Magnetization-prepared Rapid Gradient Echo Research Articles

NIMG-40. RADIOMICS-BASED DIFFERENTIATION OF GLIOBLASTOMA AND METASTATIC DISEASE: DOES DIFFERENCE IN TYPE OF T1-CONTRAST ENHANCED SEQUENCE MATTER?

Abstract BACKGROUND To evaluate the radiomics-based model performance for differentiation between glioblastoma (GB) and intracranial metastatic disease (IMD) using magnetization prepared rapid gradient echo (MPRAGE) and volumetric interpolated breath-hold examination (VIBE) T1-contrast enhanced sequences. MATERIALS AND METHODS T1-CE MPRAGE and VIBE sequences acquired in 108 patients (31 GBs and 77 IMD) during the same MRI session were retrospectively evaluated. Post standardized image pre-processing and segmentation, radiomics features were extracted from necrotic and enhancing tumor components. A total of 90 machine learning (ML) pipelines were evaluated using a five-fold cross-validation. Performance was measured by mean AUC, Log-loss, and Brier scores. Pearson correlation analysis of radiomics features from tumor subcomponents was also performed. RESULTS The mean AUC across the top-ten pipelines varied between 0.890-0.851 with T1-CE MPRAGE and 0.907-0.869 with T1-CE VIBE sequence. Top performing models for the MPRAGE sequence commonly used support vector machines, while those for VIBE sequence used either support vector machines or random forest. Common feature reduction methods for top-performing models included linear combination filter and least absolute shrinkage and selection operator (LASSO) for both sequences. Only three of the top ten models used the same combination of feature reduction-ML algorithm pipeline. A feature-wise comparison showed that the radiomic features between sequences were strongly correlated, with the highest correlation for shape-based features. CONCLUSION Radiomics features derived from T1-CE MPRAGE and VIBE sequences may have similar overall classification performance for differentiating GB from IMD, albeit often using different feature selection-ML algorithm pipelines.

Oxygen extraction fraction mapping based combining quantitative susceptibility mapping and quantitative blood oxygenation level-dependent imaging model using multi-delay PCASL

Background and purposeThe oxygen extraction fraction is an essential biomarker for the assessment of brain metabolism. A recently proposed method combined with quantitative susceptibility mapping and quantitative blood oxygen level-dependent magnitude enables noninvasive mapping of the oxygen extraction fraction. Our study investigated the oxygen extraction fraction mapping variations of single-delay and multi-delay arterial spin-labeling. Materials and methodsA total of twenty healthy participants were enrolled. The multi-echo spoiled gradient-echo, multi-delay arterial spin-labeling, and magnetization-prepared rapid gradient echo sequences were acquired at 3.0 T. The mean oxygen extraction fraction was generated under a single delay time of 1780 ms, multi-delay arterial spin-labeling of transit-corrected cerebral blood flow, and multi-delay arterial spin-labeling of arterial cerebral blood volume. The results were compared via paired t tests and the Wilcoxon test. Linear regression analyses were used to investigate the relationships among the oxygen extraction fraction, cerebral blood flow, and venous cerebral blood volume. ResultsThe oxygen extraction fraction estimate with multi-delay arterial spin-labeling yielded a significantly lower value than that with single-delay arterial spin-labeling. The average values for the whole brain under single-delay arterial spin-labeling, multi-delay arterial spin-labeling of transit-corrected cerebral blood flow, and multi-delay arterial spin-labeling of arterial cerebral blood volume were 41.5 ± 1.7 % (P < 0.05), 41.3 ± 1.9 % (P < 0.001), and 40.9 ± 1.9 % (N = 20), respectively. The oxygen extraction fraction also showed a significant inverse correlation with the venous cerebral blood volume under steady-state conditions when multi-delay arterial spin-labeling was used (r = 0.5834, p = 0.0069). ConclusionThese findings suggest that the oxygen extraction fraction is significantly impacted by the arterial spin-labeling methods used in the quantitative susceptibility mapping plus the quantitative blood oxygen level-dependent model, indicating that the differences should be accounted for when employing oxygen extraction fraction mapping based on this model in diseases.

Quantitative evaluation of Scout Accelerated Motion Estimation and Reduction (SAMER) MPRAGE for morphometric analysis of brain tissue in patients undergoing evaluation for memory loss

BackgroundThree-dimensional (3D) T1-weighted MRI sequences such as the magnetization prepared rapid gradient echo (MPRAGE) sequence are important for assessing regional cortical atrophy in the clinical evaluation of dementia but have long acquisition times and are prone to motion artifact. The recently developed Scout Accelerated Motion Estimation and Reduction (SAMER) retrospective motion correction method addresses motion artifact within clinically-acceptable computation times and has been validated through qualitative evaluation in inpatient and emergency settings. MethodsWe evaluated the quantitative accuracy of morphometric analysis of SAMER motion-corrected compared to non-motion-corrected MPRAGE images by estimating cortical volume and thickness across neuroanatomical regions in two subject groups: (1) healthy volunteers and (2) patients undergoing evaluation for dementia. In part (1), we used a set of 108 MPRAGE reconstructed images derived from 12 healthy volunteers to systematically assess the effectiveness of SAMER in correcting varying degrees of motion corruption, ranging from mild to severe. In part (2), 29 patients who were scheduled for brain MRI with memory loss protocol and had motion corruption on their clinical MPRAGE scans were prospectively enrolled. ResultsIn part (1), SAMER resulted in effective correction of motion-induced cortical volume and thickness reductions. We observed systematic increases in the estimated cortical volume and thickness across all neuroanatomical regions and a relative reduction in percent error values compared to reference standard scans of up to 66 % for the cerebral white matter volume. In part (2), SAMER resulted in statistically significant volume increases across anatomical regions, with the most pronounced increases seen in the parietal and temporal lobes, and general reductions in percent error relative to reference standard clinical scans. ConclusionSAMER improves the accuracy of morphometry through systematic increases and recovery of the estimated cortical volume and cortical thickness following motion correction, which may affect the evaluation of regional cortical atrophy in patients undergoing evaluation for dementia.

Cortical and subcortical structural changes in pediatric patients with infratentorial tumors.

This study aimed to detect supratentorial cortical and subcortical morphological changes in pediatric patients with infratentorial tumors. The study included 24 patients aged 4-18 years who were diagnosed with primary infratentorial tumors and 41 age- and gender-matched healthy controls. Synthetic magnetization-prepared rapid gradient echo images of brain magnetic resonance imaging were generated using deep learning algorithms applied to T2-axial images. The cortical thickness, surface area, volume, and local gyrification index (LGI), as well as subcortical gray matter volumes, were automatically calculated. Surface-based morphometry parameters for the patient and control groups were compared using the general linear model, and volumes between subcortical structures were compared using the t-test and Mann-Whitney U test. In the patient group, cortical thinning was observed in the left supramarginal, and cortical thickening was observed in the left caudal middle frontal (CMF), left fusiform, left lateral orbitofrontal, left lingual gyrus, right CMF, right posterior cingulate, and right superior frontal (P < 0.050). The patient group showed a volume reduction in the pars triangularis, paracentral, precentral, and supramarginal gyri of the left hemisphere (P < 0.05). A decreased surface area was observed in the bilateral superior frontal and cingulate gyri (P < 0.05). The patient group exhibited a decreased LGI in the right precentral and superior temporal gyri, left supramarginal, and posterior cingulate gyri and showed an increased volume in the bilateral caudate nucleus and hippocampus, while a volume reduction was observed in the bilateral putamen, pallidum, and amygdala (P < 0.05). The ventricular volume and tumor volume showed a positive correlation with the cortical thickness in the bilateral CMF while demonstrating a negative correlation with areas exhibiting a decreased LGI (P < 0.05). Posterior fossa tumors lead to widespread morphological changes in cortical structures, with the most prominent pattern being hypogyria. This study illuminates the neurological impacts of infratentorial tumors in children, providing a foundation for future therapeutic strategies aimed at mitigating these adverse cortical and subcortical changes and improving patient outcomes.

Radiomics Based Differentiation of Glioblastoma and Metastatic Disease: Impact of Different T1-Contrast Enhanced Sequences on Radiomic Features and Model Performance.

To evaluate the radiomics-based model performance for differentiation between glioblastoma (GB) and brain metastases (BM) using magnetization prepared rapid gradient echo (MPRAGE) and volumetric interpolated breath-hold examination (VIBE) T1-contrast enhanced sequences. T1-CE MPRAGE and VIBE sequences acquired in 108 patients (31 GBs and 77 BM) during the same MRI session were retrospectively evaluated. Post standardized image pre-processing and segmentation, radiomics features were extracted from necrotic and enhancing tumor components. Pearson correlation analysis of radiomics features from tumor subcomponents was also performed. A total of 90 machine learning (ML) pipelines were evaluated using a five-fold cross validation. Performance was measured by mean AUC-ROC, Log-loss and Brier scores. A feature-wise comparison showed that the radiomic features between sequences were strongly correlated, with the highest correlation for shape-based features. The mean AUC across the top-ten pipelines ranged between 0.851-0.890 with T1-CE MPRAGE and between 0.869-0.907 with T1-CE VIBE sequence. Top performing models for the MPRAGE sequence commonly used support vector machines, while those for VIBE sequence used either support vector machines or random forest. Common feature reduction methods for top-performing models included linear combination filter and least absolute shrinkage and selection operator (LASSO) for both sequences. For the same ML-feature reduction pipeline, model performances were comparable (AUC-ROC difference range: [-0.078, 0.046]). Radiomic features derived from T1-CE MPRAGE and VIBE sequences are strongly correlated and may have similar overall classification performance for differentiating GB from BM. BM: Brain metastases, GB: glioblastoma, T1-CE: T1 contrast enhanced sequence, MPRAGE: magnetization prepared rapid gradient echo, ML: machine learning, RF: random forest, VIBE: volumetric interpolated breath-hold examination.

Hippocampal Glutamate Levels and Their Correlation With Subregion Volume in School-Aged Children With MRI-Negative Epilepsy: A Preliminary Study.

Abnormal levels of glutamate constitute a key pathophysiologic mechanism in epilepsy. The use of glutamate chemical exchange saturation transfer (GluCEST) imaging to measure glutamate levels in pediatric epilepsy is rarely reported in research. To investigate hippocampal glutamate level variations in pediatric epilepsy and the correlation between glutamate and hippocampal subregional volumes. Cross-sectional, prospective. A total of 38 school-aged pediatric epilepsy patients with structurally normal MRI as determined by at least two independent radiologists (60% males; 8.7 ± 2.5 years; including 20 cases of focal pediatric epilepsy [FE] and 18 cases of generalized pediatric epilepsy [GE]) and 17 healthy controls (HC) (41% males; 9.0 ± 2.5 years). 3.0 T; 3D magnetization prepared rapid gradient echo (MPRAGE) and 2D turbo spin echo GluCEST sequences. The relative concentration of glutamate was calculated through pixel-wise magnetization transfer ratio asymmetry (MTRasym) analysis of the GluCEST data. Hippocampal subfield volumes were computed from MPRAGE data using FreeSurfer. This study used t tests, one-way analysis of variance, Kruskal-Wallis tests, and Pearson correlation analysis. P < 0.05 was considered statistically significant. The MTRasym values of both the left and right hippocampi were significantly elevated in GE (left: 2.51 ± 0.23 [GE] vs. 2.31 ± 0.12 [HCs], right: 2.50 ± 0.22 [GE] vs. 2.27 ± 0.22 [HCs]). The MTRasym values of the ipsilateral hippocampus were significantly elevated in FE (2.49 ± 0.28 [ipsilateral] vs. 2.29 ± 0.16 [HCs]). The MTRasym values of the ipsilateral hippocampus were significantly increased compared to the contralateral hippocampus in FE (2.49 ± 0.28 [ipsilateral] vs. 2.35 ± 0.34 [contralateral]). No significant differences in hippocampal volume were found between different groups (left hippocampus, P = 0.87; right hippocampus, P = 0.87). GluCEST imaging have potential for the noninvasive measurement of glutamate levels in the brains of children with epilepsy. 2 TECHNICAL EFFICACY: Stage 1.

Brain and Ventricle Volume Alterations in Idiopathic Normal Pressure Hydrocephalus Determined by Artificial Intelligence-Based MRI Volumetry.

The aim of this study was to employ artificial intelligence (AI)-based magnetic resonance imaging (MRI) brain volumetry to potentially distinguish between idiopathic normal pressure hydrocephalus (iNPH), Alzheimer's disease (AD), and age- and sex-matched healthy controls (CG) by evaluating cortical, subcortical, and ventricular volumes. Additionally, correlations between the measured brain and ventricle volumes and two established semi-quantitative radiologic markers for iNPH were examined. An IRB-approved retrospective analysis was conducted on 123 age- and sex-matched subjects (41 iNPH, 41 AD, and 41 controls), with all of the iNPH patients undergoing routine clinical brain MRI prior to ventriculoperitoneal shunt implantation. Automated AI-based determination of different cortical and subcortical brain and ventricular volumes in mL, as well as calculation of population-based normalized percentiles according to an embedded database, was performed; the CE-certified software mdbrain v4.4.1 or above was used with a standardized T1-weighted 3D magnetization-prepared rapid gradient echo (MPRAGE) sequence. Measured brain volumes and percentiles were analyzed for between-group differences and correlated with semi-quantitative measurements of the Evans' index and corpus callosal angle: iNPH patients exhibited ventricular enlargement and changes in gray and white matter compared to AD patients and controls, with the most significant differences observed in total ventricular volume (+67%) and the lateral (+68%), third (+38%), and fourth (+31%) ventricles compared to controls. Global ventriculomegaly and marked white matter reduction with concomitant preservation of gray matter compared to AD and CG were characteristic of iNPH, whereas global and frontoparietally accentuated gray matter reductions were characteristic of AD. Evans' index and corpus callosal angle differed significantly between the three groups and moderately correlated with the lateral ventricular volumes in iNPH patients [Evans' index (r > 0.83, p ≤ 0.001), corpus callosal angle (r < -0.74, p ≤ 0.001)]. AI-based MRI volumetry in iNPH patients revealed global ventricular enlargement and focal brain atrophy, which, in contrast to healthy controls and AD patients, primarily involved the supratentorial white matter and was marked temporomesially and in the midbrain, while largely preserving gray matter. Integrating AI volumetry in conjunction with traditional radiologic measures could enhance iNPH identification and differentiation, potentially improving patient management and therapy response assessment.

MRI-Derived AD Signature of Cortical Thinning and Plasma P-Tau217 for Predicting Alzheimer Dementia Among Community-Dwelling Older Adults.

Structural brain MRI and blood-based phosphorylated tau (p-tau) measures are among the least invasive and least expensive Alzheimer's disease (AD) biomarkers to date. The extent to which these biomarkers may outperform one another in predicting future Alzheimer dementia diagnosis is poorly understood, however. This study investigated 2 specific AD biomarkers, i.e., a cortical thickness signature of AD (AD-CT) and plasma p-tau217, for predicting Alzheimer dementia. Data came from community-dwelling older participants of the Religious Orders Study or the Rush Memory and Aging Project. AD-CT was obtained from 3T MRI scans using a magnetization-prepared rapid acquisition gradient echo sequence and by averaging thickness from previously identified cortical regions implicated in AD. Plasma p-tau217 was quantified using an immunoassay developed by Lilly Research Laboratories on the MSD platform. Both MRI scans and blood specimens were collected at the same visits, and subsequent diagnoses of Alzheimer dementia were determined through annual detailed clinical evaluations. Cox proportional hazards models examined the associations of the 2 biomarkers with incident Alzheimer dementia, and prediction accuracy was assessed using c-statistics. A total of 198 older adults, on average 84 years of age, were included. Over a mean follow-up of 4 years, 60 (30%) individuals developed Alzheimer dementia. AD-CT (hazard ratio: 1.71, 95% CI 1.26-2.31) and separately plasma p-tau217 (hazard ratio: 2.57, 95% CI 1.83-3.61) were associated with incident Alzheimer dementia. The c-statistic for prediction accuracy was consistently higher for plasma p-tau217 (between 0.74 and 0.81) than AD-CT (between 0.70 and 0.75) across a range of time horizons. Furthermore, with both biomarkers included in the same model, there was only modest improvement in the c-statistic due to AD-CT. Plasma p-tau217 outperforms an imaging-based cortical thickness signature of AD in predicting future Alzheimer dementia diagnosis. Furthermore, the AD cortical thickness signature adds little to the prediction accuracy above and beyond plasma p-tau217.

Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function

Purpose: Motion-induced magnetic resonance imaging (MRI) artifacts can deteriorate image quality and reduce diagnostic accuracy, but motion by human subjects is inevitable and can even be caused by involuntary physiological movements. Deep-learning-based motion correction methods might provide a solution. However, most studies have been based on directly applying existing models, and the trained models are rarely accessible. Therefore, we aim to develop and evaluate a deep-learning-based method (Motion Correction-Net, or MC-Net) for suppressing motion artifacts in brain MRI scans. Methods: A total of 57 subjects, providing 20,889 slices in four datasets, were used. Furthermore, 3T 3D sagittal magnetization-prepared rapid gradient-echo (MP-RAGE) and 2D axial fluid-attenuated inversion-recovery (FLAIR) sequences were acquired. The MC-Net was derived from a UNet combined with a two-stage multi-loss function. T1-weighted axial brain images contaminated with synthetic motions were used to train the network to remove motion artifacts. Evaluation used simulated T1- and T2-weighted axial, coronal, and sagittal images unseen during training, as well as T1-weighted images with motion artifacts from real scans. The performance indices included the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), and visual reading scores from three blinded clinical readers. A one-sided Wilcoxon signed-rank test was used to compare reader scores, with p &lt; 0.05 considered significant. Intraclass correlation coefficients (ICCs) were calculated for inter-rater evaluations. Results: The MC-Net outperformed other methods in terms of PSNR and SSIM for the T1 axial test set. The MC-Net significantly improved the quality of all T1-weighted images for all directions (i.e., the mean SSIM of axial, sagittal, and coronal slices improved from 0.77, 0.64, and 0.71 to 0.92, 0.75, and 0.84; the mean PSNR improved from 26.35, 24.03, and 24.55 to 29.72, 24.40, and 25.37, respectively) and for simulated as well as real motion artifacts, both using quantitative measures and visual scores. However, MC-Net performed poorly for images with untrained T2-weighted contrast because the T2 contrast was unseen during training and is different from T1 contrast. Conclusion: The proposed two-stage multi-loss MC-Net can effectively suppress motion artifacts in brain MRI without compromising image quality. Given the efficiency of MC-Net (with a single-image processing time of ~40 ms), it can potentially be used in clinical settings.

Modern Magnetic Resonance Imaging Modalities to Advance Neuroimaging in Astronauts.

INTRODUCTION: The rapid development of the space industry requires a deeper understanding of spaceflight's impact on the brain. MRI research reports brain volume changes following spaceflight in astronauts, potentially affecting cognition. Recently, we have demonstrated that this evidence of volumetric changes, as measured by typical T1-weighted sequences (e.g., magnetization-prepared rapid gradient echo sequence; MPRAGE), is error-prone due to the microgravity-related redistribution of cerebrospinal fluid in the brain. More modern neuroimaging methods, particularly dual-echo MPRAGE (DEMPRAGE) and magnetization-prepared rapid gradient echo sequence utilizing two inversion pulses (MP2RAGE), have been suggested to be resilient to this error. Here, we tested if these imaging modalities offered consistent segmentation performance improvements in some commonly employed neuroimaging software packages.METHODS: We conducted manual gray matter tissue segmentation in traditional T1w MRI images to utilize for comparison. Automated tissue segmentation was performed for traditional T1w imaging, as well as on DEMPRAGE and MP2RAGE images from the same subjects. Statistical analysis involved a comparison of total gray matter volumes for each modality, and the extent of tissue segmentation agreement was assessed using a test of similarity (Dice coefficient).RESULTS: Neither DEMPRAGE nor MP2RAGE exhibited consistent segmentation performance across all toolboxes tested.DISCUSSION: This research indicates that customized data collection and processing methods are necessary for reliable and valid structural MRI segmentation in astronauts, as current methods provide erroneous classification and hence inaccurate claims of neuroplastic brain changes in the astronaut population.Berger L, Burles F, Jaswal T, Williams R, Iaria G. Modern magnetic resonance imaging modalities to advance neuroimaging in astronauts. Aerosp Med Hum Perform. 2024; 95(5):245-253.

Cortical Microhemorrhage Presentation of Small Vessel Primary Angiitis of the Central Nervous System.

Primary angiitis of the central nervous system (PACNS) is a rare vasculitis restricted to the brain, spinal cord, and leptomeninges. This study aimed to describe the imaging characteristics of patients with small vessel PACNS (SV-PACNS) using 7 T magnetic resonance imaging (MRI). This ongoing prospective observational cohort study included patients who met the Calabrese and Mallek criteria and underwent 7 T MRI scan. The MRI protocol includes T1-weighted magnetization-prepared rapid gradient echo imaging, T2 star weighted imaging, and susceptibility-weighted imaging. Two experienced readers independently reviewed the neuroimages. Clinical data were extracted from the electronic patient records. The findings were then applied to a cohort of patients with large vessel central nervous system (CNS) vasculitis. We included 21 patients with SV-PACNS from December 2021 to November 2023. Of these, 12 (57.14%) had cerebral cortical microhemorrhages with atrophy. The pattern with microhemorrhages was described in detail based on the gradient echo sequence, leading to the identification of what we have termed the "coral-like sign." The onset age of patients with coral-like sign (33.83 ± 9.93 years) appeared younger than that of patients without coral-like sign (42.11 ± 14.18 years) (P = 0.131). Furthermore, the cerebral lesions in patients with cortical microhemorrhagic SV-PACNS showed greater propensity toward bilateral lesions (P = 0.03). The coral-like sign was not observed in patients with large vessel CNS vasculitis. The key characteristics of the coral-like sign represent cerebral cortical diffuse microhemorrhages with atrophy, which may be an important MRI pattern of SV-PACNS. ANN NEUROL 2024;96:194-203.

Quantifying cerebral microbleeds using quantitative susceptibility mapping from magnetization-prepared rapid gradient-echo.

T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) is commonly included in brain studies for structural imaging using magnitude images; however, its phase images can provide an opportunity to assess microbleed burden using quantitative susceptibility mapping (QSM). This potential application for MPRAGE-based QSM was evaluated using in vivo and simulated measurements. Possible factors affecting image quality were also explored. Detection sensitivity was evaluated against standard multiecho gradient echo (MEGE) QSM using 3-T in vivo data of 15 subjects with a combined total of 108 confirmed microbleeds. The two methods were compared based on the microbleed size and susceptibility measurements. In addition, simulations explored the detection sensitivity of MPRAGE-QSM at different representative magnetic field strengths and echo times using microbleeds of different size, susceptibility, and location. Results showed that in vivo microbleeds appeared to be smaller (× 0.54) and of higher mean susceptibility (× 1.9) on MPRAGE-QSM than on MEGE-QSM, but total susceptibility estimates were in closer agreement (slope: 0.97, r2: 0.94), and detection sensitivity was comparable. In simulations, QSM at 1.5 T had a low contrast-to-noise ratio that obscured the detection of many microbleeds. Signal-to-noise ratio (SNR) levels at 3 T and above resulted in better contrast and increased detection. The detection rates for microbleeds of minimum one-voxel diameter and 0.4-ppm susceptibility were 0.55, 0.80, and 0.88 at SNR levels of 1.5, 3, and 7 T, respectively. Size and total susceptibility estimates were more consistent than mean susceptibility estimates, which showed size-dependent underestimation. MPRAGE-QSM provides an opportunity to detect and quantify the size and susceptibility of microbleeds of at least one-voxel diameter at B0of 3 T or higher with no additional time cost, when standard T2*-weighted images are not available or have inadequate spatial resolution. The total susceptibility measure is more robust against sequence variations and might allow combining data from different protocols.

Cerebrovascular Imaging at 7T: A New High

In this article, we will review the current techniques used in clinical cerebrovascular imaging, including time-of-flight MR Angiography (TOF-MRA), magnetization prepared rapid gradient echo (MPRAGE) MRA, phase-contrast MRA (PC-MRA), vessel wall MRI (VW-MRI) and MR venography (MRV). We will also discuss current problems and obstacles encountered at 7T vascular imaging.

Simultaneous Measurement of GABA, Glutathione, and Glutamate-Glutamine in the Thalamus using Edited MR Spectroscopy: Feasibility and Applications in Traumatic Brain Injury.

MR spectroscopy (MRS) is a noninvasive tool for evaluating biochemical alterations, such as glutamate (Glu)/gamma-aminobutyric acid (GABA) imbalance and depletion of antioxidative glutathione (GSH) after traumatic brain injury (TBI). Thalamus, a critical and vulnerable region post-TBI, is challenging for MRS acquisitions, necessitating optimization to simultaneously measure GABA/Glu and GSH. To assess the feasibility and optimize acquisition and processing approaches for simultaneously measuring GABA, Glx (Glu + glutamine (Gln)), and GSH in the thalamus, employing Hadamard encoding and reconstruction of MEscher-GArwood (MEGA)-edited spectroscopy (HERMES). Prospective. 28 control subjects (age: 35.9 ± 15.1 years), and 17 mild TBI (mTBI) patients (age: 32.4 ± 11.3 years). 3T/T1-weighted magnetization-prepared rapid gradient-echo (MP-RAGE), HERMES. We evaluated the impact of acquisition with spatial saturation bands and post-processing with spectral alignment on HERMES performance in the thalamus among controls. Within-subject variability was examined in five controls through repeated scans within a week. The HERMES spectra in the posterior cingulate cortex (PCC) of controls were used as a reference for assessing HERMES performance in a reliable target. Furthermore, we compared metabolite levels and fitting quality in the thalamus between mTBI patients and controls. Unpaired t-tests and within-subject coefficient-of-variation (CV). A P-value <0.05 was deemed significant. HERMES spectra, acquired with saturation bands and processed with spectral alignment, yielded reliable metabolite measurements in the thalamus. The mean within-subject CV for GABA, Glx, and GSH levels were 18%, 10%, and 16% in the thalamus (7%, 9%, and 16% in the PCC). GABA (3.20 ± 0.60 vs 2.51 ± 0.55, P < 0.01) and Glx (8.69 ± 1.23 vs 7.72 ± 1.19, P = 0.03) levels in the thalamus were significantly higher in mTBI patients than in controls, with GSH (1.27 ± 0.35 vs 1.22 ± 0.28, P = 0.65) levels showing no significant difference. Simultaneous measuring GABA/Glx and GSH using HERMES is feasible in the thalamus, providing valuable insight into TBI. 2 TECHNICAL EFFICACY: Stage 2.

Improving Vessel Visibility and Applying Artificial Intelligence to Autodetect Brain Metastasis for a 3D MR Imaging Sequence Capable of Simultaneous Images with and without Blood Vessel Suppression.

The purposes of this study were 1) to improve vessel visibility of our MR sequence by modifying k-space filling and 2) to verify the usefulness of applying artificial intelligence (AI) for volume isotropic simultaneous interleaved bright- and black-blood examination (VISIBLE) with compressed sensitivity encoding (CS) in autodetecting brain metastases. We modified 3 sequences of VISIBLE (Centric, Reversed Centric, and Startup Echo 30). The Centric sequence is a prototype. The Reversed Centric filled the k-space in a reversed centric manner to improve vessel visibility. The Startup Echo 30 implemented dummy echoes to further improve vessel visibility. Vessel visibility was evaluated in one slice at the level of the centrum semiovale. The sensitivity, specificity, the area under the curve (AUC), and false positives of detecting brain metastases using AI were evaluated among 3 sequences. Statistical comparisons were performed using a one-way analysis of variance, followed by Friedman and Dunn's multiple comparison tests. The number of visualized vessels was significantly lower in the Centric (39.3 ± 9.7, P < 0.05) and Reversed Centric (44.2 ± 9.8, P < 0.05) methods than in the magnetization-prepared rapid gradient echo (49.3 ± 9.1) but comparable in the Startup Echo 30 method (44.9 ± 8.8, P > 0.05). No significant differences existed in sensitivity, specificity, and AUC among the 3 methods. False positives achieved using the Reversed Centric method were significantly fewer (54 false positives) than those achieved using the Centric (85 false positives) and Startup Echo 30 (68 false positives) methods (P = 0.0092). Vessel visibility was improved by modifying the k-space filling, which may reduce false positives. The AI model for VISIBLE with CS achieved good performance in autodetection of brain metastases. The AI model for VISIBLE with CS can help radiologists diagnose brain metastases in clinical practice.

Hippocampal volume and recovery profile following propofol sedation in children undergoing magnetic resonance imaging: An exploratory study.

Reduction in the hippocampal volume may contribute to agitated and delayed emergence after anesthesia in epilepsy surgery. We hypothesized that hippocampal volume and the duration of various recovery parameters after a short duration of sedation may be correlated. The primary objective was to evaluate the correlation between hippocampal volumes with time to recovery after the stoppage of propofol infusion. After obtaining Institute Ethical Clearance, we included all children of the age group 5-17 years, who needed sedation for brain magnetic resonance imaging (MRI) for at least 20-60 minutes for the evaluation of epilepsy. The hippocampal volume was estimated bilaterally in the pre-contrast volumetric magnetization-prepared rapid gradient-echo (MPRAGE) brain imaging by the radiologist using statistical parametric mapping. The correlation of hippocampal volume with recovery and time to discharge (assessed by the modified Aldrete score (MAS)) was obtained using Spearman's correlation coefficient (rho). A rho > ± 0.5 was considered a good correlation between the variables. Data on a total of 18 children (10 males and 8 females) who required sedation for an MRI were studied over a period of six months. The correlation coefficients of right and left corrected hippocampal volumes with time to spontaneous eye opening were -0.052 and -0.195, respectively. The correlation coefficients of right and left corrected hippocampal volumes with time to respond to oral commands were -0.017 and -0.219, respectively. There was a weak negative correlation between hippocampal volumes and recovery parameters after a short duration of sedation with propofol in children.

Gray Matter Thickness and Subcortical Nuclear Volume in Men After SARS-CoV-2 Omicron Infection

The clinical manifestations and effects on the brain of the SARS-CoV-2 Omicron variant in the acute postinfection phase remain unclear. To investigate the pathophysiological mechanisms underlying clinical symptoms and changes to gray matter and subcortical nuclei among male patients after Omicron infection and to provide an imaging basis for early detection and intervention. In this cohort study, a total of 207 men underwent health screening magnetic resonance imaging scans between August 28 and September 18, 2022; among them, 98 provided complete imaging and neuropsychiatric data. Sixty-one participants with Omicron infection were reevaluated after infection (January 6 to 14, 2023). Neuropsychiatric data, clinical symptoms, and magnetic resonance imaging data were collected in the acute post-Omicron period, and their clinical symptoms were followed up after 3 months. Gray matter indexes and subcortical nuclear volumes were analyzed. Associations between changes in gray matter and neuropsychiatric data were evaluated with correlation analyses. Gray matter thickness and subcortical nuclear volume change data were compared before and after Omicron infection. The gray matter indexes and subcutaneous nuclear volume were generated from the 3-dimensional magnetization-prepared rapid acquisition gradient echo and were calculated with imaging software. Ninety-eight men underwent complete baseline data collection; of these, 61 (mean [SD] age, 43.1 [9.9] years) voluntarily enrolled in post-Omicron follow-up and 17 (mean [SD] age, 43.5 [10.0] years) voluntarily enrolled in 3-month follow-up. Compared with pre-Omicron measures, Beck Anxiety Inventory scores were significantly increased (median, 4.50 [IQR, 1.00-7.00] to 4.00 [IQR, 2.00-9.75]; P = .006) and depressive distress scores were significantly decreased (median, 18.00 [IQR, 16.00-20.22] to 16.00 [IQR, 15.00-19.00]; P = .003) at the acute post-Omicron follow-up. Fever, headache, fatigue, myalgia, cough, and dyspnea were the main symptoms during the post-Omicron follow-up; among the participants in the 3-month follow-up, fever (11 [64.7%] vs 2 [11.8%]; P = .01), myalgia (10 [58.8%] vs 3 (17.6%]; P = .04), and cough (12 [70.6%] vs 4 [23.5%]; P = .02) were significantly improved. The gray matter thickness in the left precuneus (mean [SD], 2.7 [0.3] to 2.6 [0.2] mm; P < .001) and right lateral occipital region (mean [SD], 2.8 [0.2] to 2.7 [0.2] and 2.5 [0.2] to 2.5 [0.2] mm; P < .001 for both) and the ratio of the right hippocampus volume to the total intracranial volume (mean [SD]. 0.003 [0.0003] to 0.003 [0.0002]; P = .04) were significantly reduced in the post-Omicron follow-up. The febrile group had reduced sulcus depth of the right inferior parietal region compared with the nonfebrile group (mean [SD], 3.9 [2.3] to 4.8 [1.1]; P = .048. In the post-Omicron period, the thickness of the left precuneus was negatively correlated with the Beck Anxiety Inventory scores (r = -0.39; P = .002; false discovery rate P = .02), and the ratio of the right hippocampus to the total intracranial volume was positively correlated with the Word Fluency Test scores (r = 0.34; P = .007). In this cohort study of male patients infected with the Omicron variant, the duration of symptoms in multiple systems after infection was short. Changes in gray matter thickness and subcortical nuclear volume injury were observed in the post-Omicron period. These findings provide new insights into the emotional and cognitive mechanisms of an Omicron infection, demonstrate its association with alterations to the nervous system, and verify an imaging basis for early detection and intervention of neurological sequelae.

MRI motion artifact reduction using a conditional diffusion probabilistic model (MAR-CDPM).

High-resolution magnetic resonance imaging (MRI) with excellent soft-tissue contrast is a valuable tool utilized for diagnosis and prognosis. However, MRI sequences with long acquisition time are susceptible to motion artifacts, which can adversely affect the accuracy of post-processingalgorithms. This study proposes a novel retrospective motion correction method named "motion artifact reduction using conditional diffusion probabilistic model" (MAR-CDPM). The MAR-CDPM aimed to remove motion artifacts from multicenter three-dimensional contrast-enhanced T1 magnetization-prepared rapid acquisition gradient echo (3D ceT1 MPRAGE) brain dataset with different brain tumor types. This study employed two publicly accessible MRI datasets: one containing 3D ceT1 MPRAGE and 2D T2-fluid attenuated inversion recovery (FLAIR) images from 230 patients with diverse brain tumors, and the other comprising 3D T1-weighted (T1W) MRI images of 148 healthy volunteers, which included real motion artifacts. The former was used to train and evaluate the model using the in silico data, and the latter was used to evaluate the model performance to remove real motion artifacts. A motion simulation was performed in k-space domain to generate an in silico dataset with minor, moderate, and heavy distortion levels. The diffusion process of the MAR-CDPM was then implemented in k-space to convert structure data into Gaussian noise by gradually increasing motion artifact levels. A conditional network with a Unet backbone was trained to reverse the diffusion process to convert the distorted images to structured data. The MAR-CDPM was trained in two scenarios: one conditioning on the time step of the diffusion process, and the other conditioning on both and T2-FLAIR images. The MAR-CDPM was quantitatively and qualitatively compared with supervised Unet, Unet conditioned on T2-FLAIR, CycleGAN, Pix2pix, and Pix2pix conditioned on T2-FLAIR models. To quantify the spatial distortions and the level of remaining motion artifacts after applying the models, quantitative metrics were reported including normalized mean squared error (NMSE), structural similarity index (SSIM), multiscale structural similarity index (MS-SSIM), peak signal-to-noise ratio (PSNR), visual information fidelity (VIF), and multiscale gradient magnitude similarity deviation (MS-GMSD). Tukey's Honestly Significant Difference multiple comparison test was employed to quantify the difference between the models where p-value was considered statisticallysignificant. Qualitatively, MAR-CDPM outperformed these methods in preserving soft-tissue contrast and different brain regions. It also successfully preserved tumor boundaries for heavy motion artifacts, like the supervised method. Our MAR-CDPM recovered motion-free in silico images with the highest PSNR and VIF for all distortion levels where the differences were statistically significant (p-values ). In addition, our method conditioned on t and T2-FLAIR outperformed (p-values ) other methods to remove motion artifacts from the in silico dataset in terms of NMSE, MS-SSIM, SSIM, and MS-GMSD. Moreover, our method conditioned on only t outperformed generative models (p-values ) and had comparable performances compared with the supervised model (p-values ) to remove real motionartifacts. The MAR-CDPM could successfully remove motion artifacts from 3D ceT1 MPRAGE. It is particularly beneficial for elderly who may experience involuntary movements during high-resolution MRI imaging with long acquisitiontimes.

Dark Blood Contrast-Enhanced Brain MRI Using Echo-uT1RESS.

The widely used magnetization-prepared rapid gradient-echo (MPRAGE) sequence makes enhancing lesions and blood vessels appear bright after gadolinium administration. However, dark blood imaging using T1-weighted Sampling Perfection with Application optimized Contrast using different flip angle Evolution (T1 SPACE) can be advantageous since it improves the conspicuity of small metastases and leptomeningeal disease. As a potential alternative to T1 SPACE, we evaluated a new dark blood sequence called echo-uT1RESS (unbalanced T1 Relaxation-Enhanced Steady-State). We compared the performance of echo-uT1RESS with Dixon fid-uT1RESS, MPRAGE, and T1 SPACE. Retrospective, IRB approved. Phantom to assess flow properties of echo-uT1RESS. Twenty-one patients (14 female, age range 35-82 years) with primary and secondary brain tumors. 3 Tesla/MPRAGE, T1 SPACE, Dixon fid-uT1RESS, echo-uT1RESS. Flow phantom signal vs. velocity as a function of flip angle and sequence. Qualitative image assessment on 4-point scale. Quantitative evaluation of tumor-to-brain contrast, apparent contrast-to-noise ratio (aCNR), and vessel-to-brain aCNR. Friedman and Mann-Whitney U tests. A P value <0.05 was considered statistically significant. In the phantom, echo-uT1RESS showed greater flow-dependent signal loss than fid-uT1RESS. In patients, blood vessels appeared bright with MPRAGE, gray with fid-uT1RESS, and dark with T1 SPACE and echo-uT1RESS. For MPRAGE, Dixon fid-uT1RESS, echo-uT1RESS, and T1 SPACE, respective tumor-to-brain contrast values were 0.6 ± 0.3, 1.3 ± 0.5, 1.0 ± 0.4, and 0.6 ± 0.4, while normalized aCNR values were 68.9 ± 50.9, 128.4 ± 59.2, 74.2 ± 42.1, and 99.4 ± 73.9. Volumetric dark blood contrast-enhanced brain MRI is feasible using echo-uT1RESS. The dark blood effect was improved vs. fid-uT1RESS, while both uT1RESS versions provided better tumor-to-brain contrast than MPRAGE. Whereas T1 SPACE provided better tumor aSNR, echo-uT1RESS provided better Weber contrast, lesion sharpness and a more consistent dark blood effect. 3 TECHNICAL EFFICACY: Stage 1.

Convolutional Neural Network for Fully Automated Cerebellar Volumetry in Children in Comparison to Manual Segmentation and Developmental Trajectory of Cerebellar Volumes

The purpose of this study was to develop a fully automated and reliable volumetry of the cerebellum of children during infancy and childhood using deep learning algorithms in comparison to manual segmentation. In addition, the clinical usefulness of measuring the cerebellar volume is shown. One hundred patients (0 to 16.3 years old) without infratentorial signal abnormalities on conventional MRI were retrospectively selected from our pool of pediatric MRI examinations. Based on a routinely acquired 3D T1-weighted magnetization prepared rapid gradient echo (MPRAGE) sequence, the cerebella were manually segmented using ITK-SNAP. The data set of all 100 cases was divided into four splits (four-fold cross-validation) to train the network (NN) to delineate the boundaries of the cerebellum. First, the accuracy of the newly created neural network was compared with the manual segmentation. Secondly, age-related volume changes were investigated. Our trained NN achieved an excellent Spearman correlation coefficient of 0.99, a Dice Coefficient of 95.0 ± 2.1%, and an intersection over union (IoU) of 90.6 ± 3.8%. Cerebellar volume increased continuously with age, showing an exponentially rapid growth within the first year of life. Using a convolutional neural network, it was possible to achieve reliable, fully automated cerebellar volume measurements in childhood and infancy, even when based on a relatively small cohort. In this preliminary study, age-dependent cerebellar volume changes could be acquired.