Fluid-attenuated Inversion Recovery Images Research Articles

Contrast-Enhanced Three-Dimensional Fluid-Attenuated Inversion Recovery Imaging with an Optimal Scan Interval and Angulation to Visualize Endolymphatic Hydrops

Background: There is no gold standard diagnostic test for endolymphatic hydrops (EH). Three-dimensional fluid-attenuated inversion recovery (3D-FLAIR) imaging has been reported to depict EH with administration of gadolinium-based contrast media (GBCM). However, the optimal scan interval and angulation remain unknown in 3D-FLAIR labyrinthine imaging following double-dose injections of a gadolinium-based contrast agent in patients with vertigo and sensorineural hearing loss. Objectives: This study aimed to determine the optimal parameters of 3D-FLAIR labyrinthine imaging, including the optimal scan angulation and scan interval, for patients with sensorineural hearing loss and vertigo. Patients and Methods: In this cross-sectional clinical study, following the double-dose administration of a gadolinium contrast agent, 3D-FLAIR labyrinthine images were acquired from 22 patients with unilateral vertigo and sensorineural hearing loss at different intervals after injection. The corresponding contrast-to-noise ratios (CNRs) and signal-intensity ratios (SIRs) of these images, acquired at different intervals, were measured. Moreover, separate visualization of endolymphatic and perilymphatic spaces was scored, and angulation of the anterior skull base scan was investigated in the sagittal position. Results: The 3D-FLAIR images showed the strongest image contrast in the cochlea with a double-dose gadolinium-based contrast injection at six hours post-injection. Significantly higher SIR and CNR values were reported at six hours post-injection in both unaffected and affected ears compared to other intervals (4 h vs. 6 h in the affected side, SIR: 1.65 ± 0.24 vs. 2.09 ± 0.47, CNR: 13.88 ± 5.54 vs. 19.17 ± 6.81; in the unaffected side, SIR: 1.58 ± 0.27 vs. 1.82 ± 0.34, CNR: 12.20 ± 3.88 vs. 15.42 ± 4.58, P < 0.001 for all; 6 h vs. 8 h in the affected side: SIR: 2.09 ± 0.47 vs. 1.72 ± 0.43, CNR: 19.17 ± 6.81 vs. 12.22 ± 4.96; in the unaffected side, SIR: 1.82 ± 0.34 vs. 1.57 ± 0.30, CNR: 15.42 ± 4.58 vs. 10.61 ± 3.87, P < 0.001 for all). Visualization of the endo- and perilymphatic spaces for both the cochlea and vestibule was significantly better at six hours post-injection compared to four hours post-injection in both affected sides (P < 0.05 for both). The optimal angulation ranged from 6.20° to 13.6° (P < 0.001). Conclusion: By using an optimal scan interval, together with an optimal scan angulation, 3D-FLAIR imaging can reliably visualize the endolymphatic space and sensitively indicate cochlear blood-labyrinth barrier disruptions without requiring extra image reconstruction.

Semisupervised white matter hyperintensities segmentation on MRI.

This study proposed a semisupervised loss function named level-set loss (LSLoss) for cerebral white matter hyperintensities (WMHs) segmentation on fluid-attenuated inversion recovery images. The training procedure did not require manually labeled WMH masks. Our image preprocessing steps included biased field correction, skull stripping, and white matter segmentation. With the proposed LSLoss, we trained a V-Net using the MRI images from both local and public databases. Local databases were the small vessel disease cohort (HKU-SVD, n=360) and the multiple sclerosis cohort (HKU-MS, n=20) from our institutional imaging center. Public databases were the Medical Image Computing Computer-assisted Intervention (MICCAI) WMH challenge database (MICCAI-WMH, n=60) and the normal control cohort of the Alzheimer's Disease Neuroimaging Initiative database (ADNI-CN, n=15). We achieved an overall dice similarity coefficient (DSC) of 0.81 on the HKU-SVD testing set (n=20), DSC=0.77 on the HKU-MS testing set (n=5), and DSC=0.78 on MICCAI-WMH testing set (n=30). The segmentation results obtained by our semisupervised V-Net were comparable with the supervised methods and outperformed the unsupervised methods in the literature.

Technical note: Quantitative optimization of the FLAIR sequence in post mortem magnetic resonance imaging

The fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) sequence aims at suppressing the signal of the cerebrospinal fluid (CSF) by acquiring images at the time point at which the longitudinal magnetization and therefore the signal of CSF is zero. This time point is also called the null point inversion time (TInull). However, the FLAIR sequence is impaired by the temperature dependency of TInull in post mortem MRI due to the lower body temperature of the deceased subject. Therefore, the temperature correction of TInull is crucial for correctly suppressing the CSF signal in post mortem FLAIR imaging. Thus, the goal of this study was to determine the temperature effect on post mortem TInull for achieving a robust suppression of the CSF signal in in situ post mortem MRI using the FLAIR sequence. For this purpose, nine deceased subjects underwent an in situ MRI brain examination on a 3 T MRI scanner. TInull of CSF was determined quantitatively based on different FLAIR acquisitions using varying inversion times. The brain and rectal temperatures were determined prior to the MRI scan. A significant positive linear relation was found between TInull of CSF and the brain temperature, as well as between TInull of CSF and the rectal temperature. The found linear relations between TInull and both brain and rectal temperatures allow correcting TInull for varying temperatures of the deceased. This in turn enables an optimal suppression of the CSF signal in future post mortem FLAIR MRI acquisitions.

High-resolution MRI to noninvasively characterize drainage around the carotid artery into the cervical lymph nodes.

Previous studies have proposed multiple parallel channels for waste clearance from the brain, though many gaps remain in our understanding of these systems. In this study, we examined periarterial fluid drainage around intracranial and extracranial segments of the internal carotid arteries (ICAs) from the brain into the cervical lymph nodes using a noninvasive and clinical-based method. Eighty-one subjects (45 females, aged 15-80years old) with nonlesioned epilepsy underwent high-resolution 3-dimensional T2-weighted fluid-attenuated inversion recovery (FLAIR) MRI. We utilized a noninvasive and clinical-based method with a manual semiautomated approach to characterize the periarterial lymphatic system's maximum thickness and signal intensities along the ICAs using high-resolution 3-dimensional FLAIR imaging. We conducted group comparisons and correlation analyses to investigate sex- and age-based trends. Results were corrected with Bonferroni's test for multiple comparisons, and we performed power analysis for sample size calculations. Using high-resolution FLAIR images, we show evidence that fluid drainage emerges around the ICA petrous segment and joins lymphatic flow from cranial nerves in the upper neck, with this flow ultimately draining into the cervical lymph nodes bilaterally. Lymphatic signal at the petrous segment level was significantly thinner in females compared to males bilaterally (w=413, p=.0001 on the right, w=356, p<.0001 on the left). Lymphatic drainage around the petrous segments of the ICAs bilaterally was thicker with age in males but not in females. We describe the in vivo high-resolution imaging characteristics of periarterial fluid drainage along the vessel walls of ICAs. This represents a potentially major channel for brain waste clearance. We also report interesting sex- and age-based trends in these structures within our cohort.

Multimodal MRI-Based Radiomic Nomogram for the Early Differentiation of Recurrence and Pseudoprogression of High-Grade Glioma.

Objective To evaluate the diagnostic value of multimodal MRI radiomics based on T2-weighted fluid attenuated inversion recovery imaging (T2WI-FLAIR) combined with T1-weighted contrast enhanced imaging (T1WI-CE) in the early differentiation of high-grade glioma recurrence from pseudoprogression. Methods A total of one hundred eighteen patients with brain gliomas who were diagnosed from March 2014 to April 2020 were retrospectively analyzed. According to the clinical characteristics, the patients were randomly split into a training group (n = 83) and a test group (n = 35) at a 7 : 3 ratio. The region of interest (ROI) was delineated, and 2632 radiomic features were extracted. We used multiple logistic regression to establish a classification model, including the T1 model, T2 model, and T1 + T2 model, to differentiate recurrence from pseudoprogression. The diagnostic efficiency of the model was evaluated by calculating the area under the receiver operating characteristic curve (AUC) and accuracy (ACC) and by analyzing the calibration curve of the nomogram and decision curve. Results There were 75 cases of recurrence and 43 cases of pseudoprogression. The diagnostic efficacies of the multimodal MRI-based radiomic model were relatively high. The AUC values and ACC of the training group were 0.831 and 77.11%, respectively, and the AUC values and ACC of the test group were 0.829 and 88.57%, respectively. The calibration curve of the nomogram showed that the discrimination probability was consistent with the actual occurrence in the training group, and the discrimination probability was roughly the same as the actual occurrence in the test group. In the decision curve analysis, the T1 + T2 model showed greater overall net efficiency. Conclusion The multimodal MRI radiomic model has relatively high efficiency in the early differentiation of recurrence from pseudoprogression, and it could be helpful for clinicians in devising correct treatment plans so that patients can be treated in a timely and accurate manner.

Usefulness of deep learning-based noise reduction for 1.5 T MRI brain images

To evaluate 1.5 T magnetic resonance imaging (MRI) brain images with denoising procedures using deep learning-based reconstruction (dDLR) relative to the original 1.5 and 3 T images. Eleven volunteers underwent MRI at 3 and 1.5 T. Two-dimensional fast spin-echo T2-weighted imaging (T2WI), fluid-attenuated inversion recovery (FLAIR) imaging and diffusion-weighted imaging (DWI) sequences were performed. The dDLR method was applied to the 1.5 T data (dDLR-1.5 T), then the image quality of the dDLR-1.5 T data relative to the original 1.5 T and 3 T data was qualitatively and quantitatively assessed based on the structure similarity (SSIM) index; the signal-to-noise ratios (SNRs) of the grey matter (GM) and white matter (WM); and the contrast-to-noise ratios (CNRs) between the GM and WM (CNRgm-wm) and between the striatum (ST) and WM (CNRst-wm). The perceived image quality, and SNRs and CNRs were significantly higher for the dDLR-1.5 T images versus the 1.5 T images for all sequences and almost comparable or even superior to those of the 3 T images. For DWI, the SNRs and CNRst-wm were significantly higher for the dDLR-1.5 T images versus the 3 T images. The dDLR technique improved the image quality of 1.5 T brain MRI images. With respect to qualitative and quantitative measurements, the denoised 1.5 T brain images were almost equivalent or even superior to the 3 T brain images.

Periodic sharp wave complexes identify a distinctive phenotype in Creutzfeldt-Jacob disease

ObjectiveThe aim of our study was to evaluate the diagnostic and prognostic value of Electroencephalogram (EEG), brain Magnetic Resonance Imaging (MRI) and cerebrospinal fluid features, currently representing Creutzfeldt-Jacob Disease (CJD) diagnostic criteria. MethodsA retrospective study on rapidly progressive dementia patients admitted at the Neurology Clinic of the University of Rome “Tor Vergata” between 2015 and 2020 was conducted. We evaluated clinical, EEG, cerebrospinal fluid and neuroradiological findings. ResultsOur analysis included 13 patients with probable CJD and 18 patients with non-CJD rapidly progressive dementia. Periodic sharp wave complexes were observed in 7/13 CJD and in 4/18 non-CJD patients (p =.151). The sub-analysis according to the EEG features revealed that CJD patients with earlier periodic sharp wave complexes had a significantly lower average survival time (p =.003), a shorter time to admission (p =.003) and lower levels of cerebrospinal fluid p-tau (p =.008) compared to CJD patients without periodic sharp wave complexes. Finally, they did not show signs of signal alteration on Fluid Attenuated Inversion Recovery images. ConclusionsDespite the lowest diagnostic specificity and sensibility among the CJD criteria, periodic sharp wave complexes could identify a distinctive phenotype hallmarked by a faster evolution, a reduced survival time and specific MRI and cerebrospinal fluid features. SignificanceThe early presence of the typical EEG pattern may play a prognostic role in CJD.

Identifying acute ischemic stroke patients within the thrombolytic treatment window using deep learning.

Treatment of acute ischemic stroke is heavily contingent upon time, as there is a strong relationship between time clock and tissue progression. Work has established imaging biomarker assessments as surrogates for time since stroke (TSS), namely, by comparing signal mismatch between diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) imaging. Our goal was to develop an automatic technique for determining TSS from imaging that does not require subspecialist radiology expertise. Using 772 patients (66±9years, 319 women), we developed and externally evaluated a deep learning network for classifying TSS from MR images and compared algorithm predictions to neuroradiologist assessments of DWI-FLAIR mismatch. Models were trained to classify TSS within 4.5hours and performance metrics with confidence intervals were reported on both internal and external evaluation sets. Three board-certified neuroradiologists' DWI-FLAIR mismatch assessments, based on majority vote, yielded a sensitivity of .62, a specificity of .86, and a Fleiss' kappa of .46 when used to classify TSS. The deep learning method performed similarly to radiologists and outperformed previously reported methods, with the best model achieving an average evaluation accuracy, sensitivity, and specificity of .726, .712, and .741, respectively, on an internal cohort and .724, .757, and .679, respectively, on an external cohort. Our model achieved higher generalization performance on external evaluation datasets than the current state-of-the-art for TSS classification. These results demonstrate the potential of automatic assessment of onset time from imaging without the need for expertly trained radiologists.

Comparison between 3D-FLAIR and 3D-real IR MRI sequences with visual classification method in the imaging of endolymphatic hydrops in Meniere's disease

BackgroundClinically, the evidence of endolymphatic hydrops (EH) in Meniere's disease (MD) primarily relies on audiological examinations, such as glycerol tests and electrocochleography, to suggest the presence of EH indirectly. However, these techniques lack sensitivity and specificity, and they do not sufficiently assess the degree of EH. This study aims to explore the application of three-dimensional fluid-attenuated inversion recovery (3D-FLAIR) and three-dimensional real inversion recovery (3D-real IR) sequence imaging of EH in MD and to assess the image quality and grading of EH. MethodsThe study included 50 patients with definite MD. The 3D-FLAIR and 3D-real IR sequence images were performed 24 h after bilateral intratympanic injection of gadolinium. The image quality of both sequences was reviewed by two experienced radiologists. The vestibular and cochlear EH grades of both sequences were reviewed by two experienced otologists using a visual grading method. The Cohen's kappa and Pearson tests were used to analyze the data. ResultsThe reliability of image quality between the two radiologists was excellent (0.7 < kappa < 0.9). There were significant statistical differences in the image quality between the 3D-real IR and 3D-FLAIR sequences (p = 0.023 and p = 0.035, respectively). The reliability for the grading of vestibular and cochlear EH between the two otologists was excellent (0.7 < kappa < 0.9). The 3D-real IR sequence detected more severe hydrops than did the 3D-FLAIR sequence (p < 05). ConclusionThe image quality of the 3D-real IR sequence is better than that of the 3D-FLAIR sequence, and there are differences in the vestibular and cochlear EH grades of both sequences. The sensitivity of the 3D-real IR sequence in the cochlea is higher. The method of visual grading can be applied to both technologies when combined with 3D-real IR.

The Application of DTCWT on MRI-Derived Radiomics for Differentiation of Glioblastoma and Solitary Brain Metastases.

Background: While magnetic resonance imaging (MRI) is the imaging modality of choice for the evaluation of patients with brain tumors, it may still be challenging to differentiate glioblastoma multiforme (GBM) from solitary brain metastasis (SBM) due to their similar imaging features. This study aimed to evaluate the features extracted of dual-tree complex wavelet transform (DTCWT) from routine MRI protocol for preoperative differentiation of glioblastoma (GBM) and solitary brain metastasis (SBM). Methods: A total of 51 patients were recruited, including 27 GBM and 24 SBM patients. Their contrast-enhanced T1-weighted images (CET1WIs), T2 fluid-attenuated inversion recovery (T2FLAIR) images, diffusion-weighted images (DWIs), and apparent diffusion coefficient (ADC) images were employed in this study. The statistical features of the pre-transformed images and the decomposed images of the wavelet transform and DTCWT were utilized to distinguish between GBM and SBM. Results: The support vector machine (SVM) showed that DTCWT images have a better accuracy (82.35%), sensitivity (77.78%), specificity (87.50%), and the area under the curve of the receiver operating characteristic curve (AUC) (89.20%) than the pre-transformed and conventional wavelet transform images. By incorporating DTCWT and pre-transformed images, the accuracy (86.27%), sensitivity (81.48%), specificity (91.67%), and AUC (93.06%) were further improved. Conclusions: Our studies suggest that the features extracted from the DTCWT images can potentially improve the differentiation between GBM and SBM.

A Case of Rheumatoid Meningitis

Rheumatoid meningitis, a very rare complication, is not well-recognised, and there are few reports describing its treatment. We report the case of a 74-year-old Japanese woman who was diagnosed with rheumatoid meningitis by characteristic brain magnetic resonance imaging (MRI) and was successfully treated with glucocorticoids. We observed fluid-attenuated inversion recovery and diffusion-weighted imaging hyperintensity, which had a meningeal gadolinium-enhancing characteristic of rheumatoid meningitis. We suggest that it is possible to diagnose this disease based on characteristic MRI findings and treat patients early using glucocorticoids.

Associations between cerebral blood flow and progression of white matter hyperintensity in community-dwelling adults: a longitudinal cohort study.

BackgroundWhite matter hyperintensity (WMH) is prevalent in elderly populations. Ischemia is characterized by a decline in cerebral blood flow (CBF) and may play a key role in the pathogenesis of WMH. However, the association between CBF reduction and WMH progression remains controversial. This study aimed to investigate the association between CBF and the progression of WMH at a 2-year follow-up of community-based, asymptomatic adults in a longitudinal cohort study across the lifespan.MethodsAsymptomatic adults who participated in a community-based study were recruited and underwent brain structural and perfusion magnetic resonance imaging (MRI) at baseline and at a 2-year follow-up visit. The CBF was measured on pseudo-continuous arterial spin-labeling (pCASL) MRI. The WMH was evaluated on T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) images. Tissue segmentation was conducted on T1-weighted (T1W) images to derive binary masks of gray matter and normal-appearing white matter. Linear mixed effect models were conducted to analyze the cross-sectional and longitudinal associations between CBF and WMH.ResultsA total of 229 adults (mean age 57.3±12.6 years; 94 males) were enrolled at baseline, among whom 84 participants (mean age 54.1±11.9 years; 41 males) completed a follow-up visit with a mean time interval of 2.77±0.44 years. At baseline, there was a decreasing trend in gray matter (GM) CBF with an increase of WMH burden (P=0.063), but this association was attenuated after adjusting for age (P=0.362). In the longitudinal analysis, baseline WMH volume was significantly associated with the reduction of perfusion in GM [coefficient =−1.96, 95% confidence interval (CI): −3.25 to −0.67; P=0.004] and normal appearing white matter (coefficient =−0.99, 95% CI: −1.66 to −0.31; P=0.005) during follow-up. On the contrary, neither baseline CBF in GM (P=0.888) nor normal appearing white matter (P=0.850) was associated with WMH progression. In addition, CBF changes within WMH were significantly associated with both baseline (coefficient =−0.014, 95% CI: −0.025 to −0.003; P=0.017) and progression (coefficient =−1.01, 95% CI: −1.81 to −0.20; P=0.015) of WMH volume.ConclusionsA WMH burden was not found to be directly associated with cortex perfusion at baseline due to the effects of age on both CBF and WMH. However, baseline WMH volume could predict the reduction of perfusion.

Estimating Perfusion Deficits in Acute Stroke Patients Without Perfusion Imaging.

Perfusion weighted imaging (PWI) is critical for determining whether stroke patients presenting in an extended time window are candidates for mechanical thrombectomy. However, PWI is not always available. Fluid-attenuated inversion recovery hyperintense vessels (FHVs) are seen in patients with a PWI lesion. We investigated whether a scale measuring the extent FHV could serve as a surrogate for PWI to determine eligibility for thrombectomy. The National Institutes of Health (NIH) FHV score was developed to quantify the burden of FHV and applied to magnetic resonance imaging scans of stroke patients with fluid-attenuated inversion recovery and perfusion imaging. The NIH-FHV was combined with the diffusion weighted image volume to estimate the diffusion-perfusion mismatch ratio. Linear regression was used to compare PWI volumes and mismatch ratios with estimates from the NIH-FHV score. Receiver operating characteristic analysis was used to test the ability of the NIH-FHV score to identify a significant mismatch. There were 101 patients included in the analysis, of whom 78% had a perfusion deficit detected on PWI with a mean lesion volume of 47 (±59) mL. The NIH-FHV score was strongly associated with the PWI lesion volume (P<0.001; R2=0.32; β-coefficient, 0.57). When combined with diffusion weighted image lesion volume, receiver operating characteristic analysis testing the ability to detect a mismatch ratio ≥1.8 using the NIH-FHV score resulted in an area under the curve of 0.94. The NIH-FHV score provides an estimate of the PWI lesion volume and, when combined with diffusion weighted imaging, may be helpful when trying to determine whether there is a clinically relevant diffusion-perfusion mismatch in situations where perfusion imaging is not available. Further studies are needed to validate this approach.

Automatic segmentation of the brain stroke lesions from MR flair scans using improved U-net framework

BackgroundMagnetic resonance imaging (MRI) can reliably diagnose ischemic stroke. Stroke is an acute vascular illness of the brain that can lead to long-term death and disability. The majority of stroke patients have acute ischemic lesions. These stroke lesions are treatable underneath correct diagnosing and treatment. Despite the fact that fluid attenuation inversion recovery (FLAIR) images are susceptible to detecting these types of lesions, clinicians find it difficult to locate and measure them manually. New methodIn this research, we present a methodology for autonomously segmenting stroke lesions in the FLAIR modality images. A deep supervised U-Net architecture is used in our proposed network, which incorporates Blocks made up of five parallel layers. Results & comparison with existing methodsWe assessed the proposed framework On the MICCAI 2015 Ischemic Stroke Lesion Segmentation dataset (ISLES2015) Challenge. In conclusion, the dice coefficient attained a mean accuracy of 0.89. ConclusionsExperiments show that compared to traditional machine learning methods, proposed method shows better performance. The experiment results have already confirmed that the proposed U-Net model is a better tool for dealing with segmentation problems that are related to others on similar datasets.

Decoding the Heterogeneity of Malignant Gliomas by PET and MRI for Spatial Habitat Analysis of Hypoxia, Perfusion, and Diffusion Imaging: A Preliminary Study

BackgroundTumor heterogeneity poses major clinical challenges in high-grade gliomas (HGGs). Quantitative radiomic analysis with spatial tumor habitat clustering represents an innovative, non-invasive approach to represent and quantify tumor microenvironment heterogeneity. To date, habitat imaging has been applied mainly on conventional magnetic resonance imaging (MRI), although virtually extendible to any imaging modality, including advanced MRI techniques such as perfusion and diffusion MRI as well as positron emission tomography (PET) imaging.ObjectivesThis study aims to evaluate an innovative PET and MRI approach for assessing hypoxia, perfusion, and tissue diffusion in HGGs and derive a combined map for clustering of intra-tumor heterogeneity.Materials and MethodsSeventeen patients harboring HGGs underwent a pre-operative acquisition of MR perfusion (PWI), Diffusion (dMRI) and 18F-labeled fluoroazomycinarabinoside (18F-FAZA) PET imaging to evaluate tumor vascularization, cellularity, and hypoxia, respectively. Tumor volumes were segmented on fluid-attenuated inversion recovery (FLAIR) and T1 post-contrast images, and voxel-wise clustering of each quantitative imaging map identified eight combined PET and physiologic MRI habitats. Habitats’ spatial distribution, quantitative features and histopathological characteristics were analyzed.ResultsA highly reproducible distribution pattern of the clusters was observed among different cases, particularly with respect to morphological landmarks as the necrotic core, contrast-enhancing vital tumor, and peritumoral infiltration and edema, providing valuable supplementary information to conventional imaging. A preliminary analysis, performed on stereotactic bioptic samples where exact intracranial coordinates were available, identified a reliable correlation between the expected microenvironment of the different spatial habitats and the actual histopathological features. A trend toward a higher representation of the most aggressive clusters in WHO (World Health Organization) grade IV compared to WHO III was observed.ConclusionPreliminary findings demonstrated high reproducibility of the PET and MRI hypoxia, perfusion, and tissue diffusion spatial habitat maps and correlation with disease-specific histopathological features.

Segmentation and volume quantification of MR Images for the detection and monitoring multiple sclerosis progression.

Multiple Sclerosis (MS) lesions detection and disease's progression monitoring at the same time, play an important role. The purpose of this research is to demonstrate a method for detecting MS plaques and volume estimation from MR Images for monitoring the progression of the disease and the brain atrophy caused. In the proposed research, a clustering-based method is utilized in order to delineate MS plaques in brain, based on anatomical information, brain geometry and lesion features. In addition to volumetric information concerning lesions and whole brain volume, volume quantification is employed to estimate MS atrophy by measuring Brain Parenchymal Fraction (BPF). In the present study, Fluid Attenuated Inversion Recovery (FLAIR) images were utilized for the detection of MS lesions and BPF evaluation, while Tl-weighted MR Images utilized in volume estimation. 30 MS patients were included in a dataset consisted of 3D FLAIR and T1-weighted MR images in order to evaluate the proposed technique. MRI scans performed in two different clinical visits, a baseline and a visit after 6 months. The results extracted in segmentation of MS lesions in terms of sensitivity is 73.80 %. The BPF at baseline estimated to 0.82 ± 0.01, and at 1stfollow up, 0.83 ± 0.01. Finally, the brain volume loss between baseline and after 6 months is 0.4%.

Pituicytoma: A Report of Two Cases and Literature Review

Abstract Introduction Pituicytoma is a rare form of primary benign tumor of the neurohypophysis and infundibulum. There have been 140 reported cases in world literature that could be found in our search. Here we discuss about the presentation, radiological features, treatment, histopathology, and immunohistochemical markers of two cases, along with a brief review of literature. Materials and Methods A 48-year-old female presented with headache and painless visual loss for 4 years. Examination revealed bitemporal hemianopia and normal fundus. Hormonal profile was normal. Magnetic resonance imaging (MRI) showed 2.6 × 2.8 × 3.3 cm, thin-walled, cystic sellar and suprasellar lesions stretching the optic chiasm superiorly. Endoscopic transnasal, transsphenoidal gross total excision was done. Histopathological examination (HPE) and immunohistochemistry (IHC) were suggestive of pituicytoma.A 55-year-old male presented with intermittent headache and vomiting for 3 months. Examination was unremarkable. Hormonal profile was normal. MRI showed lobulated mass of size 1.4 × 1.9 × 2.0 cm, arising from anterior pituitary and extending to the suprasellar cistern. Lesion was hyperintense in T1-weighted and heterointense in T2-weighted images, and Fluid attenuated inversion recovery images (FLAIR) with homogenous contrast enhancement pushed the optic chiasm superiorly. Endoscopic transnasal partial excision of the lesion was done. HPE and IHC were suggestive of pituicytoma. Results Total number of cases was 142, of which 71 were males (50%) and 71 were females (50%). Age ranged between 7 and 83 years (mean 49.2, median 48). Brain imaging was available in 110 cases. Size of the lesion varied from 4 to 72 mm (mean 27 mm). Location was either pure sellar (24.3%) or with extension into the suprasellar cistern (34.3%) and/or the cavernous sinuses (7.1%). Lesions in MRI were solid (84%) with few areas of cystic changes, while contrast enhancement of solid portion was in 92% (homogenous in 80% and heterogeneous in 20%) of the cases. One case showed features of hemorrhage while no case showed calcification. Treatment was primarily surgical (120 out of 130 cases): transsphenoidal excision in 70% of the cases and craniotomy and excision in 30%. Preoperative embolization of arterial feeders was done in two cases and postoperative radiation was given in one case. Gross total resection was done in 46.3%, subtotal resection in 40%, partial resection in 12.6%, and biopsy was done in 1.1% of the cases in the study. Follow-up ranged from 2 to 134 months (average 31.2, median 19). Recurrence or regrowth was documented in 23 patients, treatment in 18 patients, and resurgery was done in 12 cases, followed by radiotherapy in 5. In six cases, radiotherapy was used in isolation. Improvement in the visual deficit was seen in 26 patients, and remained unchanged in 3. Among the patients with preoperative endocrine dysfunction, 10 improved while 11 suffered a worsening. Ten patients had new postoperative endocrine alteration. Only one fatal case was registered. Conclusion Pituicytoma is a rare World Health Organization grade I tumor of the sellar and suprasellar region, presenting with varied clinical, radiological, and hormonal features. Histopathology and immunohistochemistry form the mainstay in diagnosis. Surgical excision with transsphenoidal approach is ideal. Prognosis in general is good after surgical resection alone or in conjunction with radiotherapy.

Association of Stroke Lesion Pattern and White Matter Hyperintensity Burden With Stroke Severity and Outcome.

To examine whether high white matter hyperintensity (WMH) burden is associated with greater stroke severity and worse functional outcomes in lesion pattern-specific ways. MR neuroimaging and NIH Stroke Scale data at index stroke and the modified Rankin Scale (mRS) score at 3-6 months after stroke were obtained from the MRI-Genetics Interface Exploration study of patients with acute ischemic stroke (AIS). Individual WMH volume was automatically derived from fluid-attenuated inversion recovery images. Stroke lesions were automatically segmented from diffusion-weighted imaging (DWI) images, parcellated into atlas-defined brain regions and further condensed to 10 lesion patterns via machine learning-based dimensionality reduction. Stroke lesion effects on AIS severity and unfavorable outcomes (mRS score >2) were modeled within purpose-built Bayesian linear and logistic regression frameworks. Interaction effects between stroke lesions and a high vs low WMH burden were integrated via hierarchical model structures. Models were adjusted for age, age2, sex, total DWI lesion and WMH volumes, and comorbidities. Data were split into derivation and validation cohorts. A total of 928 patients with AIS contributed to acute stroke severity analyses (age: 64.8 [14.5] years, 40% women) and 698 patients to long-term functional outcome analyses (age: 65.9 [14.7] years, 41% women). Stroke severity was mainly explained by lesions focused on bilateral subcortical and left hemispherically pronounced cortical regions across patients with both a high and low WMH burden. Lesions centered on left-hemispheric insular, opercular, and inferior frontal regions and lesions affecting right-hemispheric temporoparietal regions had more pronounced effects on stroke severity in case of high compared with low WMH burden. Unfavorable outcomes were predominantly explained by lesions in bilateral subcortical regions. In difference to the lesion location-specific WMH effects on stroke severity, higher WMH burden increased the odds of unfavorable outcomes independent of lesion location. Higher WMH burden may be associated with an increased stroke severity in case of stroke lesions involving left-hemispheric insular, opercular, and inferior frontal regions (potentially linked to language functions) and right-hemispheric temporoparietal regions (potentially linked to attention). Our findings suggest that patients with specific constellations of WMH burden and lesion locations may have greater benefits from acute recanalization treatments. Future clinical studies are warranted to systematically assess this assumption and guide more tailored treatment decisions.

Relation Between Sex, Menopause, and White Matter Hyperintensities: The Rhineland Study.

Mounting evidence implies that there are sex differences in white matter hyperintensity (WMH) burden in older people. Questions remain regarding possible differences in WMH burden between men and women of younger age, sex-specific age trajectories and effects of (un)controlled hypertension, and the effect of menopause on WMH. Therefore, our aim was to investigate these sex differences and age dependencies in WMH load across the adult life span and to examine the effect of menopause. This cross-sectional analysis was based on participants of the population-based Rhineland Study (30-95 years) who underwent brain MRI. We automatically quantified WMH using T1-weighted, T2-weighted, and fluid-attenuated inversion recovery images. Menopausal status was self-reported. We examined associations of sex and menopause with WMH load (logit-transformed and z-standardized) using linear regression models while adjusting for age, age-squared, and vascular risk factors. We checked for an age × sex and (un)controlled hypertension × sex interaction and stratified for menopausal status comparing men with premenopausal women (persons aged 59 years or younger), men with postmenopausal women (persons aged 45 years or older), and premenopausal with postmenopausal women (age range 45-59 years). Of 3,410 participants with a mean age of 54.3 years (SD = 13.7), 1,973 (57.9%) were women, of which 1,167 (59.1%) were postmenopausal. We found that the increase in WMH load accelerates with age and in a sex-dependent way. Premenopausal women and men of similar age did not differ in WMH burden. WMH burden was higher and accelerated faster in postmenopausal women compared with men of similar age. In addition, we observed changes related to menopause, in that postmenopausal women had more WMH than premenopausal women of similar age. Women with uncontrolled hypertension had a higher WMH burden compared with men, which was unrelated to menopausal status. After menopause, women displayed a higher burden of WMH than contemporary premenopausal women and men and an accelerated increase in WMH. Sex-specific effects of uncontrolled hypertension on WMH were not related to menopause. Further studies are warranted to investigate menopause-related physiologic changes that may inform on causal mechanisms involved in cerebral small vessel disease progression.

Spatial heterogeneity of edema region uncovers survival-relevant habitat of Glioblastoma

ObjectivesThe composition and extent of edema (ED) region can reflect the aggressive degree of glioblastoma (GBM). Investigating its heterogeneity pattern and identifying the high-risk habitat may provide important prognostic information. MethodsWe prospectively collected 122 GBM patients from the Cancer Imaging Archive (TCIA) and 65 GBM patients from the local institution for the training cohort and external test cohort respectively. The signal intensities of ED region from T1-weighted contrast-enhanced images (T1CE) and T2-weighted fluid-attenuated inversion recovery images (FLAIR) were pooled together from each patient as a global matrix. Then, K-means clustering was applied, which could segment ED regions into several habitats (i.e., subregions). A group of radiomics features were extracted and radiomics signatures (RadScores) were derived. The high-risk habitat was identified and evaluated in light of the prognostic at the intra-regional, inter-regional, and model levels. Molecular analysis was also conducted to investigate the potential of the high-risk habitat in complementing biological information. ResultsAfter three levels comparison, the high-risk habitat was determined. When combing with the RadScores of enhanced tumor (ET), the concordance index (C-index) was leveraged from 0.658 to 0.677. When combining with clinical factors and RadScores of ET, the C-index increased to 0.770. For molecular analysis, we observed a more significant difference among groups in survival prediction after uniting MGMT methylation status and the high-risk habitat signature. ConclusionsThis study demonstrates that investigating the spatial heterogeneity of ED and identifying the high-risk habitat within it may provide more references for GBM treatment and prognosis studies.