox regression analysis revealed that increases in proportion of non-enhancing component (p=0.040) and proportion of necrosis (p=0.039) were significantly associated with overall survival. Machine-learning models were successful in differentiating patients living longer than 12 months with 96.40% accuracy (sensitivity=97.22%, specificity=95.55%). The classification accuracies based on 19 and 24 months survival thresholds were 70.87% (sensitivity=83.02%, specificity=60.11%) and 74.66% (sensitivity=67.58%, specificity=82.08%), respectively., Conclusion: Employing clinical and VASARI features together resulted in a successful classification of glioblastomas that would have a longer overall survival., Competing Interests: Declaration of competing interest The authors report no conflicts of interest., (Copyright © 2022 Elsevier Inc. All rights reserved.)

The primary aim of Gamma Knife (GK) radiosurgery is to deliver high-dose radiation precisely to a target while conforming to the target shape. In this study, the effects of tumor shape irregularity (TSI) on GK dose-plan quality and treatment outcomes were analyzed in 234 vestibular schwannomas. TSI was quantified using seven different metrics including volumetric index of sphericity (VioS). GK treatment plans were created on a single GK-Perfexion/ICON platform. The plan quality was measured using selectivity index (SI), gradient index (GI), Paddick's conformity index (PCI), and efficiency index (EI). Correlation and linear regression analyses were conducted between shape irregularity features and dose plan indices. Machine learning was employed to identify the shape feature that predicted dose plan quality most effectively. The treatment outcome analysis including tumor growth control and serviceable hearing preservation at 2 years, were conducted using Cox regression analyses. All TSI features correlated significantly with the dose plan indices (P < 0.0012). With increasing tumor volume, vestibular schwannomas became more spherical (P < 0.05) and the dose plan indices varied significantly between tumor volume subgroups (P < 0.001 and P < 0.01). VioS was the most effective predictor of GK indices (P < 0.001) and we obtained 89.36% accuracy (79.17% sensitivity and 100% specificity) for predicting PCI. Our results indicated that TSI had significant effects on the plan quality however did not adversely affect treatment outcomes., (© 2022. The Author(s).)

Objective: To investigate metabolic changes of mild cognitive impairment in Parkinson's disease (PD-MCI) using proton magnetic resonance spectroscopic imaging ( 1 H-MRSI)., Methods: Sixteen healthy controls (HC), 26 cognitively normal Parkinson's disease (PD-CN) patients, and 34 PD-MCI patients were scanned in this prospective study. Neuropsychological tests were performed, and three-dimensional 1 H-MRSI was obtained at 3 T. Metabolic parameters and neuropsychological test scores were compared between PD-MCI, PD-CN, and HC. The correlations between neuropsychological test scores and metabolic intensities were also assessed. Supervised machine learning algorithms were applied to classify HC, PD-CN, and PD-MCI groups based on metabolite levels., Results: PD-MCI had a lower corrected total N-acetylaspartate over total creatine ratio (tNAA/tCr) in the right precentral gyrus, corresponding to the sensorimotor network (p = 0.01), and a lower tNAA over myoinositol ratio (tNAA/mI) at a part of the default mode network, corresponding to the retrosplenial cortex (p = 0.04) than PD-CN. The HC and PD-MCI patients were classified with an accuracy of 86.4% (sensitivity = 72.7% and specificity = 81.8%) using bagged trees., Conclusion: 1 H-MRSI revealed metabolic changes in the default mode, ventral attention/salience, and sensorimotor networks of PD-MCI patients, which could be summarized mainly as 'posterior cortical metabolic changes' related with cognitive dysfunction., (© 2022. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).)

Purpose: In patients diagnosed with epilepsy, decreased ratio of N-acetyl aspartate to creatine (NAA/Cr) measured in magnetic resonance spectroscopy (MRS) has been accepted as a sign of neuronal cell loss or dysfunction. In this study, we aimed to determine whether a similar neuronal cell loss is present in a group of encephalopathy with electrical status epilepticus in sleep (ESES) patients METHODS: We performed this case-control study at a tertiary pediatric neurology center with patients with ESES. Inclusion criteria for the patient group were as follows: 1) a spike-wave index of at least 50%, 2) acquired neuropsychological regression, 3) normal cranial MRI. Eventually, a total of 21 patients with ESES and 17 control subjects were enrolled in the study. MRI of all control subjects was also within normal limits. 3D Slicer program was used for the analysis of thalamic and brain volumes. LCModel spectral fitting software was used to analyze single-voxel MRS data from the right and left thalamus of the subjects., Results: The mean age was 8.0 ± 1.88 years and 8.3 ± 1.70 years in ESES patients and the control subjects. After correcting for the main potential confounders (age and gender) with a linear regression model, NAA/Creatine ratio of the right thalamus was significantly lower in the ESES patient group compared to the healthy control group (p = 0.026). Likewise, the left thalamus NAA/Cr ratio was significantly lower in the ESES patient group than the healthy control group (p = 0.007). After correcting for age and gender, right thalamic volume was not statistically significantly smaller in ESES patients than in healthy controls (p = 0.337), but left thalamic volume was smaller in ESES patients than in healthy controls (p = 0.024)., Conclusion: In ESES patients, the NAA/Creatine ratio, which is an indicator of neuronal cell loss or dysfunction in the right and left thalamus, which appears regular on MRI, was found to be significantly lower than the healthy control group. This metabolic-induced thalamic dysfunction, which was reported for the first time up to date, may play a role in ESES epileptogenesis., (Copyright © 2021 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Mild cognitive impairment of Parkinson's disease (PD) may be an early manifestation that may progressively worsen to dementia. Cognitive decline has been associated with changes in the brain perfusion pattern. This study aimed to evaluate cerebral blood flow (CBF) deficits specific to different stages of cognitive decline. Seventeen patients with cognitively normal PD (PD-CN), 18 patients with PD with mild cognitive impairment (PD-MCI), and 16 patients with PD with dementia (PDD) were included in this study. The participants were scanned using a 3 T Philips MRI scanner. Arterial spin labelling magnetic resonance (ASL-MR) images were acquired, followed by calculation of the CBF maps, and registration onto the MNI152 brain atlas. A whole-brain voxel-based CBF comparison was performed among the patient groups using age as a covariate. The mean age of patients with PDD was significantly higher than that of patients with PD-MCI (P = 0.015) and PD-CN (P = 0.001). The CBF values of the three groups were significantly different in the left cuneus of the visual network (VN), left inferior frontal gyrus of the frontoparietal network (FPN), and left dorsomedial nucleus of the thalamus. PDD had lower perfusion values than PD-MCI group in the same regions detected in the main group analysis. Additionally, comparison of PDD with PD-CN and non-demented groups revealed that the perfusion reduction extended into the bilateral cuneus of the VN, bilateral thalami, and left inferior frontal gyrus of the FPN. PDD could be separated from PD-MCI and PD-CN stages with CBF deficits in non-dopaminergically mediated posterior and dopaminergically mediated frontal networks., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Parkinson's disease (PD) with mild cognitive impairment (PD-MCI) is currently diagnosed based on an arbitrarily predefined standard deviation of neuropsychological test scores, and more objective biomarkers for PD-MCI diagnosis are needed. The purpose of this study was to define possible brain perfusion-based biomarkers of not only mild cognitive impairment, but also risky gene carriers in PD using arterial spin labeling magnetic resonance imaging (ASL-MRI). Fifteen healthy controls (HC), 26 cognitively normal PD (PD-CN), and 27 PD-MCI subjects participated in this study. ASL-MRI data were acquired by signal targeting with alternating radio-frequency labeling with Look-Locker sequence at 3 T. Single nucleotide polymorphism genotyping for rs9468 [microtubule-associated protein tau (MAPT) H1/H1 versus H1/H2 haplotype] was performed using a Stratagene Mx3005p real-time polymerase chain-reaction system (Agilent Technologies, USA). There were 15 subjects with MAPT H1/H1 and 11 subjects with MAPT H1/H2 within PD-MCI, and 33 subjects with MAPT H1/H1 and 19 subjects with MAPT H1/H2 within all PD. Voxel-wise differences of cerebral blood flow (CBF) values between HC, PD-CN and PD-MCI were assessed by one-way analysis of variance followed by pairwise post hoc comparisons. Further, the subgroup of PD patients carrying the risky MAPT H1/H1 haplotype was compared with noncarriers (MAPT H1/H2 haplotype) in terms of CBF by a two-sample t test. A pattern that could be summarized as "posterior hypoperfusion" (PH) differentiated the PD-MCI group from the HC group with an accuracy of 92.6% (sensitivity = 93%, specificity = 93%). Additionally, the PD patients with MAPT H1/H1 haplotype had decreased perfusion than the ones with H1/H2 haplotype at the posterior areas of the visual network (VN), default mode network (DMN), and dorsal attention network (DAN). The PH-type pattern in ASL-MRI could be employed as a biomarker of both current cognitive impairment and future cognitive decline in PD.

Background: There is a growing interest in noninvasively defining molecular subsets of hemispheric diffuse gliomas based on the isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutation status, which correspond to distinct tumor entities, and differ in demographics, natural history, treatment response, recurrence, and survival patterns., Purpose: To investigate whether metabolite levels detected with short echo time (TE) proton MR spectroscopy ( 1 H-MRS) at 3T can be used for noninvasive molecular classification of IDH and TERTp mutation-based subsets of gliomas., Study Type: Retrospective., Subjects: In all, 112 hemispheric diffuse gliomas (70 males/42 females, mean age: 42.1 ± 13.9 years)., Field Strength/sequence: Short-TE 1 H-MRS (repetition time (TR) = 2000 msec, TE = 30 msec, number of signal averages = 192) and routine clinical brain tumor MR protocols were acquired at 3T., Assessment: 1 H-MRS data were quantified using LCModel software. TERTp and IDH1 or IDH2 (IDH1/2) mutations in the tissue were determined by either minisequencing or Sanger sequencing., Statistical Tests: Metabolic differences between IDH mutant and IDH wildtype gliomas were assessed by a Mann-Whitney U-test. A Kruskal-Wallis test followed by a Tukey-Kramer test was used to analyze metabolic differences between IDH and TERTp mutational molecular subsets of gliomas. A Spearman rank correlation coefficient was used to assess the correlations of metabolite intensities with the Ki-67 index. Furthermore, machine learning was employed to classify the IDH and TERTp mutational status of gliomas, and the accuracy, sensitivity, and specificity values were estimated., Results: Short-TE 1 H-MRS classified the presence of an IDH mutation with 88.39% accuracy, 76.92% sensitivity, and 94.52% specificity, and a TERTp mutation within primary IDH wildtype gliomas with 92.59% accuracy, 83.33% sensitivity, and 95.24% specificity., Data Conclusion: Short-TE 1 H-MRS could be used to identify molecular subsets of hemispheric diffuse gliomas corresponding to IDH and TERTp mutations., Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1799-1809., (© 2019 International Society for Magnetic Resonance in Medicine.)

Objective: The objective of this study was to assess the contribution of multi-parametric (mp) magnetic resonance imaging (MRI) quantitative features in the machine learning-based grading of gliomas with a multi-region-of-interests approach., Materials and Methods: Forty-three patients who were newly diagnosed as having a glioma were included in this study. The patients were scanned prior to any therapy using a standard brain tumor magnetic resonance (MR) imaging protocol that included T1 and T2-weighted, diffusion-weighted, diffusion tensor, MR perfusion and MR spectroscopic imaging. Three different regions-of-interest were drawn for each subject to encompass tumor, immediate tumor periphery, and distant peritumoral edema/normal. The normalized mp-MRI features were used to build machine-learning models for differentiating low-grade gliomas (WHO grades I and II) from high grades (WHO grades III and IV). In order to assess the contribution of regional mp-MRI quantitative features to the classification models, a support vector machine-based recursive feature elimination method was applied prior to classification., Results: A machine-learning model based on support vector machine algorithm with linear kernel achieved an accuracy of 93.0%, a specificity of 86.7%, and a sensitivity of 96.4% for the grading of gliomas using ten-fold cross validation based on the proposed subset of the mp-MRI features., Conclusion: In this study, machine-learning based on multiregional and multi-parametric MRI data has proven to be an important tool in grading glial tumors accurately even in this limited patient population. Future studies are needed to investigate the use of machine learning algorithms for brain tumor classification in a larger patient cohort., (Copyright © 2018. Published by Elsevier Ltd.)

The purpose of this study was to apply compressed sensing method for accelerated phosphorus MR spectroscopic imaging ( 31 P-MRSI) of human brain in vivo at 3T. Fast 31 P-MRSI data of five volunteers were acquired on a 3T clinical MR scanner using pulse-acquire sequence with a pseudorandom undersampling pattern for a data reduction factor of 5.33 and were reconstructed using compressed sensing. Additionally, simulated 31 P-MRSI human brain tumor datasets were created to analyze the effects of k-space sampling pattern, data matrix size, regularization parameters of the reconstruction, and noise on the compressed sensing accelerated 31 P-MRSI data. The 31 P metabolite peak ratios of the full and compressed sensing accelerated datasets of healthy volunteers in vivo were similar according to the results of a Bland-Altman test. The estimated effective spatial resolution increased with reduction factor and sampling more at the k-space center. A lower regularization parameter for both total variation and L1-norm penalties resulted in a better compressed sensing reconstruction of 31 P-MRSI. Although the root-mean-square error increased with noise levels, the compressed sensing reconstruction was robust for up to a reduction factor of 10 for the simulated data that had sharply defined tumor borders. As a result, compressed sensing was successfully applied to accelerate 31 P-MRSI of human brain in vivo at 3T.

The high-fidelity reconstruction of compressed and low-resolution magnetic resonance (MR) data is essential for simultaneously improving patient care, accuracy in diagnosis and quality in clinical research. Sponsored by the Royal Society through the Newton Mobility Grant Scheme, we held a half-day workshop on reconstruction schemes for MR data on 17 August 2016 to discuss new ideas from related research fields that could be useful to overcome the shortcomings of the conventional reconstruction methods that have been evaluated to date. Participants were 21 university students, computer scientists, image analysts, engineers and physicists from institutions from six different countries. The discussion evolved around exploring new avenues to achieve high resolution, high quality and fast acquisition of MR imaging. In this article, we summarize the topics covered throughout the workshop and make recommendations for ongoing and future works.

This study aims classification of phosphorus magnetic resonance spectroscopic imaging ((31)P-MRSI) data of human brain tumors using machine-learning algorithms. The metabolite peak intensities and ratios were estimated for brain tumor and healthy (31)P MR spectra acquired at 3T. The spectra were classified based on metabolite characteristics using logistic regression and support vector machine. This study showed that machine learning could be successfully applied for classification of (31)P-MR spectra of brain tumors. Future studies will measure the performance of classification algorithms for (31)P-MRSI of brain tumors in a larger patient cohort.

Objective: This study aimed at evaluating linear discriminant analysis (LDA) and support vector machine (SVM) classifiers for estimating final Gleason score preoperatively using multiparametric magnetic resonance imaging (mp-MRI) and clinical parameters., Materials and Methods: Thirty-three patients who underwent mp-MRI on a 3T clinical MR scanner and radical prostatectomy were enrolled in this study. The input features for classifiers were age, the presence of a palpable prostate abnormality, prostate specific antigen (PSA) level, index lesion size, and Likert scales of T2 weighted MRI (T2w-MRI), diffusion weighted MRI (DW-MRI), and dynamic contrast enhanced MRI (DCE-MRI) estimated by an experienced radiologist. SVM based recursive feature elimination (SVM-RFE) was used for eliminating features. Principal component analysis (PCA) was applied for data uncorrelation., Results: Using a standard PCA before final Gleason score classification resulted in mean sensitivities of 51.19% and 64.37% and mean specificities of 72.71% and 39.90% for LDA and SVM, respectively. Using a Gaussian kernel PCA resulted in mean sensitivities of 86.51% and 87.88% and mean specificities of 63.99% and 56.83% for LDA and SVM, respectively., Conclusion: SVM classifier resulted in a slightly higher sensitivity but a lower specificity than LDA method for final Gleason score prediction for prostate cancer for this limited patient population.

This study aims at assessing the performance of compressed sensing method for faster phosphorus magnetic resonance spectroscopic imaging ((31)P-MRSI) of human brain. A simulated 2D (31)P-MRSI dataset containing a tumor region and a healthy region was created based on the metabolite peak intensities and ratios of a volunteer dataset acquired at 3T. k-space data was randomly undersampled, and reconstructed using compressed sensing algorithm. This simulation study showed that compressed sensing reconstruction could be applied for faster (31)P-MRSI. Future studies will measure the performance of compressed sensing reconstruction for (31)P-MRSI in volunteers and patients with brain tumors.

The purpose of this study was to implement a new lactate-edited 3D 1H magnetic resonance spectroscopic imaging (MRSI) sequence at 3 T and demonstrate the feasibility of using this sequence for measuring lactate in patients with gliomas. A 3D PRESS MRSI sequence incorporating shortened, high bandwidth 180° pulses, new dual BASING lactate-editing pulses, high bandwidth very selective suppression (VSS) pulses and a flyback echo-planar readout was implemented at 3 T. Over-prescription factor of PRESS voxels was optimized using phantom to minimize chemical shift artifacts. The lactate-edited flyback sequence was compared with lactate-edited MRSI using conventional elliptical k-space sampling in a phantom and volunteers, and then applied to patients with gliomas. The results demonstrated the feasibility of detecting lactate within a short scan time of 9.5 min in both phantoms and patients. Over-prescription of voxels gave less chemical shift artifacts allowing detection of lactate on the majority of the selected volume. The normalized SNR of brain metabolites using the flyback encoding were comparable to the SNR of brain metabolites using conventional phase encoding MRSI. The specialized lactate-edited 3D MRSI sequence was able to detect lactate in brain tumor patients at 3 T. The implementation of this technique means that brain lactate can be evaluated in a routine clinical setting to study its potential as a marker for prognosis and response to therapy.

We report metabolic images of (13)C, following injection of a bolus of hyperpolarized [1-(13)C] pyruvate in a live rat. The data were acquired on a clinical scanner, using custom coils for volume transmission and array reception. Proton blocking of all carbon resonators enabled proton anatomic imaging with the system body coil, to allow for registration of anatomic and metabolic images, for which good correlation was achieved, with some anatomic features (kidney and heart) clearly visible in a carbon image, without reference to the corresponding proton image. Parallel imaging with sensitivity encoding was used to increase the spatial resolution in the SI direction of the rat. The signal to noise ratio in was in some instances unexpectedly high in the parallel images; variability of the polarization among different trials, plus partial volume effects, are noted as a possible cause of this., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Magnetic Resonance Spectroscopic Imaging (MRSI) is a technique for imaging spatial variation of metabolites and has been very useful in characterizing biochemical changes associated with disease as well as response to therapy in malignant pathologies. This work presents a self-calibrated undersampling to accelerate 3D elliptical MRSI and an extrapolation-reconstruction algorithm based on the GRAPPA method. The accelerated MRSI technique was tested in three volunteers and five brain tumor patients. Acceleration allowed larger spatial coverage and consequently, less lipid contamination in spectra, compared to fully sampled acquisition within the same scantime. Metabolite concentrations measured from the accelerated acquisitions were in good agreement with measurements obtained from fully sampled MRSI scans.