Fluid-attenuated Inversion Recovery Research Articles

Concurrent spontaneous coronary dissection and reversible cerebral vasoconstriction syndrome during postnatal care.

Pregnancy-associated spontaneous coronary artery dissection (SCAD) and reversible cerebral vasoconstriction syndrome (RCVS) are rare conditions that may occur during pregnancy and the postpartum period. The coexistence of both diseases may pose a risk to patients, potentially resulting in a variety of complications and clinical manifestations. This is considered the first case of a patient who successfully recovered from a critical condition in the postpartum period, with contemporaneous SCAD and RCVS. A 33-year-old female with no known medical history was referred to the emergency department after experiencing cardiac arrest, which occurred 1 week after giving birth to her third child. She complained of sudden anterior squeezing chest pain, accompanied by a headache, and eventually collapsed due to ventricular fibrillation with seizure. She was successfully resuscitated after receiving basic life support. She showed an alert mentality and did not experience any further seizure events or additional neurological symptoms. Although vital sign remained stable, the level of highly sensitive troponin I was significantly elevated. Electrocardiography revealed sinus rhythm with T-wave inversion at V1-4, while chest computed tomography (CT) demonstrated severe aspiration pneumonia. The patient was admitted to the intensive care unit due to a high requirement of O2 supply. A consultation with the neurologic department and a brain magnetic resonance angiography (MRA) were conducted for the thunderclap headache. The brain MRA demonstrated stenosis in the basilar artery, the right M2 segment, and bilateral A1 segments, along with sulcal hyperintensity on post-contrast fluid-attenuated inversion recovery (FLAIR) suggesting blood-brain barrier breakdown due to vasoconstriction. Formal echocardiography showed regional wall motion abnormality in the left anterior descending artery (LAD) territory. After the improvement of pneumonia, a coronary angiography was performed, revealing diffuse luminal narrowing from the mid to distal LAD due to a long segmental, extensive dissection. We decided to maintain the medical therapy. A follow-up coronary CT angiography performed 6months later revealed complete remission of the dissected coronary vessel, and a brain MRA checked 3months later showed resolved vasoconstriction of the relevant cerebral vessels. The physicians must be aware of pregnancy-associated complications in certain patients. Clear diagnoses and proper treatments are required in pregnant patients who may be exposed to multiple acute conditions, in order to reduce complications and achieve favorable outcomes.

Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning.

Identifying active lesions in magnetic resonance imaging (MRI) is crucial for the diagnosis and treatment planning of multiple sclerosis (MS). Active lesions on MRI are identified following the administration of Gadolinium-based contrast agents (GBCAs). However, recent studies have reported that repeated administration of GBCA results in the accumulation of Gd in tissues. In addition, GBCA administration increases health care costs. Thus, reducing or eliminating GBCA administration for active lesion detection is important for improved patient safety and reduced healthcare costs. Current state-of-the-art methods for identifying active lesions in brain MRI without GBCA administration utilize data-intensive deep learning methods. To implement nonlinear dimensionality reduction (NLDR) methods, locally linear embedding (LLE) and isometric feature mapping (Isomap), which are less data-intensive, for automatically identifying active lesions on brain MRI in MS patients, without the administration of contrast agents. Fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images were included in the multiparametric MRI dataset used in this study. Subtracted pre- and post-contrast T1-weighted images were labeled by experts as active lesions (ground truth). Unsupervised methods, LLE and Isomap, were used to reconstruct multiparametric brain MR images into a single embedded image. Active lesions were identified on the embedded images and compared with ground truth lesions. The performance of NLDR methods was evaluated by calculating the Dice similarity (DS) index between the observed and identified active lesions in embedded images. LLE and Isomap, were applied to 40 MS patients, achieving median DS scores of 0.74 ± 0.1 and 0.78 ± 0.09, respectively, outperforming current state-of-the-art methods. NLDR methods, Isomap and LLE, are viable options for the identification of active MS lesions on non-contrast images, and potentially could be used as a clinical decision tool.

Experimental Ischemic Stroke Induces Secondary Bihemispheric White Matter Degeneration and Long-Term Cognitive Impairment.

Clinical studies have identified widespread white matter degeneration in ischemic stroke patients. However, contemporary research in stroke has predominately focused on the infarct and periinfarct penumbra regions. The involvement of white matter degeneration after ischemic stroke and its contribution to post-stroke cognitive impairment and dementia (PSCID) has remained less explored in experimental models. In this study, we examined the progression of locomotor and cognitive function up to 4 months after inducing ischemic stroke by middle cerebral artery occlusion in young adult rats. Despite evident ongoing locomotor recovery, long-term cognitive and affective impairments persisted after ischemic stroke, as indicated by Morris water maze, elevated plus maze, and open field performance. At 4 months after stroke, multimodal MRI was conducted to assess white matter degeneration. T2-weighted MRI (T2WI) unveiled bilateral cerebroventricular enlargement after ischemic stroke. Fluid Attenuated Inversion Recovery MRI (FLAIR) revealed white matter hyperintensities in the corpus callosum and fornix across bilateral hemispheres. A positive association between the volume of white matter hyperintensities and total cerebroventricular volume was noted in stroke rats. Further evidence of bilateral white matter degeneration was indicated by the reduction of fractional anisotropy and quantitative anisotropy at bilateral corpus callosum in diffusion-weighted MRI (DWI) analysis. Additionally, microglia and astrocyte activation were identified in the bilateral corpus callosum after stroke. Our study suggests that experimental ischemic stroke induced by MCAO in young rat replicate long-term cognitive impairment and bihemispheric white matter degeneration observed in ischemic stroke patients. This model provides an invaluable tool for unraveling the mechanisms underlying post-stroke secondary white matter degeneration and its contribution to PSCID.

Halka Şekilli Lateral Ventrikül Nodüllerinin Konvansiyonel ve Diffüzyon MR Görüntüleme Özellikleri

Objective: The aim of this study is to evaluate the conventional and diffusion MRI findings of ring-shaped lateral ventricular nodules (RSLVN) along with clinical features.
 Methods: MR images of all patients who underwent contrast-enhanced brain MRI between 2019 and 2023 were retrospectively evaluated. The number, shape, maximal diameter, and signal intensity of RSLVNs on T1-weighted (T1W), T2-weighted (T2W), fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging (DWI), and contrast-enhancement status were evaluated. Apparent diffusion coefficient (ADC) values and normalized ADC ratios of nodules were also determined. If follow-up MRIs were performed, morphological changes of RSLVNs were evaluated. 
 Results: RSLVN was observed in fifteen (0.51%) of 2920 patients. Multiple RSLVNs were observed in five patients and therefore a total of 23 RSLVNs were identified in fifteen patients. Nodules were located on the roof of the lateral ventricle in eight nodules (34.8%), in the frontal horn in twelve nodules (52.2%), and in the septum pellucidum in three. 6 of 23 RSLVNs (26.1%) were larger than 1 cm. All RSLVNs were isointense on T1W and T2W, while hyperintense on FLAIR. On DWI, 20 of 23 RSLVNs had isointense signal and the remaining 3 lesions were hyperintense. The mean ADC value and nADC ratio were 1.42 ± 0.29 x 10-3mm2 and 1.87 ± 0.31, respectively.
 Conclusion: RSLVNs may be more frequent than previously reported. Their uniform MRI appearance and typical localizations are distinctive, and they can reach relatively large sizes. Morphological stability during follow-up and the ADC values of these lesions suggest a possible benign nature.

Association between renal insufficiency and lesion characteristics of posterior reversible encephalopathy syndrome.

Posterior reversible encephalopathy syndrome (PRES) is characterized by cerebral blood flow dysregulation and the blood-brain barrier (BBB) disruption. While renal insufficiency has been considered a factor in BBB fragility, the relationship between renal insufficiency and the PRES lesions volume remains unclear. This observational study was performed retrospectively. PRES patients were categorized into two groups with renal insufficiency, defined as an estimated glomerular filtration rate (eGFR) of less than 60mL/min/1.73m2 on the day of symptom occurrence. Lesion volume was measured using fluid-attenuated inversion recovery (FLAIR) imaging, and the brain was divided into nine regions. The volume of the parietal-occipital-temporal lobe was considered typical, while the other six regions were labeled as atypical. The study included 200 patients, of whom 94 (47%) had renal insufficiency. Patients with renal insufficiency had a larger lesion volume (144.7 ± 125.2cc) compared to those without renal insufficiency (110.5 ± 93.2cc; p = 0.032); particularly in the atypical lesions volume (49.2 ± 65.0 vs. 29.2 ± 44.3cc; p = 0.013). However, there was no difference in the reversibility of the lesions (35.2 ± 67.5 vs. 18.8 ± 33.4cc; p = 0.129). Multiple regression analysis revealed that decreases in eGFR (β = -0.34, 95% CI -0.62-0.05, p = 0.020) were positively associated with total lesion volume. Our findings suggest that PRES patients with renal insufficiency experience more severe lesion volumes, likely due to the atypical brain regions involvement. The lesions involving atypical regions may have a similar pathophysiology to typical lesions, as the PRES lesions reversibility was found to be similar between individuals with and without renal insufficiency.

Machine-Learning and Radiomics-Based Preoperative Prediction of Ki-67 Expression in Glioma Using MRI Data

BackgroundGliomas are the most common primary brain tumours and constitute approximately half of all malignant glioblastomas. Unfortunately, patients diagnosed with malignant glioblastomas typically survive for less than a year. In light of this circumstance, genotyping is an effective means of categorizing gliomas. The Ki67 proliferation index, a widely used marker of cellular proliferation in clinical contexts, has demonstrated potential for predicting tumour classification and prognosis. In particular, magnetic resonance imaging (MRI) plays a vital role in the diagnosis of brain tumours. Using MRI to extract glioma-related features and construct a machine learning model offers a viable avenue to classify and predict the level of Ki67 expression. MethodsThis study retrospectively collected MRI data and postoperative immunohistochemical results from 613 glioma patients from the …… Hospital. Subsequently, we performed registration and skull stripping on the four MRI modalities: T1-weighted (T1), T2-weighted (T2), T1-weighted with contrast enhancement (T1CE), and Fluid Attenuated Inversion Recovery (FLAIR). Each modality's segmentation yielded three distinct tumour regions. Following segmentation, a comprehensive set of features encompassing texture, first-order, and shape attributes were extracted from these delineated regions. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) algorithm with subsequent sorting to identify the most important features. These selected features were further analysed using correlation analysis to finalise the selection for machine learning model development. Eight models: logistic regression (LR), naive bayes, decision tree, gradient boosting tree, and support vector classification (SVM), random forest (RF), XGBoost, and LightGBM were used to objectively classify Ki67 expression. ResultsIn total, 613 patients were enrolled in the study, and 24,455 radiomic features were extracted from each patient’s MRI. These features were eventually reduced to 36 after LASSO screening, RF importance ranking, and correlation analysis. Among all the tested machine learning models, LR and linear SVM exhibited superior performance. LR achieved the highest area under the curve score of 0.912 ± 0.036, while linear SVM obtained the top accuracy with a score of 0.884 ± 0.031. ConclusionsThis study introduced a novel approach for classifying Ki67 expression levels using MRI, which has been proven to be highly effective. With the LR model at its core, our method demonstrated its potential in signalling a promising avenue for future research. This innovative approach of predicting Ki67 expression based on MRI features not only enhances our understanding of cell activity but also represents a significant leap forward in brain glioma research. This underscores the potential of integrating machine learning with medical imaging to aid in the diagnosis and prognosis of complex diseases.

Association between brain amyloid deposition and longitudinal changes of white matter hyperintensities

BackgroundGrowing evidence suggests that not only cerebrovascular disease but also Alzheimer’s disease (AD) pathological process itself cause cerebral white matter degeneration, resulting in white matter hyperintensities (WMHs). Some preclinical evidence also indicates that white matter degeneration may precede or affect the development of AD pathology. This study aimed to clarify the direction of influence between in vivo AD pathologies, particularly beta-amyloid (Aβ) and tau deposition, and WMHs through longitudinal approach.MethodsTotal 282 older adults including cognitively normal and cognitively impaired individuals were recruited from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease (KBASE) cohort. The participants underwent comprehensive clinical and neuropsychological assessment, [11C] Pittsburgh Compound B PET for measuring Aβ deposition, [18F] AV-1451 PET for measuring tau deposition, and MRI scans with fluid-attenuated inversion recovery image for measuring WMH volume. The relationships between Aβ or tau deposition and WMH volume were examined using multiple linear regression analysis. In this analysis, baseline Aβ or tau were used as independent variables, and change of WMH volume over 2 years was used as dependent variable to examine the effect of AD pathology on increase of WMH volume. Additionally, we set baseline WMH volume as independent variable and longitudinal change of Aβ or tau deposition for 2 years as dependent variables to investigate whether WMH volume could precede AD pathologies.ResultsBaseline Aβ deposition, but not tau deposition, had significant positive association with longitudinal change of WMH volume over 2 years. Baseline WMH volume was not related with any of longitudinal change of Aβ or tau deposition for 2 years. We also found a significant interaction effect between baseline Aβ deposition and sex on longitudinal change of WMH volume. Subsequent subgroup analyses showed that high baseline Aβ deposition was associated with increase of WMH volume over 2 years in female, but not in male.ConclusionsOur findings suggest that Aβ deposition accelerates cerebral WMHs, particularly in female, whereas white matter degeneration appears not influence on longitudinal Aβ increase. The results also did not support any direction of influence between tau deposition and WMHs.

Content-aware Nakagami morphing for incremental brain MRI

Within the carcinogenesis mechanism, from the initiation of the very first tumor cell to the preneoplastic and neoplastic cancer cell groups, cancer cells omnidirectionally and unpredictably proliferate in three-dimensional (3D) space during the promotion and progression steps. Whole tumors areas, consisting of edema, necrosis, enhancing and non-enhancing tumor subareas, could easily be identified by the typical sequences of magnetic resonance imaging (MRI), mostly by FLAIR, the fluid attenuated inversion recovery sequence. Depending on the increment value of the scanner, not every scan could be contiguous though, which might create some gaps between the slices and cause some crucial loss of information. Shape-based morphing by erosion and dilation operations might be inadequate when the source or target lesions are not found on the MRI slices, which might emerge due to unpredictable shape of tumors. Furthermore, the real trajectories and genuine shapes of the tumors in spatial view might not be seen in the planar MRI slices due to approximations during the voxel-to-pixel transformations executed in MRI devices. Therefore, we propose a novel content-aware morphing procedure to generate imaginary slices by m-parametric Nakagami imaging. The mathematical and parametric design in our framework not only leads to precise segmentation of the whole tumors according to the ground truth images; but also, to realistic morphing, based on the content of tumor footprints in MRI slices. For testing purposes, a total number of 5133 morphs are created using 1831 genuine images containing at least one lesion taken from the Brats 2012 dataset. Given the average dice score coefficients (DSC), we obtained an average of 88.44% DSC with our morphing framework outperforming the conventional dilation-based morphing result which is 83.25%. Besides being a novel morphing methodology, the outcomes of this research could also be very beneficial in smoother 3D reconstruction and more realistic volumetric calculations of lesions to help the experts working on assessment of lesions.

Hand knob sign in osmotic demyelinating syndrome

BackgroundOsmotic demyelinating syndrome, commonly recognized as a consequence of the rapid correction of hyponatremia, has been known to cause motor, neuropsychiatric, or extrapyramidal symptoms. We reported a patient with an unusual presentation involving bilateral hand weakness, and pseudobulbar affect. The imaging was compatible with osmotic demyelinating syndrome with bilateral hand knob lesions, despite no history of overcorrection of hyponatremia.Case presentationA 44-year-old female presented with three weeks of emotional lability, spastic dysarthria, and bilateral hand weakness following ankle surgery and a mild head injury. Physical examination revealed weakness in the intrinsic hand muscles, leading to a claw-like deformity of the hands, although sensation remained unimpaired. Magnetic resonance imaging (MRI) of the brain revealed several hyperintensities on fluid-attenuated inversion recovery imaging involving various areas, including the hand knob area of the bilateral precentral gyri, caudate, lentiform nuclei, and pons, suggestive of osmotic demyelinating syndrome. Clinical improvement was observed following a trial of intravenous pulse methylprednisolone and plasmapheresis.ConclusionsBilateral hand weakness is an unusual manifestation of osmotic demyelinating syndrome. The precentral gyrus, specifically in the hand knob area, is the vulnerable region that can result from osmotic demyelinating syndrome.

Subtle white matter intensity changes on fluid-attenuated inversion recovery imaging in patients with ischaemic stroke.

Leukoaraiosis is a neuroimaging marker of small-vessel disease that is characterized by high signal intensity on fluid-attenuated inversion recovery MRI. There is increasing evidence from pathology and neuroimaging suggesting that the structural abnormalities that characterize leukoaraiosis are actually present within regions of normal-appearing white matter, and that the underlying pathophysiology of white matter damage related to small-vessel disease involves blood-brain barrier damage. In this study, we aim to verify whether leukoaraiosis is associated with elevated signal intensity on fluid-attenuated inversion recovery imaging, a marker of brain tissue free-water accumulation, in normal-appearing white matter. We performed a cross-sectional study of adult patients admitted to our hospital with a diagnosis of acute ischaemic stroke or transient ischaemic attack. Leukoaraiosis was segmented using a semi-automated method involving manual outlining and signal thresholding. White matter regions were segmented based on the probabilistic tissue maps from the International Consortium for Brain Mapping 152 atlas. Also, normal-appearing white matter was further segmented based on voxel distance from leukoaraiosis borders, resulting in five normal-appearing white matter strata at increasing voxel distances from leukoaraiosis. The relationship between mean normalized fluid-attenuated inversion recovery signal intensity on normal-appearing white matter and leukoaraiosis volume was studied in a multivariable statistical analysis using linear mixed modelling, having normal-appearing white matter strata as a clustering variable. One hundred consecutive patients meeting inclusion and exclusion criteria were selected for analysis (53% female, mean age 68 years). Mean normalized fluid-attenuated inversion recovery signal intensity on normal-appearing white matter was higher in the vicinity of leukoaraiosis and progressively lower at increasing distances from leukoaraiosis. In a multivariable analysis, the mean normalized fluid-attenuated inversion recovery signal intensity on normal-appearing white matter was positively associated with leukoaraiosis volume and age (B = 0.025 for each leukoaraiosis quartile increase; 95% confidence interval 0.019-0.030). This association was found similarly across normal-appearing white matter strata. Voxel maps of the mean normalized fluid-attenuated inversion recovery signal intensity on normal-appearing white matter showed an increase in signal intensity that was not adjacent to leukoaraiosis regions. Our results show that normal-appearing white matter exhibits subtle signal intensity changes on fluid-attenuated inversion recovery imaging that are related to leukoaraiosis burden. These results suggest that diffuse free-water accumulation is likely related to the aetiopathogenic processes underlying the development of white matter damage related to small-vessel disease.

Joint Brain Tumor Segmentation from Multi-magnetic Resonance Sequences through a Deep Convolutional Neural Network.

Brain tumor segmentation is highly contributive in diagnosing and treatment planning. Manual brain tumor delineation is a time-consuming and tedious task and varies depending on the radiologist's skill. Automated brain tumor segmentation is of high importance and does not depend on either inter- or intra-observation. The objective of this study is to automate the delineation of brain tumors from the Fluid-attenuated inversion recovery (FLAIR), T1-weighted (T1W), T2-weighted (T2W), and T1W contrast-enhanced (T1ce) magnetic resonance (MR) sequences through a deep learning approach, with a focus on determining which MR sequence alone or which combination thereof would lead to the highest accuracy therein. The BraTS-2020 challenge dataset, containing 370 subjects with four MR sequences and manually delineated tumor masks, is applied to train a residual neural network. This network is trained and assessed separately for each one of the MR sequences (single-channel input) and any combination thereof (dual- or multi-channel input). The quantitative assessment of the single-channel models reveals that the FLAIR sequence would yield higher segmentation accuracy compared to its counterparts with a 0.77 ± 0.10 Dice index. As to considering the dual-channel models, the model with FLAIR and T2W inputs yields a 0.80 ± 0.10 Dice index, exhibiting higher performance. The joint tumor segmentation on the entire four MR sequences yields the highest overall segmentation accuracy with a 0.82 ± 0.09 Dice index. The FLAIR MR sequence is considered the best choice for tumor segmentation on a single MR sequence, while the joint segmentation on the entire four MR sequences would yield higher tumor delineation accuracy.

Amide Proton Transfer-Weighted MRI, Associations with Clinical Severity and Prognosis in Ischemic Strokes.

The National Institutes of Health Stroke Scale (NIHSS) and the modified Rankin scale (mRS) scores have important shortcomings. Amide proton transfer-weighted (APTw) imaging might offer more valuable information in ischemic strokesassessment. To utilize APTw, apparent diffusion coefficient (ADC), and computed tomography perfusion (CTP) for the assessment of clinical symptom severity and 90-day prognosis in patients diagnosed with ischemic stroke. Prospective. 61 patients (mean age 63.2 ± 9.7 years; 46 males, 15 females) with ischemic strokes were included in the study. 3T/turbo spin echo (TSE) T1-weighted imaging, T2-weighted imaging, T2-fluid attenuated inversion recovery (T2-FLAIR), diffusion-weighted imaging (DWI), and single-shot TSE APTw imaging. APTw, ADC, and CTP were used to compare patient subgroups and construct a prognostic nomogram model. Kolmogorov-Smirnov test, t-test, Mann-Whitney U test, chi-square test, Pearson correlation analysis, multivariate logistic regression analysis, decision curve analysis (DCA), receiver operating characteristic curves (ROCs). The significance threshold was set at P < 0.05. Correlation analysis revealed that APTw and NIHSS exhibit the highest correlation (r = -0.634, 95% confidence interval [CI] -0.418 to -0.782), surpassing that of ADC and lesion size. Multivariable analysis revealed APTw (odds ratio [OR] 0.905, 95% CI 0.845-0.970), ADC (OR 0.745, 95% CI 0.609-0.911), and infarct core-cerebral blood volume (IC-CBV) (OR 0.547, 95% CI 0.310-0.964) as potential risk factors associated with a poor prognosis. The nomogram model demonstrated the highest predictive efficacy, with an area under the curve(AUC) of 0.960 (95% CI 0.911-0.988), exceeding that of APTw, ADC, and IC-CBV individually. The APTw technique holds potential value in categorizing and managing patients with ischemic stroke, offering guidance for the implementation of clinical treatment strategies. 1 TECHNICAL EFFICACY: Stage 2.

Deep learning convolutional neural network ResNet101 and radiomic features accurately analyzes mpMRI imaging to predict MGMT promoter methylation status with transfer learning approach

AbstractAccurate brain tumor classification is crucial for enhancing the diagnosis, prognosis, and treatment of glioblastoma patients. We employed the ResNet101 deep learning method with transfer learning to analyze the 2021 Radiological Society of North America (RSNA) Brain Tumor challenge dataset. This dataset comprises four structural magnetic resonance imaging (MRI) sequences: fluid‐attenuated inversion‐recovery (FLAIR), T1‐weighted pre‐contrast (T1w), T1‐weighted post‐contrast (T1Gd), and T2‐weighted (T2). We assessed the model's performance using standard evaluation metrics. The highest performance to detect MGMT methylation status for patients suffering glioblastoma was an accuracy (85.48%), sensitivity (80.64%), specificity (90.32%). Whereas classification performance with no tumor was yielded with accuracy (85.48%), sensitivity (90.32%), specificity (80.64%). The radiomic features (74) computed with ensembled Bagged Tree and relief feature selection method (30/74) improved the validation accuracy of 84.3% and AUC of 0.9038 to detect. O6‐methylguanine‐DNA methyltransferase (MGMT) promoter methylation status in glioblastoma patients holds promise for optimizing treatment planning and prognosis. By understanding MGMT methylation status, clinicians can make informed decisions about treatment strategies, potentially leading to improved clinical outcomes.

Patient-Specific Three-Dimensional-Printed and Bolus Material-Based Localization Grids for Magnetic Resonance Imaging-Guided Interventional Procedures

Abstract In view of the lack of commercially available localization grids for magnetic resonance imaging (MRI)-guided interventional procedures, two customizable and easily fabricable grids are proposed. The first one is a patient-specific three-dimensional-printed localization grid that incorporates MRI markers while the second one is a grid constructed with Superflex Transparent Bolus material. MRI scans were performed with the grids attached on an abdominal phantom. The patient-specific three-dimensional-printed grid is visible in T1-weighted, T2-weighted, proton density (PD) and fluid attenuated inversion recovery MR images, whereas the Superflex grid is visible only in T1-weighted and PD images. However, the Superflex grid offers the advantage of a simpler fabrication process and is more cost-effective. Both proposed localization grids can facilitate the determination of the optimal needle entry positions for MRI-guided interventional procedures, leading to reduced overall procedure time and improved efficiency.

Diagnostic Utility of Contrast-Enhanced Fluid-Attenuated Inversion Recovery Magnetic Resonance Imaging in Intracranial Pathologies: Qualitative and Quantitative Analyses

Diagnostic Utility of Contrast-Enhanced Fluid-Attenuated Inversion Recovery Magnetic Resonance Imaging in Intracranial Pathologies: Qualitative and Quantitative Analyses

Chronic Active Lesions and Larger Choroid Plexus Explain Cognition and Fatigue in Multiple Sclerosis.

Chronic inflammation may contribute to cognitive dysfunction and fatigue in patients with multiple sclerosis (MS). Paramagnetic rim lesions (PRLs) and choroid plexus (CP) enlargement have been proposed as markers of chronic inflammation in MS being associated with a more severe disease course. However, their relation with cognitive impairment and fatigue has not been fully explored yet. Here, we investigated the contribution of PRL number and volume and CP enlargement to cognitive impairment and fatigue in patients with MS. Brain 3T MRI, neurologic evaluation, and neuropsychological assessment, including the Brief Repeatable Battery of Neuropsychological Tests and Modified Fatigue Impact Scale, were obtained from 129 patients with MS and 73 age-matched and sex-matched healthy controls (HC). PRLs were identified on phase images of susceptibility-weighted imaging, whereas CP volume was quantified using a fully automatic method on brain three-dimensional T1-weighted and fluid-attenuated inversion recovery MRI sequences. Predictors of cognitive impairment and fatigue were identified using random forest. Thirty-six (27.9%) patients with MS were cognitively impaired, and 31/113 (27.4%) patients had fatigue. Fifty-nine (45.7%) patients with MS had ≥1 PRLs (median = 0, interquartile range = 0;2). Compared with HC, patients with MS showed significantly higher T2-hyperintense white matter lesion (WM) volume; lower normalized brain, thalamic, hippocampal, caudate, cortical, and WM volumes; and higher normalized CP volume (p from <0.001 to 0.040). The predictors of cognitive impairment (relative importance) (out-of-bag area under the curve [OOB-AUC] = 0.707) were normalized brain volume (100%), normalized caudate volume (89.1%), normalized CP volume (80.3%), normalized cortical volume (70.3%), number (67.3%) and volume (66.7%) of PRLs, and T2-hyperintense WM lesion volume (64.0%). Normalized CP volume was the only predictor of the presence of fatigue (OOB-AUC = 0.563). Chronic inflammation, with higher number and volume of PRLs and enlarged CP, may contribute to cognitive impairment in MS in addition to gray matter atrophy. The contribution of enlarged CP in explaining fatigue supports the relevance of immune-related processes in determining this manifestation independently of disease severity. PRLs and CP enlargement may contribute to the pathophysiology of cognitive impairment and fatigue in MS, and they may represent clinically relevant therapeutic targets to limit the impact of these clinical manifestations in MS.

Three-dimensional identification of microvascular pathology and neurovascular inflammation in severe white matter hyperintensity: a case report

White matter hyperintensities (WMH) are the most prevalent markers of cerebral small vessel disease (SVD), which is the major vascular risk factor for dementia. Microvascular pathology and neuroinflammation are suggested to drive the transition from normal-appearing white matter (NAWM) to WMH, particularly in individuals with hypertension. However, current imaging techniques cannot capture ongoing NAWM changes. The transition from NAWM into WMH is a continuous process, yet white matter lesions are often examined dichotomously, which may explain their underlying heterogeneity. Therefore, we examined microvascular and neurovascular inflammation pathology in NAWM and severe WMH three-dimensionally, along with gradual magnetic resonance imaging (MRI) fluid-attenuated inversion recovery (FLAIR) signal (sub-)segmentation. In WMH, the vascular network exhibited reduced length and complexity compared to NAWM. Neuroinflammation was more severe in WMH. Vascular inflammation was more pronounced in NAWM, suggesting its potential significance in converting NAWM into WMH. Moreover, the (sub-)segmentation of FLAIR signal displayed varying degrees of vascular pathology, particularly within WMH regions. These findings highlight the intricate interplay between microvascular pathology and neuroinflammation in the transition from NAWM to WMH. Further examination of neurovascular inflammation across MRI-visible alterations could aid deepening our understanding on WMH conversion, and therewith how to improve the prognosis of SVD.

Combination of Contrast-enhanced FlAIR and Contrast-enhanced T1WI: A Quick and Efficient Method in Detecting Brain Metastases of Lung Cancers.

Some patients with suspected brain metastases (BM) could not tolerate longer scanning examinations according to the standardized MRI protocol. The purpose of this study was to evaluate the clinical value of contrast-enhanced fast fluid-attenuated inversion recovery (CE FLAIR) imaging in combination with contrast-enhanced T1 weighted imaging (CE T1WI) in detecting BM of lung cancer and explore a quick and effective MRI protocol. In 201 patients with lung cancers and suspected BM, T1WI and FLAIR were performed before and after administration of gadopentetate dimeglumine. Two radiologists reviewed pre- and post-contrast images to determine the presence of abnormal contrast enhancement or signal intensity and decided whether it was metastatic or not on CE T1WI (Group 1) and CE FLAIR (Group 2). The number, locations and features of abnormal findings in two groups were recorded. Receiver Operating Characteristic (ROC) analyses were conducted in three groups: Group 1, 2 and 3(combination of CE FLAIR and CE T1WI). A total of 714 abnormal findings were revealed, of which 672 were considered as BM and 42 nonmetastatic. Superficial and small metastases(≤10mm) in parenchyma and ependyma, leptomeningeal and non-expansive skull metastases were typically better seen on CE FLAIR. The areas under ROC in the three groups were 0.720,0.887 and 0.973, respectively. Group 3 was significantly better in diagnostic efficiency of BMs than Group 1 (p<0.0001) or Group 2 (p=0.0006). The combination of CE T1WI and CE FLAIR promotes diagnostic performance and results in better observation and characterization of BM in patients with lung cancers. It provides a quick and efficient way of detecting BM.

Choroid plexus volume in multiple sclerosis can be estimated on structural MRI avoiding contrast injection

We compared choroid plexus (ChP) manual segmentation on non-contrast-enhanced (non-CE) sequences and reference standard CE T1- weighted (T1w) sequences in 61 multiple sclerosis patients prospectively included. ChP was separately segmented on T1w, T2-weighted (T2w) fluid-attenuated inversion-recovery (FLAIR), and CE-T1w sequences. Inter-rater variability assessed on 10 subjects showed high reproducibility between sequences measured by intraclass correlation coefficient (T1w 0.93, FLAIR 0.93, CE-T1w 0.99). CE-T1w showed higher signal-to-noise ratio and contrast-to-noise ratio (CE-T1w 23.77 and 18.49, T1w 13.73 and 7.44, FLAIR 13.09 and 10.77, respectively). Manual segmentation of ChP resulted 3.073 ± 0.563 mL (mean ± standard deviation) on T1w, 3.787 ± 0.679 mL on FLAIR, and 2.984 ± 0.506 mL on CE-T1w images, with an error of 28.02 ± 19.02% for FLAIR and 3.52 ± 12.61% for T1w. FLAIR overestimated ChP volume compared to CE-T1w (p < 0.001). The Dice similarity coefficient of CE-T1w versus T1w and FLAIR was 0.67 ± 0.05 and 0.68 ± 0.05, respectively. Spatial error distribution per slice was calculated after nonlinear coregistration to the standard MNI152 space and showed a heterogeneous profile along the ChP especially near the fornix and the hippocampus. Quantitative analyses suggest T1w as a surrogate of CE-T1w to estimate ChP volume.Relevance statement To estimate the ChP volume, CE-T1w can be replaced by non-CE T1w sequences because the error is acceptable, while FLAIR overestimates the ChP volume. This encourages the development of automatic tools for ChP segmentation, also improving the understanding of the role of the ChP volume in multiple sclerosis, promoting longitudinal studies.Key points • CE-T1w sequences are considered the reference standard for ChP manual segmentation.• FLAIR sequences showed a higher CNR than T1w sequences but overestimated the ChP volume.• Non-CE T1w sequences can be a surrogate of CE-T1w sequences for manual segmentation of ChP.Graphical

Late Lesion Growth following Endovascular Therapy: Is 24 h Too Early to Assess Acute Infarct Size Including the Effects of Secondary Injury?

Introduction: Stroke lesion volume on MRI or CT provides objective evidence of tissue injury as a consequence of ischemic stroke. Measurement of “final” lesion volume at 24-h following endovascular therapy (post-EVT) has been used in multiple studies as a surrogate for clinical outcome. However, despite successful recanalization, a significant proportion of patients do not experience favorable clinical outcome. The goals of this study were to quantify lesion growth during the first week after treatment, identify early predictors, and explore the association with clinical outcome. Methods: This is a prospective study of stroke patients at two centers who met the following criteria: (i) anterior large vessel occlusion acute ischemic stroke, (ii) attempted EVT, and (iii) had 3T MRI post-EVT at 24-h and 5-day. We defined “early” and “late” lesion growth as ≥10 mL lesion growth between baseline and 24-h diffusion-weighted imaging (DWI) and between 24-h DWI and 5-day fluid attenuated inversion recovery imaging, respectively. Complete reperfusion was defined as >90% reduction of the volume of tissue with perfusion delay (Tmax>6 s) between pre-EVT and 24-h post-EVT. Favorable clinical outcome was defined as modified Rankin scale (mRS) of 0–2 at 30 or 90 days. Results: One hundred twelve patients met study criteria with median age 67 years, 56% female, median admit NIHSS 19, 54% received IV or IA thrombolysis, 66% with M1 occlusion, and median baseline DWI volume 21.2 mL. Successful recanalization was achieved in 87%, and 68% had complete reperfusion, with an overall favorable clinical outcome rate of 53%. Nearly two-thirds (65%) of the patients did not have late lesion growth with a median volume change of −0.3 mL between 24-h and 5-day and an associated high rate of favorable clinical outcome (64%). However, ∼1/3 of patients (35%) did have significant late lesion growth despite successful recanalization (87%: 46% mTICI 2b/41% mTICI 3). Late lesion growth patients had a 27.4 mL change in late lesion volume and 30.1 mL change in early lesion volume. These patients had an increased hemorrhagic transformation (HT) rate of 68% with only 1 in 3 patients having favorable clinical outcome. Late lesion growth was independently associated with incomplete reperfusion, HT, and unfavorable outcome. Conclusion: Approximately 1 out of 3 patients had late lesion growth following EVT, with a favorable clinical outcome occurring in only 1 out of 3 of these patients. Most patients with no early lesion growth had no late lesion growth. Identification of patients with late lesion growth could be critical to guide clinical management and inform prognosis post-EVT. Additionally, it can serve as an imaging biomarker for the development of adjunctive therapies to mitigate reperfusion injury.