FLAIR Sequences Research Articles

Role of Magnetic Resonance Imaging (MRI) in grading gliomas comparable with pathology: A cross-sectional study from Syria.

ObjectiveGliomas are one of the most common brain tumors in adults with a poor prognosis in most patients. Magnetic Resonance Imaging (MRI) plays a critical role in the diagnosis, management, and follow-up of gliomas. The aim of this study is to assess the sensitivity and specificity of MRI in the preoperative grading of supratentorial gliomas in comparison to histopathology.MethodsA cross-sectional study included 39 patients, aged between 40 and 75 years with histologically diagnosed supratentorial gliomas who underwent conventional MR imaging, which included T1, T2, and FLAIR sequences from November 2018–December 2019 in the Department of Neurosurgery, Tishreen University Hospital, Lattakia. The histopathological typing and grading of the tumor were done by using 2016 WHO classification. The sensitivity, specificity, predictive value, and accuracy of MRI in determining tumor grade were calculated. The comparison was done between MRI findings and WHO histopathological grading.ResultsThe overall sensitivity and specificity of MRI findings in the assessment of high-grade gliomas were 100% and 91% respectively. The positive predictive value (PPV) was 66.6%, and the negative predictive value (NPV) was 100%. The overall accuracy was 94.9%. The agreement between histopathological and MRI findings was 72%.ConclusionsMRI plays an essential role in the initial diagnosis and grading of supratentorial gliomas with high sensitivity and specificity. It is considered a non-invasive method and is useful in cases where the biopsy procedure is a contraindication or rejected by the patient.

U-Net Based Segmentation and Characterization of Gliomas.

Simple SummaryGliomas comprise 80% of all malignant brain tumors. We aimed to develop a deep learning-based framework for the automatic segmentation and characterization of gliomas. In this retrospective study, patients were included if they: (1) had a diagnosis of glioma confirmed by histopathology and (2) had preoperative MRI with the inclusion of FLAIR imaging. The deep learning-based U-Net framework was developed based on manual segmentation on FLAIR as the ground truth mask for automatic segmentation and feature extraction, which were used for the prediction of biomarker status and prognosis. A total of 208 patients were included from our internal dataset with stratified sampling to split the database into training and validation. An external dataset (n = 31) from an outside institution was used for testing. The dice similarity coefficient of the generated mask was 0.93 on the testing dataset. The prediction of the radiomic model achieved an AUC of 0.88 for IDH-1 and 0.62 for MGMT on the testing dataset. Our deep learning-based framework can detect and segment gliomas with excellent performance for the prediction of IDH-1 biomarker status and survival.(1) Background: Gliomas are the most common primary brain neoplasms accounting for roughly 40–50% of all malignant primary central nervous system tumors. We aim to develop a deep learning-based framework for automated segmentation and prediction of biomarkers and prognosis in patients with gliomas. (2) Methods: In this retrospective two center study, patients were included if they (1) had a diagnosis of glioma with known surgical histopathology and (2) had preoperative MRI with FLAIR sequence. The entire tumor volume including FLAIR hyperintense infiltrative component and necrotic and cystic components was segmented. Deep learning-based U-Net framework was developed based on symmetric architecture from the 512 × 512 segmented maps from FLAIR as the ground truth mask. (3) Results: The final cohort consisted of 208 patients with mean ± standard deviation of age (years) of 56 ± 15 with M/F of 130/78. DSC of the generated mask was 0.93. Prediction for IDH-1 and MGMT status had a performance of AUC 0.88 and 0.62, respectively. Survival prediction of <18 months demonstrated AUC of 0.75. (4) Conclusions: Our deep learning-based framework can detect and segment gliomas with excellent performance for the prediction of IDH-1 biomarker status and survival.

Tumefactive demyelination-A Rare Presentation of Multiple Sclerosis

Background: Multiple Sclerosis is an inflammatory demyelinating disease having varied manifestations in terms of clinical featuresand radiological features. Rarely it may present as a large demyelinating lesionwith accompanying edema and mass effect, thereby simulating an intracranial tumor, known as Tumefactive Demyelination. Symptoms are usually related to the pressure of a focal mass lesion. When it manifests in a patient without pre-existing MS, it poses a diagnostic challengeas it may mimic a neoplasm, infarct or abscess. Thus, it is essential to recognize this rare clinical entity for proper patient management. Case Presentation: We report here a case of 40 years old female who presented with right sided hemiparesis. The initial brain imaging (CT) suggested left parietal lobe lesion suggestive of glioma. But MRI of the brain showed a cortical based lesion in left parietal lobe in parasaggital location which appeared hyperintense on both T2WI and FLAIR sequences. Further evaluation by MR spectroscopy suggested the lesion to be tumefactive demyelination. She was administered IV Methylprednisolone (1gm daily for 5 days) and responded well to treatment. The characteristic magnetic resonance findings of the patient, its acute onset, and its clinical improvement after corticosteroid therapy finally set the diagnosis of Tumefactive Demyelination. Conclusion: Tumefactive demyelinating lesions pose a unique diagnostic challenge in defining differential diagnosis and management and should always be considered in young patients presenting with tumour like lesions on imaging. Bangladesh Journal of Medical Science Vol. 21 No. 04 October’22 Page : 931-934

P01.17.A Role of preoperative assessment in predicting tumor induced plasticity in patients with diffuse gliomas

Abstract Background When diffuse gliomas (DG) affect the brain′s potential to reorganize functional networks, patients can exhibit seizures and/or language/cognitive impairment. The tumor-brain interaction and the individual connectomic organization cannot be predicted preoperatively. We aimed to, first, investigate the relationship between preoperative assessment and intraoperative findings of eloquent tumors in 36 DG operated with awake surgery. Second, we also studied possible mechanisms of tumor-induced brain reorganisation in these patients. Material and Methods FLAIR-MRI sequences were used for tumor volume segmentation and the Brain-Grid system (BG). This standardized radiological tool was used as an overlay for infiltration analysis. Neuropsychological (NPS) and/or language preoperative assessments were performed in all patients before the direct cortical/subcortical brain mapping. The distance between eloquent spots and tumor margins was registered and measured on the neuronavigation system. Tumours containing eloquent points within hyperintense area on FLAIR sequences (cortical or subcortical) were considered eloquent. Eloquent points within 5-10 mm from the FLAIR signal margin were considered peritumoral, while beyond 10 mm from the FLAIR signal were considered outside the tumour area. Pre and postoperative images (3 months after the operation) were merged to detect eventual residual tumour. Clinical, radiological, and topographical variables and intraoperative findings were used for correlation and logistic regression analyses. Results 84.6% of our patients displayed neuropsychological symptoms at the preoperative assessment. Eloquent tumors were detected in 75% of the cases. Impaired NPS functions were correlated with more invasive tumors, crucial location (A4C2S2/A3C2S2-voxels, left opercular-insular/sub-insular region) and higher risk of eloquent tumors. Epilepsy was correlated with larger tumor volumes and infiltrated A4C2S2 /A3C2S2 voxels. Language impairment was correlated with infiltrated A3C2S2 voxel. Group comparison between younger and older patients (cut-off at 38 years old based on ROC curves) showed a significative difference in the number of intratumoral cortical eloquent spots (p &amp;lt; .05) and the number of peritumoral cortical eloquent spots (p &amp;lt; .001). Conclusion Preoperative NPS impairment was linked with high risk of eloquent tumors. Epilepsy and language impairment were linked with specific tumor locations while peritumoral cortical eloquent spots reflected an early compensative mechanism with age as possible influencing factor. A systematic integration of extensive cognitive assessment and advanced neuroimaging can improve our comprehension of the connectomic brain organization at the individual scale and lead to a better oncological/functional balance.

Deep attention super-resolution of brain magnetic resonance images acquired under clinical protocols.

Vast quantities of Magnetic Resonance Images (MRI) are routinely acquired in clinical practice but, to speed up acquisition, these scans are typically of a quality that is sufficient for clinical diagnosis but sub-optimal for large-scale precision medicine, computational diagnostics, and large-scale neuroimaging collaborative research. Here, we present a critic-guided framework to upsample low-resolution (often 2D) MRI full scans to help overcome these limitations. We incorporate feature-importance and self-attention methods into our model to improve the interpretability of this study. We evaluate our framework on paired low- and high-resolution brain MRI structural full scans (i.e., T1-, T2-weighted, and FLAIR sequences are simultaneously input) obtained in clinical and research settings from scanners manufactured by Siemens, Phillips, and GE. We show that the upsampled MRIs are qualitatively faithful to the ground-truth high-quality scans (PSNR = 35.39; MAE = 3.78E−3; NMSE = 4.32E−10; SSIM = 0.9852; mean normal-appearing gray/white matter ratio intensity differences ranging from 0.0363 to 0.0784 for FLAIR, from 0.0010 to 0.0138 for T1-weighted and from 0.0156 to 0.074 for T2-weighted sequences). The automatic raw segmentation of tissues and lesions using the super-resolved images has fewer false positives and higher accuracy than those obtained from interpolated images in protocols represented with more than three sets in the training sample, making our approach a strong candidate for practical application in clinical and collaborative research.

3D Cube FLAIR plus HyperSense compressed sensing is superior to 2D T2WI FLAIR scanning regarding image quality, spatial resolution, detection rate for cortical microinfarcts.

3-dimention (3D) Cube isotropic volumetric magnetic resonance imaging (MRI) facilitates comprehensive recognition of microinfarcts while it takes long scanning time. HyperSense compressed sensing is an emerging technique for accelerating MRI acquisition to reduce scanning time, while its application along with 3D Cube MRI for microinfarcts is seldom reported. Therefore, this study aimed to investigate the efficiency of 3D Cube FLAIR plus HyperSense compressed sensing technique versus conventional 2-dimention (2D) FLAIR scanning in the detection of cortical microinfarcts (CMIs).Totally 59 patients with cerebrovascular disease were enrolled then scanned by 3D Cube FLAIR plus HyperSense compressed sensing and 2D T2WI FLAIR sequences. The image quality scores, signal-to-noise ratio (SNR) for gray matter (GM), SNR for white matter (WM), their contrast-to-noise ratio (WM-to-GM CNR), detected number of CMIs were evaluated.3D Cube FLAIR plus HyperSense showed a dramatically increased scores of uniformity, artifact, degree of lesion displacement, and overall image quality compared to 2D T2WI FLAIR. Meanwhile, it exhibited similar SNRwm and SNRgm, but a higher WM-to-GM contrast-to-noise ratio compared with 2D T2WI FLAIR. Furthermore, the scanning time of 3D Cube FLAIR plus HyperSense and 2D T2WI FLAIR were both set as 2.5 minutes. Encouragingly, 244 CMIs were detected by 3D Cube FLAIR plus HyperSense, which was higher compared to 2D T2WI FLAIR (106 detected CMIs).3D Cube FLAIR plus HyperSense compressed sensing is superior to 2D T2WI FLAIR scanning regarding image quality, spatial resolution, detection rate for CMIs; meanwhile, it does not increase the scanning time. These findings may contribute to early detection and treatment of stroke.

A-148 The Association of Neighborhood Trust and Violence with White Matter Integrity in a Diverse Sample

Abstract Objective: Hispanic and non-Hispanic Black older adults are at increased risk of dementia compared with non-Hispanic Whites. Cerebral white matter disease may contribute to this disparity, though little is known about how factors such as neighborhood disadvantage and perceived economic status may relate to white matter integrity in diverse groups. Method: Participants from a population-based, multiethnic cohort (N=2766; Female=58%; Black=48%; Hispanic=14%) completed a measure of neighborhood perceptions (“resources,” “trust,” “violence”) and perceived SES (“access to food,” “access to healthcare”) and underwent 3T brain MRI with 2D FLAIR and 3D T1-weighted sequences to assess white matter hyperintensity (WMH) burden and white matter volume (WMV), respectively. Linear regression models examined associations among these variables controlling for demographic and traditional SES variables (income, education), followed by post-hoc analyses by race/ethnicity and sex. Results: Reported income, education, ethnoracial group, and sex were associated with WMH in the overall sample (ps&amp;lt;.05), and “trust,” “access to healthcare,” income, education, race/ethnicity, and sex were significantly associated with cerebral WMV. Post-hoc analyses revealed “violence” was associated with more WMH in Black women (p&amp;lt;.05,β=-0.12), lower “trust” was associated with lower WMV in Hispanic men (p&amp;lt;.05,β=0.22), and lower “access to medical care” was associated with lower WMV in White women (p&amp;lt;.05,β=-0.18). Conclusions: Neighborhood disadvantage and perceived SES were significantly associated with indicators of white matter pathology, with differential relationships across race and sex. Findings may reflect disproportionate environmental and social stressors encountered by some groups. Future analyses will examine whether these sociocultural factors relate to white matter burden and cognitive function later in life.

O-02: RADIOMICS AND ARTIFICIAL INTELLIGENCE FOR IDENTIFICATION AND MONITORING OF SILENT CEREBRAL INFARCTS IN SICKLE CELL DISEASE: FIRST ANALYSIS FROM THE GENOMED4ALL EUROPEAN PROJECT

Purpose: The use of Artificial Intelligence (AI) for personalized medicine has recently guided dramatic improvements in the diagnostic pathway of several diseases. The European Project GENOMED4ALL, aims at using European level data of patients affected by Multiple Myeloma, Myelodysplastic Syndromes and Sickle Cell Disease (SCD) to find correlation between genomics – and other omics data – with phenotypic manifestations and seize the opportunity of improving diagnostics through AI. Silent Cerebral Infarcts (SCIs) are a significant determinant of morbidity since childhood in SCD. One of the aims of the SCD clinical case in GENOMED4ALL is the use of radiomics – quantitative method for the evaluation and interpretation of medical images- and AI firstly to develop an automatic and uniform identification and characterization of SCI through the analysis of cerebral MRI, secondly, to correlate imaging data with other types of omics data in order to predict risk of recurrence. Materials and methods: The MRI protocol included 3D-T1, FLAIR and DWI sequences. Neuroradiological reports were cross checked for consistency. A stepwise segmentation (identification of lesion volume), pre-processing and extraction of MRIs was performed utilizing different open-source software: Lesion Segmentation Tools and UNet for segmentation, Freesurfer for brain extraction. To optimize SCI identification, we have selected the best software for segmentation; freesurfer was substituted with a faster approach. Results: Six European SCD centers participated in this first phase of the Radiomics project with 501 MRIs: 225 were classified as abnormal by the local neuroradiologists due to presence of SCI, 275 were normal; 70% were pediatric MRIs. Different instruments were used in the 6 centers: Philips 1.5 T (n.2), Siemens 1.5 T and 3 T (n.2), GE 3T (n.2). A stepwise procedure allowed the optimization of SCI identification, with distinction between SCI and background non clinically significant noise (i.e periventricular areas) or other white matter hyperintensities (i.e transient glial maturation). As shown in Figure 1, to segment SCI in FLAIR MRI, we have used a pre-trained UNet [Li H, 2018]. Firstly, we have extracted the brain registering the MNI152 on the T1 image. Then we have applied the brain mask and a threshold taking only the largest component. The UNet performs the segmentation of hyperintensities. We have removed the regions surrounded by less than the 90% of White Matter or near the brain ventricles to remove non-SCI region. In the end, we have enlarged the remaining ones. To date, segmentation on 303 exams from different centers showed very few false positive and false negatives highlighting the need to take into account different technical characteristics due to the various equipment used in the different centers. Conclusion: SCD is a systemic disorder with extreme phenotypic variability. Radiomics and AI offer the opportunity to seize the potential of big datasets analysis to optimize diagnostic in SCD. SCI can be detected automatically from different datasets. Four more European Centers will add their MRI in the second phase of the project, to increase variability and allow correlation of in detailed Radiomics data (lesion volume, lesion site, etc) with clinical-hematological characteristics and other omics data.Figure 1. Stepwise Segmentation (identification and selection of lesion volume) workflow for automatic selection of all SCI. From left to right: Axial slices of the input scan, Slice after skull stripping to highlight only the brain parenchyma, Segmentatio The authors do not declare any conflict of interest

Magnetic Resonance Angiography and Cisternography fused images in acute ischemic stroke may save time during endovascular procedure revealing vessel anatomy

Background and purposeEndovascular treatment (EVT) is a time-dependent procedure that aims to remove the arterial blood flow obstruction in brain vessels in acute ischemic stroke. In our center, the MRI patient selection protocol in acute ischemic stroke is performed with DWI, FLAIR, MR angiography (MRA) and MR cisternography (MRC) sequences. MRA and MRC are promptly and automatically fused in order to have a clear detection of vessel anatomy, before and during EVT.Our study aim is to evaluate if the fusion process between MRA and MRC could be considered time-safe and could influence EVT duration or outcome. Materials and methods45 patients were retrospectively selected for the study and divided into 2 groups according to the presence of MRC sequence fused with MRA (Group 1) or not (Group 2 - controls). ResultsMRA and MRC fusion was able to depict vessel anatomy in all subjects of Group 1 (22 patients, 12 females; age 75.59 years ± 10.87). Group 1 presented EVT time reduction (p < 0.05;p = 0.040) (51.59 min ± 30.94) when compared to Group 2 (23 patients, 13 females; age 75.04 years ± 12.12) (71.96 min ± 34.55) of 20.37 min average. No differences between groups were detected evaluating: NIHSS at admission (p = 0.49) and discharge (p = 0.67), pre-stroke mRS (p = 0.89), mRS at 90 days (p = 0.62), ASPECT (p = 0.98) and ASPECT-DWI scores (p = 0.93), time from symptom onset to groin puncture (p = 0.80), thromboaspiration vs combined technique (p = 0.67), EVT success (p = 0.63). ConclusionFusion of MRA and MRC is a safe and promising technique in promptly revealing vascular anatomy beyond vessel obstruction, and can play a role in EVT duration reduction.

Validation of a Denoising Method Using Deep Learning-Based Reconstruction to Quantify Multiple Sclerosis Lesion Load on Fast FLAIR Imaging.

Accurate quantification of WM lesion load is essential for the care of patients with multiple sclerosis. We tested whether the combination of accelerated 3D-FLAIR and denoising using deep learning-based reconstruction could provide a relevant strategy while shortening the imaging examination. Twenty-eight patients with multiple sclerosis were prospectively examined using 4 implementations of 3D-FLAIR with decreasing scan times (4 minutes 54 seconds, 2 minutes 35 seconds, 1 minute 40 seconds, and 1 minute 15 seconds). Each FLAIR sequence was reconstructed without and with denoising using deep learning-based reconstruction, resulting in 8 FLAIR sequences per patient. Image quality was assessed with the Likert scale, apparent SNR, and contrast-to-noise ratio. Manual and automatic lesion segmentations, performed randomly and blindly, were quantitatively evaluated against ground truth using the absolute volume difference, true-positive rate, positive predictive value, Dice similarity coefficient, Hausdorff distance, and F1 score based on the lesion count. The Wilcoxon signed-rank test and 2-way ANOVA were performed. Both image-quality evaluation and the various metrics showed deterioration when the FLAIR scan time was accelerated. However, denoising using deep learning-based reconstruction significantly improved subjective image quality and quantitative performance metrics, particularly for manual segmentation. Overall, denoising using deep learning-based reconstruction helped to recover contours closer to those from the criterion standard and to capture individual lesions otherwise overlooked. The Dice similarity coefficient was equivalent between the 2-minutes-35-seconds-long FLAIR with denoising using deep learning-based reconstruction and the 4-minutes-54-seconds-long reference FLAIR sequence. Denoising using deep learning-based reconstruction helps to recognize multiple sclerosis lesions buried in the noise of accelerated FLAIR acquisitions, a possibly useful strategy to efficiently shorten the scan time in clinical practice.

Shigella-associated mild encephalitis with reversible splenial lesion in Hospital Center Delafontaine, Saint-Denis, France: a case report

BackgroundMild encephalitis with reversible splenial lesion of the corpus callosum (MERS) is a clinical/radiological syndrome characterized by hyperintense signal changes in the splenium of the corpus callosum visible on diffusion weighted imaging (DWI) in the brain Magnetic Resonance Imaging (MRI) associated with various neurological symptoms. Progression is usually favorable with disappearance of the MRI brain lesion and regression of clinical symptoms over a few days to a few weeks. The exact pathophysiology remains unclear. MERS can be associated with various pathogens.Case presentationWe report here a paediatric case of MERS associated with Shigella flexneri infection. A five-year-old boy with no relevant past medical history presented with symptoms such as headache, fever, profuse diarrhea and hallucinations.A brain Magnetic Resonance Imaging performed on Day 2 of the symptoms revealed hyperintense signal changes of the splenium of the corpus callosum in T2 FLAIR sequence. This infection had a favorable outcome after antibiotic therapy. No further recurrence of symptoms was observed and a follow-up clinical examination eight weeks later was normal. A follow-up brain Magnetic Resonance Imaging three months after discharge was also normal and the hyperintense signal changes of the splenium of the corpus callosum had disappeared completely.ConclusionsMERS is a clinical/radiological syndrome with a generally good prognosis.We believe that this is the first description of a case of Shigella-associated MERS.It is useful to know about this condition to help distinguish it from acute disseminated encephalomyelitis.

Differential spatial distribution of white matter lesions in Parkinson's and Alzheimer's diseases and cognitive sequelae.

White Matter Lesions (WML) are a radiological finding common in aged subjects. We explored the impact of WML on underlying neurodegenerative processes. We focused on the impact of WML on two neurodegenerative diseases with different pathology. In this cross-sectional study of 137 subjects (78 female, 59 men, mean age 67.2; 43-87years), we compared WML in healthy controls (HC; n = 55), patients with Alzheimer's disease and amnestic Mild Cognitive Impairment (aMCI), and Parkinson's disease patients with normal cognition and with MCI. Subjects with AD and aMCI were treated as one group (n = 40), subjects with PD and PDMCI were another group (n = 42). MRI T2_FLAIR sequences were analyzed. WML were divided into periventricular (pWML) or subcortical (sWML) depending on their distance from the ventricles. Subjects from the AD + aMCI group, had a significantly greater volume of WML than both HC and the PD + PDMCI group. The volume of WML was greater in the PD + PDMCI than in HC but the difference was not significant. In AD + aMCI subjects, sWML and not pWML were related to a decrease in global cognitive functioning despite greater volume of pWML. In PD + PDMCI, pWML correlate with decline in executive functions and working memory. In HC, pWML correlated with the multidomain decrease corresponding with the aging. This points to a difference between normal aging and pathological aging due to AD and PD brain pathology. The WML location together with underlying disease related neurodegeneration may play a role in determining the effect of WML on cognition. Our results suggest that the impact of WML is not uniform in all patients; rather, their volume, location and cognitive effect may be disease-specific.

Diagnostic Accuracy of Contrast Enhanced Flair Magnetic Resonance Imaging in Diagnosis of Meningitis Taking Lumbar Puncture as Gold Standard

Objective: The objective of this article is to determine and compare the diagnostic accuracies of contrastenhanced FLAIR and T1W sequences of MRI brain in the detection of meningitis keeping lumbar puncture asgold standard.Study Design: Cross-sectional validation study.Place and Duration of Study: The study was carried out at Department of Diagnostic Radiology of POFHospital, Wah Cantt from 23 June 2019 to 22 March 2020.Materials and Methods: A total of 173 patients were included in the study by non-probability purposivesampling. Patients of age between 2-70 years, of either gender with suspected meningitis based on clinicalpresentation are included in our study. Patients in whom contrast enhanced MRI and lumbar puncture wascontraindicated, diagnosed patient of meningitis and non-consenting patients were excluded from this study.All patients received intravenous contrast medium gadolinium at rate of 0.2 ml/second. Post gadolinium T1Wand post gadolinium FLAIR images were acquired and evaluated by a consultant radiologist. Findings wererecorded on a prescribed performa. Patients were followed and results of lumbar puncture were collected fromlaboratory.Result: The mean age was 26.4±23.5 year ranging from 2 to 70 years. Out of 173 patients, 98 patients (56.6%)were male and 75 patients (43.4%) were female. Clinical presentations were as follows: poor feeding, irritabilityand lethargy 86 (49.7%), headache 137 (79.2%), nausea/vomiting 125 (72.3%), neck stiffness 89 (51.4%),altered level of consciousness 132 (76.3%), seizures 78 (45.1%) and local neurological deficit 45 (26%).Diagnostic accuracy of contrast enhanced MRI FLAIR in diagnosing meningitis taking lumbar puncture goldstandard showed sensitivity 91%, specificity 85%, PPV 87.6%, NPV 89.4% and diagnostic accuracy 88.4%.Diagnostic accuracy of contrast enhanced MRI T1W in the diagnosis of meningitis taking lumbar puncture goldstandard revealed sensitivity 60.2%, specificity 77.5%, PPV 75.6%, NPV 62.6% and diagnostic accuracy 68.2%.Conclusion: The sensitivity and specificity of post contrast FLAIR sequence is greater as compared to postcontrast T1W sequence to detect meningeal enhancement. Therefore, for all patients with suspicion ofmeningitis, post contrast FLAIR sequence should be added to MRI brain protocol as a routine sequence.How to cite this: Jawwad S, Gul N, Mehmood K, Iqbal B, Afzal K. Diagnostic Accuracy of Contrast Enhanced Flair Magnetic Resonance Imaging in Diagnosis of Meningitis Taking Lumbar Puncture as Gold Standard. Life and Science. 2022; 3(3): 120-126. doi: http://doi.org/10.37185/LnS.1.1.259

Role of contrast-enhanced flair sequence in magnetic resonance imaging for evaluating intracranial pathologies

Background: Magnetic resolution imaging (MRI) has superior soft tissue contrast for characterization and diagnosing various intracranial lesions. Various MRI techniques are used for characterizing intracranial lesions. Post contrast T1 imaging is the traditional technique used for assessing lesion contrast enhancement. This study intends to assess the use of post contrast Fluid Attenuated Inversion Recovery (FLAIR) imaging for assessing lesion contrast enhancement. Results: The participants were analyzed by age, gender, number of lesions observed for each participant, signal intensity pattern for all intracranial lesions on both post contrast sequences, comparison of degree of contrast enhancement of all intracranial lesions on both post contrast sequences, comparison between various intra-axial & extra-axial intracranial lesions on both post contrast sequences, comparison between various intracranial lesions based on their etiology on both post contrast sequences and comparison of each intracranial lesion on both post contrast sequences. Conclusion: The study suggests that the overall degree of contrast enhancement is superior for contrast enhanced T1 sequences for the majority of the lesions, however complete lesion pathology cannot be assessed on this sequence alone. Although post contrast FLAIR sequence has shown greater degree of contrast enhancement in infectious meningitis, hemangioblastoma and toxoplasmosis, this was not statistically significant (p value >0.05) possibly owing to less sample size. Hence, further studies with more sample size should be assessed to establish the significance. Therefore, contrast enhanced FLAIR sequence is an important adjunct for imaging of the various intracranial pathologies and should be incorporated for evaluating various intracranial pathologies.

Radiomic Features Based on MRI Predict Progression-Free Survival in Pediatric Diffuse Midline Glioma/Diffuse Intrinsic Pontine Glioma.

Purpose: Biopsy-based assessment of H3 K27M status helps in predicting survival, but biopsy is usually limited to unusual presentations and clinical trials. We aimed to evaluate whether radiomics can serve as prognostic marker to stratify diffuse intrinsic pontine glioma (DIPG) subsets. Methods: In this retrospective study, diagnostic brain MRIs of children with DIPG were analyzed. Radiomic features were extracted from tumor segmentations and data were split into training/testing sets (80:20). A conditional survival forest model was applied to predict progression-free survival (PFS) using training data. The trained model was validated on the test data, and concordances were calculated for PFS. Experiments were repeated 100 times using randomized versions of the respective percentage of the training/test data. Results: A total of 89 patients were identified (48 females, 53.9%). Median age at time of diagnosis was 6.64years (range: 1-16.9years) and median PFS was 8months (range: 1-84months). Molecular data were available for 26 patients (29.2%) (1 wild type, 3 K27M-H3.1, 22 K27M-H3.3). Radiomic features of FLAIR and nonenhanced T1-weighted sequences were predictive of PFS. The best FLAIR radiomics model yielded a concordance of .87 [95% CI: .86-.88] at 4months PFS. The best T1-weighted radiomics model yielded a concordance of .82 [95% CI: .8-.84] at 4months PFS. The best combined FLAIR + T1-weighted radiomics model yielded a concordance of .74 [95% CI: .71-.77] at 3months PFS. The predominant predictive radiomic feature matrix was gray-level size-zone. Conclusion: MRI-based radiomics may predict progression-free survival in pediatric diffuse midline glioma/diffuse intrinsic pontine glioma.

An Integrated Analysis of Clinical, Genomic, and Imaging Features Reveals Predictors of Neurocognitive Outcomes in a Longitudinal Cohort of Pediatric Cancer Survivors, Enriched with CNS Tumors (Rad ART Pro).

BackgroundNeurocognitive deficits in pediatric cancer survivors occur frequently; however, individual outcomes are unpredictable. We investigate clinical, genetic, and imaging predictors of neurocognition in pediatric cancer survivors, with a focus on survivors of central nervous system (CNS) tumors exposed to radiation.MethodsOne hundred eighteen patients with benign or malignant cancers (median diagnosis age: 7; 32% embryonal CNS tumors) were selected from an existing multi-institutional cohort (RadART Pro) if they had: 1) neurocognitive evaluation; 2) available DNA; 3) standard imaging. Utilizing RadART Pro, we collected clinical history, genomic sequencing, CNS imaging, and neurocognitive outcomes. We performed single nucleotide polymorphism (SNP) genotyping for candidate genes associated with neurocognition: COMT, BDNF, KIBRA, APOE, KLOTHO. Longitudinal neurocognitive testing were performed using validated computer-based CogState batteries. The imaging cohort was made of patients with available iron-sensitive (n = 28) and/or T2 FLAIR (n = 41) sequences. Cerebral microbleeds (CMB) were identified using a semi-automated algorithm. Volume of T2 FLAIR white matter lesions (WML) was measured using an automated method based on a convolutional neural network. Summary statistics were performed for patient characteristics, neurocognitive assessments, and imaging. Linear mixed effects and hierarchical models assessed patient characteristics and SNP relationship with neurocognition over time. Nested case-control analysis was performed to compare candidate gene carriers to non-carriers.ResultsCMB presence at baseline correlated with worse performance in 3 of 7 domains, including executive function. Higher baseline WML volumes correlated with worse performance in executive function and verbal learning. No candidate gene reliably predicted neurocognitive outcomes; however, APOE ϵ4 carriers trended toward worse neurocognitive function over time compared to other candidate genes and carried the highest odds of low neurocognitive performance across all domains (odds ratio 2.85, P=0.002). Hydrocephalus and seizures at diagnosis were the clinical characteristics most frequently associated with worse performance in neurocognitive domains (5 of 7 domains). Overall, executive function and verbal learning were the most frequently negatively impacted neurocognitive domains.ConclusionPresence of CMB, APOE ϵ4 carrier status, hydrocephalus, and seizures correlate with worse neurocognitive outcomes in pediatric cancer survivors, enriched with CNS tumors exposed to radiation. Ongoing research is underway to verify trends in larger cohorts.

Radiological hints for differentiation of cerebellar multiple system atrophy from spinocerebellar ataxia

Differentiation cerebellar multiple systemic atrophy (MSA-C) from spinocerebellar ataxia (SCA) is important. The “hot cross bun” sign (HCBS) at pons and magnetic resonance spectroscopy (MRS) are helpful. However, the prevalence of HCBS and the alteration of cerebellar MRS parameters are evolving with disease progression. We hypothesized that since the HCBS and MRS are evolving with time, different parameters for differentiation of MSA-C and SCA are required at different disease stages. The aim of this study was to evaluate the HCBS and MRS changes in patients with MSA-C and SCA at different disease stages. A total of 398 patients with molecularly confirmed SCA (SCA1, 2, 3, 6, 17) and 286 patients diagnosed with probable MSA-C (without mutations in SCA1, 2, 3, 6, 17 genes), who had received brain magnetic resonance imaging (MRI) and MRS from January 2000 to January 2020, were recruited. Twenty-five patients were molecularly identified as having SCA1, 68 as SCA2, 253 as SCA3, 34 as SCA6, and 18 as SCA17. We compared their clinical parameters and neuroimaging features at different disease stages. The presence of HCBS was assessed using an axial T2 fast spin-echo or FLAIR sequence. Proton MRS was recorded with voxel of interest focusing on cerebellar hemispheres and cerebellar vermis and avoiding cerebrospinal fluid spaces space using a single-voxel stimulated echo acquisition mode sequence. We found that patients with MSA-C tend to have a higher prevalence of pontine HCBS, worse Scale for the Assessment and Rating of Ataxia scores, lower cerebellar N-acetyl aspartate (NAA)/creatinine (Cr), and choline (Cho)/Cr, compared to patients with SCA at corresponding disease stages. In MSA-C patients with a disease duration < 1 year and without pontine HCBS, a cerebellar NAA/Cr ≤ 0.79 is a good indicator of the possibility of MSA-C. By using the pontine HCBS and cerebellar MRS, discerning MSA-C from SCA became possible. This study provides cutoff values of MRS to serve as clues in differentiating MSA-C from SCAs.

A polynomial regression-based approach to estimate relaxation rate maps suitable for multiparametric segmentation of clinical brain MRI studies in multiple sclerosis

Background and ObjectiveRelaxation parameter maps (RPMs) calculated from spin-echo data have provided a basis for the segmentation of normal brain tissues and white matter lesions in multiple sclerosis (MS) MRI studies. However, Conventional Spin-Echo (CSE) sequences, once the core of clinical MRI studies, have been largely replaced by faster ones, which do not allow the calculation a-posteriori of RPMs from clinical studies. Aim of the study was to develop and validate a method to estimate RPMs (pseudo-RPMs) from routine clinical MRI protocols (including 3D-Gradient Echo T1w, FLAIR and fast-T2w sequences), suitable for fully automatic multiparametric segmentation of normal-appearing and pathological brain tissues in MS. MethodsThe proposed method processes spatially normalized clinical MRI studies through a multistep pipeline, to collect a set of data points of matched signal intensities (from MRI studies) and relaxation parameters (from a CSE-derived digital template and an MS lesion database), which are then fitted by a multiple and multivariate 4-th degree polynomial regression, providing pseudo-RPMs. The method was applied to a dataset of 59 clinical MRI studies providing pseudo-RPMs that were segmented through a method originally developed for the CSE-derived RPMs. Results of the segmentation in 12 studies were used to iteratively optimize method parameters. Accuracy of segmentation of normal-appearing brain tissues from the pseudo-RPMs was assessed by comparing their age-related changes, as measured in 47 clinical studies, against those measured acquired using CSE sequences in a comparable dataset of 47 patients. Lesion segmentation was validated against manual segmentation carried out by three neuroradiologists. ResultsAge-related changes of normal-appearing brain tissue volumes measured using the pseudo-RPMs substantially overlapped those measured using the RPMs obtained from CSE sequences, and segmentation of MS lesions showed a moderate-high spatial overlap with manual segmentation, comparable to that achieved by the widely used Lesion Segmentation Tool on FLAIR images, with a greater volumetric agreement. ConclusionsThe proposed approach allows calculation from clinical studies of pseudo-RPMs, which are equivalent to those obtainable from CSE sequences, avoiding the need for the acquisition of additional, dedicated sequences for segmentation purposes.

Evaluation of the response assessment criteria in newly diagnosed and recurrent glioblastoma.

2020 Background: The Response Assessment in Neuro-Oncology (RANO) and modified RANO (mRANO) criteria are the two most widely used criteria to evaluate treatment response in glioblastoma (GBM) clinical trials. Unlike RANO, mRANO omits the evaluation of FLAIR sequence and requires a repeat scan to confirm responses. It also uses the post-radiation (RT) MRI as a baseline MRI in the newly diagnosed setting instead of the pre-RT MRI used in RANO. We sought to compare the 2 response assessment criteria and evaluate the differences between them in a large patient population. We also sought to compare them to immunotherapy RANO (iRANO) in patients who received immunotherapy. Methods: We conducted a retrospective review of consecutive patients with newly diagnosed (nGBM) and recurrent (rGBM) IDH wild-type GBM treated at Dana-Farber Cancer Institute from 2014 to 2020. Bidimensional measurements of enhancing disease and evaluation of FLAIR sequences were performed by two independent readers on patients’ brain MRIs obtained before change of treatment, and discrepancies were evaluated by a third reader. Dates of disease progression (PD) were identified using RANO, mRANO, iRANO, and other response assessment criteria variations. Spearman’s correlations between PFS and OS were calculated using an iterative multiple imputation method to account for any right-censoring. Results: 526 and 580 patients were included in the newly diagnosed and recurrent cohorts, respectively. Spearman’s correlations were not significantly different between RANO and mRANO in the nGBM (0.69 [95% CI 0.62 to 0.75] vs. 0.67 [0.60, 0.73]) and rGBM (0.45 [0.37, 0.52] vs. 0.50 [0.42, 0.57]) cohorts. Evaluation of FLAIR sequences did not improve the correlation between PFS and OS in patients who received antiangiogenic therapy. Addition of confirmation scans was associated with stronger Spearman’s correlations only when PD was identified within 12 weeks of completion of RT in the nGBM cohort, but did not affect the Spearman’s correlations in the rGBM cohort. The use of the post-RT MRI as a baseline was associated with a higher Spearman’s correlation in nGBM than the use of pre-RT MRI (0.67 [0.60, 0.73] vs. 0.53 [0.42, 0.62]). Among 98 patients with nGBM and 175 patients with rGBM who received immunotherapy, the Spearman’s correlations (nGBM and rGBM) with iRANO (0.63 [0.44, 0.76] and 0.34 [0.17, 0.49]) were similar to RANO (0.73 [0.60, 0.82] and 0.42 [0.28, 0.54]) and mRANO (0.65 [0.48, 0.77] and 0.43 [0.28, 0.56]). Conclusions: RANO and mRANO demonstrated similar correlation between PFS and OS. The evaluation of FLAIR can be omitted, while confirmation scans appear to be only beneficial in the nGBM settings during the first 12 weeks of completion of RT. There was a nonsignificant trend in favor of the use of post-RT MRI as the baseline scan in the nGBM setting. The application of iRANO criteria did not add significant benefit in patients who received immunotherapy.

Diagnostic value of three-dimensional cube fluid attenuated inversion recovery imaging and its axial MIP reconstruction in multiple sclerosis

BackgroundMagnetic resonance imaging is regarded as one of the most important markers for multiple sclerosis. It can detect lesions in order to establish dissemination in time and space, which would aid in the diagnosis. Two-dimensional FLAIR is a standard sequence in MS routine imaging because it suppresses cerebrospinal fluid signal, increasing contrast between lesions and CSF and improving white matter lesion detection. Newer 3D FLAIR sequences are expected to offer even more benefits, such as improved MS lesions detection and higher resolution due to thinner slice thickness. We aimed to compare the role of 3D Cube FLAIR imaging (versus standard 2D FLAIR) in the assessment of white matter lesions in MS patients, as well as to test the convenience of using maximum intensity projection (MIP) on 3D FLAIR images for faster and easier evaluation.ResultsThis study included 160 MS patients. A 1.5 T routine brain MRI scan was performed, which included a 2D FLAIR sequence, followed by a 3D-FLAIR sequence. All images were analyzed after 3D-FLAIR images were reformatted into axial MIP images. Lesions were counted in each sequence and classified into supra-tentorial (periventricular, deep white matter, and juxta-cortical), and infra-tentorial lesions, with the relative comparison of lesions numbers on 3D-FLAIR and MIP versus 2D-FLAIR expressed as a percentage increase or decrease. 3D FLAIR can significantly improve MS lesion detection in all areas of the brain when compared with 2D FLAIR results. At 2 mm reformatting, there is no difference in MS lesion detection between sagittal 3D FLAIR and axial MIP reconstruction, implying that the MIP algorithm can be used to simplify lesion detection by reducing the number of images while maintaining the same level of reliability.Conclusion3D FLAIR sequences should be added to conventional 2D FLAIR sequences in the MRI protocol when MS is suspected.