Search Results (24)

Objectives: To assess amide proton transfer weighted (APTw) MR imaging capabilities in differentiating high-grade glial tumors across alpha-thalassemia/mental retardation X-linked (ATRX) expression, tumor-suppressor protein p53 expression (p53), O6-methylguanine-DNA methyltransferase promoter (MGMTp) methylation, isocitrate dehydrogenase (IDH) status, and proliferation marker Ki-67 (Ki-67 index) as a preoperative diagnostic aid. Material & Methods: A total of 42 high-grade glioma WHO grade 4 (HGG) patients were evaluated prospectively (30 males and 12 females). All patients were examined using conventional MRI, including the following: T1w-MPRAGE pre- and post-contrast administration, conventional T2w and 3D FLAIR, and APTw imaging with a 3T MR scanner. Receiver operating characteristic (ROC) curves were calculated for the APTw% mean, median, and max signal for the different molecular biomarkers. A logistic regression model was constructed for combined mean and median APTw% signals for p53 expression. Results: The whole-tumor max APTw% signal could significantly differentiate MGMTp from non-MGMTp HGG, p = 0.035. A cutoff of 4.28% max APTw% signal yielded AUC (area under the curve) = 0.702, with 70.6% sensitivity and 66.7% specificity. The mean/median APTw% signals differed significantly in p53 normal versus p53-overexpressed HGG s: 1.81%/1.83% vs. 1.15%/1.18%, p = 0.002/0.006, respectively. Cutoffs of 1.25%/1.33% for the mean/median APTw% signals yielded AUCs of 0.786/0.757, sensitivities of 76.9%/76.9%, and specificities of 50%/66.2%, p = 0.002/0.006, respectively. A logistic regression model with a combined mean and median APTw% signal for p53 status yielded an AUC = 0.788 and 76.9% sensitivity and 66.2% specificity. ATRX-, IDH- wild type (wt) vs. mutation (mut), and the level of Ki-67 did not differ significantly, but trends were found: IDH-wt and low Ki-67 showed higher mean/median/max APTw% signals vs. IDH-mut and high Ki-67, respectively. ATRX-wt vs. mutation showed higher mean and median APTw% signals but lower max APTw% signal. Conclusions: APTw imaging can potentially be a useful marker for the stratification of p53 expression and MGMT status in high-grade glioma in the preoperative setting and potentially aid surgical decision-making. Full article

Chemotherapy-related cognitive impairment termed «chemobrain» is a prevalent complication in breast cancer survivors that requires early detection for the development of novel therapeutic approaches. Magnetic resonance voxel morphometry (MR morphometry), due to its high sensitivity, might be employed for the evaluation of the early changes in the volumes of brain structures in order to explore the «chemobrain» condition. Methods: The open, prospective, single-center study enrolled 86 breast cancer survivors (43.3 ± 4.4 years) and age-matched 28 healthy female volunteers (44.0 ± 5.68). Conventional MR sequences (T1- and T2-weighted, TIRM, DWI, MPRAGE) were obtained in three mutually perpendicular planes to exclude an organ pathology of the brain. Additionally, the MPRAGE sequence was performed for subsequent MR morphometry of the volume of brain structures using the open VolBrain program. The evaluation was performed at two follow-up visits 6 months and 3 years after the completion of BC treatment. Results: According to the MR morphometry, breast cancer survivors presented with significantly decreased volumes of brain structures (including total brain volume, cerebellum volume, subcortical gray matter, etc.) as compared to healthy volunteers. Evaluation over the follow-up period of 3 years did not show the restoration of brain volume structures. Conclusions: The data obtained employing MR morphometry revealed significant reductions (that were not detected on the conventional MR sequences) in both gray and white matter in breast cancer survivors following chemotherapy. This comprehensive analysis indicated the utility of MR morphometry in detecting subtle yet statistically significant neuroanatomical changes associated with cognitive and motor impairments in patients, which can in turn provide valuable insights into the extent of structural brain alterations, helping to identify specific regions that are most affected by treatment. Full article

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. Full article

Accurate brain extraction is crucial for the validity of MRI analyses, particularly in the context of traumatic brain injury (TBI), where conventional automated methods frequently fall short. This study investigates the interplay between intensity normalization, bias-field correction (also called intensity inhomogeneity correction), and automated brain extraction in MRIs of individuals with TBI. We analyzed 125 T1-weighted Magnetization-Prepared Rapid Gradient-Echo (T1-MPRAGE) and 72 T2-weighted Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRI sequences from a cohort of 143 patients with moderate to severe TBI. Our study combined 14 different intensity processing procedures, each using a configuration of N3 inhomogeneity correction, Z-score normalization, KDE-based normalization, or WhiteStripe intensity normalization, with 10 different configurations of the Brain Extraction Tool (BET) and the Optimized Brain Extraction Tool (optiBET). Our results demonstrate that optiBET with N3 inhomogeneity correction produces the most accurate brain extractions, specifically with one iteration of N3 for T1-MPRAGE and four iterations for T2-FLAIR, and pipelines incorporating N3 inhomogeneity correction significantly improved the accuracy of BET as well. Conversely, intensity normalization demonstrated a complex relationship with brain extraction, with effects varying by the normalization algorithm and BET parameter configuration combination. This study elucidates the interactions between intensity processing and the accuracy of brain extraction. Understanding these relationships is essential to the effective and efficient preprocessing of TBI MRI data, laying the groundwork for the development of robust preprocessing pipelines optimized for multi-site TBI MRI data. Full article

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 < 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. Full article

Background: MRI magnetization-prepared rapid acquisition (MPRAGE) is an easily available imaging modality for dementia diagnosis. Previous studies suggested that volumetric analysis plays a crucial role in various stages of dementia classification. In this study, volumetry, radiomics and demographics were integrated as inputs to develop an artificial intelligence model for various stages, including Alzheimer’s disease (AD), mild cognitive decline (MCI) and cognitive normal (CN) dementia classifications. Method: The Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset was separated into training and testing groups, and the Open Access Series of Imaging Studies (OASIS) dataset was used as the second testing group. The MRI MPRAGE image was reoriented via statistical parametric mapping (SPM12). Freesurfer was employed for brain segmentation, and 45 regional brain volumes were retrieved. The 3D Slicer software was employed for 107 radiomics feature extractions from within the whole brain. Data on patient demographics were collected from the datasets. The feed-forward neural network (FFNN) and the other most common artificial intelligence algorithms, including support vector machine (SVM), ensemble classifier (EC) and decision tree (DT), were used to build the models using various features. Results: The integration of brain regional volumes, radiomics and patient demographics attained the highest overall accuracy at 76.57% and 73.14% in ADNI and OASIS testing, respectively. The subclass accuracies in MCI, AD and CN were 78.29%, 89.71% and 85.14%, respectively, in ADNI testing, as well as 74.86%, 88% and 83.43% in OASIS testing. Balanced sensitivity and specificity were obtained for all subclass classifications in MCI, AD and CN. Conclusion: The FFNN yielded good overall accuracy for MCI, AD and CN categorization, with balanced subclass accuracy, sensitivity and specificity. The proposed FFNN model is simple, and it may support the triage of patients for further confirmation of the diagnosis. Full article

With the advancements in neuroimaging techniques, understanding the relationship between brain morphology and behavioral tendencies such as criminal behavior has garnered interest. This research addresses the investigation of disparities in neuroanatomical structures between adolescent offenders and non-offenders and considers the implications of such distinctions regarding offender behavior within adolescent populations. Employing data-driven methodologies, MRI scans of adolescents from Barranquilla, Colombia, were analyzed to explore morphological variations. Utilizing a 1.5 Tesla Siemens resonator (Siemens Healthineers, Erlangen, Germany), T1-weighted MPRAGE anatomical images were acquired and analyzed using a systematic five-step methodology including data acquisition, MRI pre-processing, feature selection, model selection, and model validation and evaluation. Participants, both offenders and non-offenders, were aged 14–18 and selected based on education, criminal history, and physical conditions. The research identified significant disparities in the volumes of 42 brain structures between adolescent offenders (AOs) and non-offenders (NOs), highlighting particular brain regions potentially associated with offending behavior. Additionally, a considerable proportion of AOs emanated from lower socioeconomic backgrounds and showcased marked substance use. The findings suggest that neuroanatomical disparities potentially correlate with criminal behavior among adolescents at a neurobiological level. Noticeable socio-environmental factors, such as lower socioeconomic status and substance abuse, were substantially prevalent among AOs. Particularly, neurobiological deviations in structures like ctx-lh-rostralmiddlefrontal and ctx-lh-caudalanteriorcingulate perhaps represent a link between neurological factors and external stimuli. Full article

A brain tumor in the left hemisphere can decrease language laterality as assessed through fMRI. However, it remains unclear whether or not this decreased language laterality is associated with a structural reshaping of the grey matter, particularly within the language network. Here, we examine if the disruption of the language hubs exclusively affects the macrostructural properties of the contralateral homologues or whether it affects both hemispheres. This study uses voxel-based morphometry applied to high-resolution MR T1-weighted MPRAGE images from 31 adult patients’ left hemisphere, which is dominant for language. Eighteen patients had brain tumors in the left hemisphere, and thirteen had tumors in the right hemisphere. A cohort of 71 healthy individuals matched with respect to age and sex was used as a baseline. We defined 10 ROIs per hemisphere involved in language function. Two separate repeated-measure ANOVAs were conducted with the volume per region as the dependent variable. For the patients, tumor lateralization (right versus left) served as a between-subject factor. The current study demonstrated that the presence of a brain tumor generates global volumetric changes affecting the left language regions and their contralateral homologues. These changes are mediated by the lateralization of the lesion. Our findings suggest that functional mechanisms are supported by the rearrangement of the grey matter. Full article

Cohort studies that quantify volumetric brain data among individuals with different levels of COVID-19 severity are presently limited. It is still uncertain whether there exists a potential correlation between disease severity and the effects of COVID-19 on brain integrity. Our objective was to assess the potential impact of COVID-19 on measured brain volume in patients with asymptomatic/mild and severe disease after recovery from infection, compared with healthy controls, using artificial intelligence (AI)-based MRI volumetry. A total of 155 participants were prospectively enrolled in this IRB-approved analysis of three cohorts with a mild course of COVID-19 (n = 51, MILD), a severe hospitalised course (n = 48, SEV), and healthy controls (n = 56, CTL) all undergoing a standardised MRI protocol of the brain. Automated AI-based determination of various brain volumes in mL and calculation of normalised percentiles of brain volume was performed with mdbrain software, using a 3D T1-weighted magnetisation-prepared rapid gradient echo (MPRAGE) sequence. The automatically measured brain volumes and percentiles were analysed for differences between groups. The estimated influence of COVID-19 and demographic/clinical variables on brain volume was determined using multivariate analysis. There were statistically significant differences in measured brain volumes and percentiles of various brain regions among groups, even after the exclusion of patients undergoing intensive care, with significant volume reductions in COVID-19 patients, which increased with disease severity (SEV > MILD > CTL) and mainly affected the supratentorial grey matter, frontal and parietal lobes, and right thalamus. Severe COVID-19 infection, in addition to established demographic parameters such as age and sex, was a significant predictor of brain volume loss upon multivariate analysis. In conclusion, neocortical brain degeneration was detected in patients who had recovered from SARS-CoV-2 infection compared to healthy controls, worsening with greater initial COVID-19 severity and mainly affecting the fronto-parietal brain and right thalamus, regardless of ICU treatment. This suggests a direct link between COVID-19 infection and subsequent brain atrophy, which may have major implications for clinical management and future cognitive rehabilitation strategies. Full article

(1) Background: Cerebral venous and dural sinus thrombosis (CVT) rarely appears in the adult population. It is difficult to diagnosis because of its variable clinical presentation and the overlapping signal intensities of thrombosis and venous flow on conventional MR images and MR venograms. (2) Case presentation: A 41-year-old male patient presented with an acute isolated intracranial hypertension syndrome. The diagnosis of acute thrombosis of the left lateral sinus (both transverse and sigmoid portions), the torcular Herophili, and the bulb of the left internal jugular vein was established by neuroimaging data from head-computed tomography, magnetic resonance imaging (including Contrast-enhanced 3D T1-MPRAGE sequence), and magnetic resonance venography (2D-TOF MR venography). We detected different risk factors (polycythemia vera-PV with JAK2 V617F mutation and inherited low-risk thrombophilia). He was successfully treated with low-molecular-weight heparin, followed by oral anticoagulation. (3) Conclusions: In the case of our patient, polycythemia vera represented a predisposing risk factor for CVT, and the identification of JAK2 V617F mutation was mandatory for the etiology of the disease. Contrast-enhanced 3D T1-MPRAGE sequence proved superior to 2D-TOF MR venography and to conventional SE MR imaging in the diagnosis of acute intracranial dural sinus thrombosis. Full article

A recently introduced model-based deep learning (MoDL) technique successfully incorporates convolutional neural network (CNN)-based regularizers into physics-based parallel imaging reconstruction using a small number of network parameters. Wave-controlled aliasing in parallel imaging (CAIPI) is an emerging parallel imaging method that accelerates imaging acquisition by employing sinusoidal gradients in the phase- and slice/partition-encoding directions during the readout to take better advantage of 3D coil sensitivity profiles. We propose wave-encoded MoDL (wave-MoDL) combining the wave-encoding strategy with unrolled network constraints for highly accelerated 3D imaging while enforcing data consistency. We extend wave-MoDL to reconstruct multicontrast data with CAIPI sampling patterns to leverage similarity between multiple images to improve the reconstruction quality. We further exploit this to enable rapid quantitative imaging using an interleaved look-locker acquisition sequence with T₂ preparation pulse (3D-QALAS). Wave-MoDL enables a 40 s MPRAGE acquisition at 1 mm resolution at 16-fold acceleration. For quantitative imaging, wave-MoDL permits a 1:50 min acquisition for T₁, T₂, and proton density mapping at 1 mm resolution at 12-fold acceleration, from which contrast-weighted images can be synthesized as well. In conclusion, wave-MoDL allows rapid MR acquisition and high-fidelity image reconstruction and may facilitate clinical and neuroscientific applications by incorporating unrolled neural networks into wave-CAIPI reconstruction. Full article

Purpose: Differentiation between tumor recurrence and treatment-related contrast enhancement in MRI can be difficult. Late enhancement MRI up to 75 min after contrast agent application has been shown to improve differentiation between tumor recurrence and treatment-related changes. We investigated the diagnostic performance of late enhancement using a rapid MRI protocol optimized for clinical workflow. Methods: Twenty-three patients with 28 lesions suspected for glioma recurrence underwent MRI including T1-MPRAGE-series acquired 2 and 20 min after contrast agent administration. Early contrast series were subtracted from late contrast series using motion correction. Contrast enhancing lesions were retrospectively and independently evaluated by two readers blinded to the patients’ later clinical course and histology with or without the use of late enhancement series. Sensitivity, specificity, NPV, and PPV were calculated for both readers by comparing results of MRI with histological samples. Results: Using standard MR sequences, sensitivity, specificity, PPV, and NPV were 0.84, 0, 0.875, and 0 (reader 1) and 0.92, 0, 0.885, and 0 (reader 2), respectively. Early late enhancement increased sensitivity, specificity, PPV, and NPV to 1 for each value and for both readers. Inter-reader reliability increased from 0.632 (standard MRI sequences) to 1.0 (with early late enhancement). Conclusion: The described rapid late enhancement MRI protocol improves MRI-based discrimination between tumor tissue and treatment-related changes of the brain parenchyma. Full article

Alzheimer’s Disease (AD) is the most common cause of dementia. Due to the progressive nature of the neurodegeneration associated with the disease, it is of clinical interest to achieve an early diagnosis of AD. In this study, we analyzed the viability of asymmetry-related measures as potential biomarkers to facilitate the early diagnosis of AD. These measures were obtained from MAPER-segmented MP-RAGE MRI studies available at the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and by analyzing these studies at the level of individual segmented regions. The temporal evolution of these measures was obtained and then analyzed by generating spline regression models. Data imputation was performed where missing information prevented the temporal analysis of each measure from being realized, using additional information provided by ADNI for each patient. The temporal evolution of these measures was compared to the evolution of other commonly used markers for the diagnosis of AD, such as cognitive function, concentrations of Phosphorylated-Tau, Amyloid-β, and structural MRI volumetry. The results of the regression models showed that asymmetry measures, in particular regions such as the parahippocampal gyrus, differentiated themselves temporally before most of the other evaluated biomarkers. Further studies are suggested to corroborate these results. Full article

This study uses magnetic resonance imaging (MRI) data to propose end-to-end learning implementing volumetric convolutional neural network (CNN) models for two binary classification tasks: Alzheimer’s disease (AD) vs. cognitively normal (CN) and stable mild cognitive impairment (sMCI) vs. AD. The baseline MP-RAGE T1 MR images of 245 AD patients and 229 with sMCI were obtained from the ADNI dataset, whereas 245 T1 MR images of CN people were obtained from the IXI dataset. All of the images were preprocessed in four steps: N4 bias field correction, denoising, brain extraction, and registration. End-to-end-learning-based deep CNNs were used to discern between different phases of AD. Eight CNN-based architectures were implemented and assessed. The DenseNet264 excelled in both types of classification, with 82.5% accuracy and 87.63% AUC for training and 81.03% accuracy for testing relating to the sMCI vs. AD and 100% accuracy and 100% AUC for training and 99.56% accuracy for testing relating to the AD vs. CN. Deep learning approaches based on CNN and end-to-end learning offer a strong tool for examining minute but complex properties in MR images which could aid in the early detection and prediction of Alzheimer’s disease in clinical settings. Full article

Oxidative stress has been implied in cellular injury even in the early phases of multiple sclerosis (MS). In this study, we quantified levels of biomarkers of oxidative stress and antioxidant capacity in cerebrospinal fluid (CSF) in newly diagnosed MS patients and their associations with brain atrophy and iron deposits in the brain tissue. Consecutive treatment-naive adult MS patients (n = 103) underwent brain MRI and CSF sampling. Healthy controls (HC, n = 99) had brain MRI. CSF controls (n = 45) consisted of patients with non-neuroinflammatory conditions. 3T MR included isotropic T1 weighted (MPRAGE) and gradient echo (GRE) images that were processed to quantitative susceptibility maps. The volume and magnetic susceptibility of deep gray matter (DGM) structures were calculated. The levels of 8-hydroxy-2′-deoxyguanosine (8-OHdG), 8-iso prostaglandin F2α (8-isoPG), neutrophil gelatinase-associated lipocalin (NGAL), peroxiredoxin-2 (PRDX2), and malondialdehyde and hydroxyalkenals (MDA + HAE) were measured in CSF. Compared to controls, MS patients had lower volumes of thalamus, pulvinar, and putamen, higher susceptibility in caudate nucleus and globus pallidus, and higher levels of 8-OHdG, PRDX2, and MDA + HAE. In MS patients, the level of NGAL correlated negatively with volume and susceptibility in the dentate nucleus. The level of 8-OHdG correlated negatively with susceptibility in the caudate, putamen, and the red nucleus. The level of PRDX2 correlated negatively with the volume of the thalamus and both with volume and susceptibility of the dentate nucleus. From MRI parameters with significant differences between MS and HC groups, only caudate susceptibility and thalamic volume were significantly associated with CSF parameters. Our study shows that increased oxidative stress in CSF detected in newly diagnosed MS patients suggests its role in the pathogenesis of MS. Full article