Magnetization-prepared Rapid Gradient Echo Research Articles

Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI.

Gadolinium-enhanced "sampling perfection with application-optimized contrasts using different flip angle evolution" (SPACE) sequence allows better visualization of brain metastases (BMs) compared to "magnetization-prepared rapid acquisition gradient echo" (MPRAGE). We hypothesize that this better conspicuity leads to high-quality annotation (HAQ), enhancing deep learning (DL) algorithm detection of BMs on MPRAGE images. Retrospective contrast-enhanced (gadobutrol 0.1 mmol/kg) SPACE and MPRAGE data of 157 patients with BM were used, either annotated on MPRAGE resulting in normal annotation quality (NAQ) or on coregistered SPACE resulting in HAQ. Multiple DL methods were developed with NAQ or HAQ using either SPACE or MRPAGE images and evaluated on their detection performance using positive predictive value (PPV), sensitivity, and F1 score and on their delineation performance using volumetric Dice similarity coefficient, PPV, and sensitivity on one internal and four additional test datasets (660 patients). The SPACE-HAQ model reached 0.978 PPV, 0.882 sensitivity, and 0.916 F1-score. The MPRAGE-HAQ reached 0.867, 0.839, and 0.840, the MPRAGE NAQ 0.964, 0.667, and 0.798, respectively (p ≥ 0.157). Relative to MPRAGE-NAQ, the MPRAGE-HAQ F1-score detection increased on all additional test datasets by 2.5-9.6 points (p < 0.016) and sensitivity improved on three datasets by 4.6-8.5 points (p < 0.001). Moreover, volumetric instance sensitivity improved by 3.6-7.6 points (p < 0.001). HAQ improves DL methods without specialized imaging during application time. HAQ alone achieves about 40% of the performance improvements seen with SPACE images as input, allowing for fast and accurate, fully automated detection of small (< 1 cm) BMs. Training with higher-quality annotations, created using the SPACE sequence, improves the detection and delineation sensitivity of DL methods for the detection of brain metastases (BMs)on MPRAGE images. This MRI cross-technique transfer learning is a promising way to increase diagnostic performance. Delineating small BMs on SPACE MRI sequence results in higher quality annotations than on MPRAGE sequence due to enhanced conspicuity. Leveraging cross-technique ground truth annotations during training improved the accuracy of DL models in detecting and segmenting BMs. Cross-technique annotation may enhance DL models by integrating benefits from specialized, time-intensive MRI sequences while not relying on them. Further validation in prospective studies is needed.

Cross-Sectional Validation of an Automated Lesion Segmentation Software in Multiple Sclerosis: Comparison with Radiologist Assessments.

Magnetic Resonance Imaging is widely used to assess disease burden in multiple sclerosis (MS). This study aimed to evaluate the effectiveness of a commercially available k-nearest neighbors (k-NN) software in quantifying white matter lesion (WML) burden in MS. We compared the software's WML quantification to expert radiologists' assessments. We retrospectively reviewed brain MRI examinations of adult MS patients and of adult patients without MS and with a normal brain MRI referred from the neurology clinic. MRI images were processed using an AI-powered, cloud-based k-NN software, which generated a DICOM lesion distribution map and a report of WML count and volume in four brain regions (periventricular, deep, juxtacortical, and infratentorial white matter). Two blinded radiologists performed semi-quantitative assessments of WM lesion load and lesion segmentation accuracy. Additionally, four blinded neuroradiologists independently reviewed the data to determine if MRI findings supported an MS diagnosis. Results were considered significant when p < 0.05. The study included 32 MS patients (35.4 years ± 9.1) and 19 patients without MS (33.5 years ± 12.1). The k-NN software demonstrated 94.1% and 84.3% accuracy in differentiating MS from non-MS subjects based respectively on WML count and WML volume, compared to radiologists' accuracy of 90.2% to 94.1%. Lesion segmentation was more accurate for the deep WM and infratentorial regions than for the juxtacortical region (both p <0.001). k-NN-derived WML volume and WML count provide valuable quantitative metrics of disease burden in MS. AI-powered post-processing software may enhance the interpretation of brain MRIs in MS patientsABBREVIATIONS: MS = multiple sclerosis; k-NN＝k-Nearest Neighbors; WML＝white matter lesion; MPRAGE = Magnetization-Prepared Rapid Acquisition Gradient Echo; SPACE = Sampling Perfection with Application-optimized Contrasts using a Different Flip Angle Evolution; EDSS = Expanded Disability Status Scale.

Generation of high-resolution MPRAGE-like images from 3D head MRI localizer (AutoAlign Head) images using a deep learning-based model.

Magnetization prepared rapid gradient echo (MPRAGE) is a useful three-dimensional (3D) T1-weighted sequence, but is not a priority in routine brain examinations. We hypothesized that converting 3D MRI localizer (AutoAlign Head) images to MPRAGE-like images with deep learning (DL) would be beneficial for diagnosing and researching dementia and neurodegenerative diseases. We aimed to establish and evaluate a DL-based model for generating MPRAGE-like images from MRI localizers. Brain MRI examinations including MPRAGE taken at a single institution for investigation of mild cognitive impairment, dementia and epilepsy between January 2020 and December 2022 were included retrospectively. Images taken in 2020 or 2021 were assigned to training and validation datasets, and images from 2022 were used for the test dataset. Using the training and validation set, we determined one model using visual evaluation by radiologists with reference to image quality metrics of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). The test dataset was evaluated by visual assessment and quality metrics. Voxel-based morphometric analysis was also performed, and we evaluated Dice score and volume differences between generated and original images of major structures were calculated as absolute symmetrized percent change. Training, validation, and test datasets comprised 340 patients (mean age, 56.1 ± 24.4years; 195 women), 36 patients (67.3 ± 18.3years, 20 women), and 193 patients (59.5 ± 24.4years; 111 women), respectively. The test dataset showed: PSNR, 35.4 ± 4.91; SSIM, 0.871 ± 0.058; and LPIPS 0.045 ± 0.017. No overfitting was observed. Dice scores for the segmentation of main structures ranged from 0.788 (left amygdala) to 0.926 (left ventricle). Quadratic weighted Cohen kappa values of visual score for medial temporal lobe between original and generated images were 0.80-0.88. Images generated using our DL-based model can be used for post-processing and visual evaluation of medial temporal lobe atrophy.

Associations of Central Arterial Stiffness With Brain White Matter Integrity and Gray Matter Volume in MRI Across the Adult Lifespan.

Central arterial stiffening is associated with brain white matter (WM) damage and gray matter (GM) volume loss in older adults, but little is known about this association from an adult lifespan perspective. To investigate the associations of central arterial stiffness with WM microstructural organization, WM lesion load, cortical thickness, and GM volume in healthy adults across the lifespan. This is a cross-sectional study. A total of 173 healthy adults (22-81 years) were included in this study. 3-T, T1-weighted magnetization prepared rapid gradient echo (MPRAGE), single-shot echo-planar imaging diffusion-weighted, and T2-weighted fluid-attenuated inversion recovery (FLAIR) sequences. The participants underwent measurements of central arterial stiffness using carotid-femoral pulse wave velocity (cfPWV), diffusion tensor imaging (DTI) to measure whole-brain WM microstructural organization with free water (FW) and FW-corrected fractional anisotropy (FACOR), FLAIR to measure whole-brain WM hyperintensities (WMH), and MPRAGE to measure whole-brain cortical thickness and GM volume. The associations of age and cfPWV with MRI measures were assessed. Linear regression models to examine the associations of brain WM and GM measures with age, cfPWV, and age × cfPWV interaction after adjusting for sex, education, and intracranial volume (ICV) (voxel-wise and cluster threshold P < 0.05). To understand the direction of the interaction result, the sample was stratified into lower and higher cfPWV groups using a median split of cfPWV. Age × cfPWV interactions were observed in WM FW, WMH volume, cortical thickness, and GM volume (P < 0.01) such that the positive regression slopes between age, FW, and WMH volume were higher, while the negative regression slopes between age, cortical thickness, and GM volume were lower in those who had higher cfPWV relative to those who had lower cfPWV. Central arterial stiffening may accelerate the age-related deteriorations in GM and WM structure across the adult lifespan. Central arterial stiffening is associated with brain white matter (WM) damage and gray matter (GM) volume loss in older adults. We extended this investigation into an adult lifespan perspective by examining the associations of central arterial stiffening with brain structure in adults across age. A total of 172 healthy adults (22-81 years) underwent central arterial stiffening measure using applanation tonometry and brain measurement using MRI. We observed that higher central arterial stiffening may accelerate the age-related deterioration in brain WM and GM structure. These results suggest the importance of maintaining vascular health to slow age-related brain structural changes from an adult lifespan perspective. 4 TECHNICAL EFFICACY: Stage 5.

Imaging breakthroughs in dementia: Pioneering 3D T1-weighted MPRAGE vs. routine spin echo with a focus on Alzheimer's disease

Background. Dementia, a spectrum of neurocognitive disorders, leads to progressive cognitive and functional decline, primarily affecting memory and executive functions. Among the most common causes is Alzheimer's disease (AD), which accounts for 60-80% of dementia cases. Neuroimaging, particularly Magnetic Resonance Imaging (MRI), plays a critical role in diagnosing dementia, helping differentiate between various subtypes such as Alzheimer's disease, vascular dementia (VaD), frontotemporal dementia (FTD), and Lewy body dementia (LBD). This study explores the utility of 3D T1-weighted Magnetization Prepared Rapid Gradient Echo (MPRAGE) and conventional spin echo sequences in distinguishing dementia subtypes, with a focus on Alzheimer's disease. Methods. This cross-sectional study included 40 elderly subjects diagnosed with cognitive impairment or dementia. Participants were assessed using 3D T1 MPRAGE sequences to calculate Medial Temporal Lobe Atrophy (MTA) indices and Entorhinal Cortex and Related Areas (ERICA) scores, alongside conventional spin echo sequences. MRI served as the primary imaging modality to differentiate between Alzheimer's disease, vascular dementia, and other dementia subtypes. Statistical analyses included descriptive statistics, inferential tests, and Receiver Operating Characteristic (ROC) curves to evaluate diagnostic accuracy. Results. The average MTA score for Alzheimer's disease was 3.33, significantly higher than that for vascular dementia (2.73), frontotemporal dementia (2.33), and Lewy body dementia (0.6). The mean ERICA score for Alzheimer's disease was 3.0, higher than FTD (0.83), VaD (0.55), and LBD (0.0). Lewy body dementia exhibited significantly larger hippocampal volumes than other dementia subtypes. The ROC curve for MTA in predicting Alzheimer's disease yielded an area under the curve (AUC) of 0.833, with a sensitivity of 98.7% and an overall diagnostic accuracy of 80%. The ERICA score achieved an AUC of 1.0, demonstrating perfect diagnostic accuracy for Alzheimer's disease. Conclusion. The study underscores the superior diagnostic accuracy of 3D T1 MPRAGE in calculating MTA and ERICA scores, enhancing early detection and differentiation of Alzheimer's disease from other dementia subtypes. The integration of these advanced imaging techniques provides a powerful diagnostic tool, ensuring more reliable and early diagnosis in clinical practice. This research highlights the potential of 3D T1 MPRAGE in revolutionizing dementia diagnostics, paving the way for improved patient outcomes.

Diagnostic Confidence of Contrast-Enhanced T1-Weighted MRI for the Detection of Brain Metastases: 3D FSE-vs. 3D GRE-Based Sequences.

This retrospective study evaluated the utility of contrast-enhanced (CE) T1-weighted 3D fast spinecho-based SPACE sequences for brain metastasis detection on 3T MRI compared to gradient recalled-echo-based 3D fast low-angle shot (FLASH) sequence. We identified all patients at a single institution who underwent SPACE and 3D FLASH sequences as part of a practice quality improvement project. Their medical records were retrospectively reviewed. Five certified neuroradiologists reviewed the images, with at least 2 weeks separation between scoring sequences for the same patient. The following parameters were evaluated: number of metastatic lesions, number of indeterminate lesions, lesion margin, contrast-to-noise ratio (CNR), extent of image artifacts, and overall image quality. CNR was also quantified for solidly enhancing lesions > 1 cm. We identified 220 patients who underwent SPACE and 3D FLASH sequences (the order of the sequences was equally distributed). Of these, 79 had brain metastases on imaging, and 7 were excluded; thus, 72 patients were included in the study. Twenty patients were scored by 2 radiologists. Out of the 92 evaluations, SPACE detected more lesions than did 3D FLASH in 35, while 3D FLASH detected more lesions in 10. More indeterminate lesions were seen on 3D FLASH (27) than on SPACE (9). For lesion margin, CNR, and overall image quality on a Likert scale, SPACE performed significantly better than did 3D FLASH, with less image artifacts (P < 0.00001). Higher quantitative CNRs were found on SPACE than on 3D FLASH images, although this result was not statistically significant (median = 22.9 vs. 15.5, respectively, P = 0.134). There was a high inter-reader lesion detection concordance with Krippendorf's alpha ordinals at 0.962 for SPACE, 0.870 for 3D FLASH, and 0.918 for the two sequences combined. Compared with 3D FLASH, the SPACE sequence detected more metastatic lesions and was rated higher for image quality, lesion margin, and CNR, with fewer artifacts. Importantly, the SPACE sequence resulted in increased reader confidence, with fewer indeterminate lesions detected. FLASH = fast low-angle shot; FSE = fast spin-echo; GRE = gradient-recalled echo; MP-RAGE = magnetization-prepared rapid gradient echo; SPACE = Sampling Perfection with Application-optimized Contrasts using different flip angle Evolution; VIBE = volumetric interpolated breath-hold examination.

Role of Vessel Wall Magnetic Resonance Imaging in the Diagnosis of Intracranial Vasculopathies.

Stroke is a leading cause of death and disability worldwide, affecting millions annually. Accurate etiological diagnosis is critical for the effective treatment and prevention of recurrent strokes. Traditional luminal imaging techniques like computed tomography (CT) and magnetic resonance angiography (MRA) provide limited information, focusing solely on vessel lumen characteristics. Vessel wall magnetic resonance imaging (VW-MRI) has emerged as a valuable non-invasive technique for evaluating intracranial vasculopathies with high spatial resolution. It also helps in identifying plaque composition and distinguishing between lipid-rich, fibrous, calcified, and thrombotic components. This information is crucial for assessing plaque vulnerability and predicting the risk of future cardiovascular events. This study aimed to evaluate the findings of VW-MRI among cases having suspicious stenosis detected on MRA and to characterize atherosclerotic plaque vulnerability features on VW-MRI. This cross-sectional hospital-based study was conducted at the Department of Radiodiagnosis, Sawai Man Singh (SMS) Medical College and Hospital, Jaipur, on 45 patients who met the inclusion criteria. Participants who showed suspicious stenosis on conventional MRA underwent VW-MRI using a 3T GE Signa Architect 64-channel MRI machine (GE HealthCare, Chicago, IL, USA) to obtain vessel wall MR sequences: 3D Ax time-of-flight (TOF) fat-suppressed spoiled gradient recalled echo (SPGR) Fs, 3D Sag T2 Cube, 3D Sag T1 black-blood (BB) Cube Fs, 3D Sag T1 magnetization-prepared rapid gradient-echo(MP-RAGE), and 3D Sag T1 BB Cube Fs +C. These sequences will be taken to differentiate between intracranial atherosclerotic plaque, vasculitis, and moyamoya disease/pattern and also to characterize atherosclerotic plaque vulnerability features. Data were collected through a structured questionnaire and magnetic resonance imaging (MRI) results, followed by statistical analysis. The majority (50%) of patients were middle-aged, with a mean age of 48 years. Atherosclerosis was the most common (67%) diagnosis, followed by central nervous system (CNS) vasculitis (22%) and moyamoya disease (11%). VW-MRI detected significant VW abnormalities in all cases as compared to MRA which detected only 73.35% of cases. Positive remodeling was associated with CNS vasculitisand vulnerable plaques, while moyamoya disease was linked to negative remodeling. VW-MRI proves superior to traditional luminal imaging for diagnosing intracranial vasculopathies. It enhances the understanding of VW abnormalities, aiding in the management of stroke and other cerebrovascular diseases.

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.

Brain morphometrics correlations with age among 352 participants imaged with both 3T and 7T MRI: 7T improves statistical power and reduces required sample size.

Magnetic resonance imaging (MRI) at 7 Telsa (7T) has superior signal-to-noise ratio to 3 Telsa (3T) but also presents higher signal inhomogeneities and geometric distortions. A key knowledge gap is to robustly investigate the sensitivity and accuracy of 3T and 7T MRI in assessing brain morphometrics. This study aims to (a) aggregate a large number of paired 3T and 7T scans to evaluate their differences in quantitative brain morphological assessment using a widely available brain segmentation tool, FreeSurfer, as well as to (b) examine the impact of normalization methods for subject variability and smaller sample sizes on data analysis. A total of 452 healthy participants aged 29 to 68 were imaged at both 3T and 7T. Structural T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) images were processed and segmented using FreeSurfer. To account for head size variability, the brain volumes underwent intracranial volume (ICV) correction using the Residual (regression model) and Proportional (simple division to ICV) methods. The resulting volumes and thicknesses were correlated with age using Pearson correlation and false discovery rate correction. The correlations were also calculated in increasing sample size from 3 to the whole sample to estimate the sample size required to detect aging-related brain variation. 352 subjects (210 females) passed the image quality control with 100 subjects excluded due to excessive motion artifacts on 3T, 7T, or both. 7T MRI showed an overall stronger correlation between morphometrics and age and a larger number of significantly correlated brain volumes and cortical thicknesses. While the ICV is consistent between both field strengths, the Residual normalization method shows markedly higher correlation with age for 3T when compared with the Proportional normalization method. The 7T results are consistent regardless of the normalization method used. In a large cohort of healthy participants with paired 3T and 7T scans, we compared the statistical performance in assessing age-related brain morphological changes. Our study reaffirmed the inverse correlation between brain volumes and cortical thicknesses and age and highlighted varying correlations in different brain regions and normalization methods at 3T and 7T. 7T imaging significantly improves statistical power and thus reduces required sample size.

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.

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.