Fluid-attenuated Inversion Recovery Images Research Articles

Synthesizing high-resolution magnetic resonance imaging using parallel cycle-consistent generative adversarial networks for fast magnetic resonance imaging.

The common practice in acquiring the magnetic resonance (MR) images is to obtain two-dimensional (2D) slices at coarse locations while keeping the high in-plane resolution in order to ensure enough body coverage while shortening the MR scan time. The aim of this study is to propose a novel method to generate HR MR images from low-resolution MR images along the longitudinal direction. In order to address the difficulty of collecting paired low- and high-resolution MR images in clinical settings and to gain the advantage of parallel cycle consistent generative adversarial networks (CycleGANs) in synthesizing realistic medical images, we developed a parallel CycleGANs based method using a self-supervised strategy. The proposed workflow consists of two parallely trained CycleGANs to independently predict the HR MR images in the two planes along the directions that are orthogonal to the longitudinal MR scan direction. Then, the final synthetic HR MR images are generated by fusing the two predicted images. MR images, including T1-weighted (T1), contrast enhanced T1-weighted (T1CE), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR), of the multimodal brain tumor segmentation challenge 2020 (BraTS2020) dataset were processed to evaluate the proposed workflow along the cranial-caudal (CC), lateral, and anterior-posterior directions. Institutional collected MR images were also processed for evaluation of the proposed method. The performance of the proposed method was investigated via both qualitative and quantitative evaluations. Metrics of normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), edge keeping index (EKI), structural similarity index measurement (SSIM), information fidelity criterion (IFC), and visual information fidelity in pixel domain (VIFP) were calculated. It is shown that the proposed method can generate HR MR images visually indistinguishable from the ground truth in the investigations on the BraTS2020 dataset. In addition, the intensity profiles, difference images and SSIM maps can also confirm the feasibility of the proposed method for synthesizing HR MR images. Quantitative evaluations on the BraTS2020 dataset shows that the calculated metrics of synthetic HR MR images can all be enhanced for the T1, T1CE, T2, and FLAIR images. The enhancements in the numerical metrics over the low-resolution and bi-cubic interpolated MR images, as well as those genearted with a comparative deep learning method, are statistically significant. Qualitative evaluation of the synthetic HR MR images of the clinical collected dataset could also confirm the feasibility of the proposed method. The proposed method is feasible to synthesize HR MR images using self-supervised parallel CycleGANs, which can be expected to shorten MR acquisition time in clinical practices.

Oxygen Metabolic Stress and White Matter Injury in Patients With Cerebral Small Vessel Disease.

Chronic hypoxia-ischemia is a putative mechanism underlying the development of white matter hyperintensities (WMH) and microstructural disruption in cerebral small vessel disease. WMH fall primarily within deep white matter (WM) watershed regions. We hypothesized that elevated oxygen extraction fraction (OEF), a signature of hypoxia-ischemia, would be detected in the watershed where WMH density is highest. We further hypothesized that OEF would be elevated in regions immediately surrounding WMH, at the leading edge of growth. In this cross-sectional study conducted from 2016 to 2019 at an academic medical center in St Louis, MO, participants (age >50) with a range of cerebrovascular risk factors underwent brain magnetic resonance imaging using pseudocontinuous arterial spin labeling, asymmetric spin echo, fluid-attenuated inversion recovery and diffusion tensor imaging to measure cerebral blood flow (CBF), OEF, WMH, and WM integrity, respectively. We defined the physiologic watershed as a region where CBF was below the 10th percentile of mean WM CBF in a young healthy cohort. We conducted linear regression to evaluate the relationship between CBF and OEF with structural and microstructural WM injury defined by fluid-attenuated inversion recovery WMH and diffusion tensor imaging, respectively. We conducted ANOVA to determine if OEF was increased in proximity to WMH lesions. In a cohort of 42 participants (age 50-80), the physiologic watershed region spatially overlapped with regions of highest WMH lesion density. As CBF decreased and OEF increased, WMH density increased. Elevated watershed OEF was associated with greater WMH burden and microstructural disruption, after adjusting for vascular risk factors. In contrast, WM and watershed CBF were not associated with WMH burden or microstructural disruption. Moreover, OEF progressively increased while CBF decreased, in concentric contours approaching WMH lesions. Chronic hypoxia-ischemia in the watershed region may contribute to cerebral small vessel disease pathogenesis and development of WMH. Watershed OEF may hold promise as an imaging biomarker to identify individuals at risk for cerebral small vessel disease progression.

Using of Laplacian Re-decomposition image fusion algorithm for glioma grading with SWI, ADC, and FLAIR images

Abstract Introduction: Based on the tumor’s growth potential and aggressiveness, glioma is most often classified into low or high-grade groups. Traditionally, tissue sampling is used to determine the glioma grade. The aim of this study is to evaluate the efficiency of the Laplacian Re-decomposition (LRD) medical image fusion algorithm for glioma grading by advanced magnetic resonance imaging (MRI) images and introduce the best image combination for glioma grading. Material and methods: Sixty-one patients (17 low-grade and 44 high-grade) underwent Susceptibility-weighted image (SWI), apparent diffusion coefficient (ADC) map, and Fluid attenuated inversion recovery (FLAIR) MRI imaging. To fuse different MRI image, LRD medical image fusion algorithm was used. To evaluate the effectiveness of LRD in the classification of glioma grade, we compared the parameters of the receiver operating characteristic curve (ROC). Results: The average Relative Signal Contrast (RSC) of SWI and ADC maps in high-grade glioma are significantly lower than RSCs in low-grade glioma. No significant difference was detected between low and high-grade glioma on FLAIR images. In our study, the area under the curve (AUC) for low and high-grade glioma differentiation on SWI and ADC maps were calculated at 0.871 and 0.833, respectively. Conclusions: By fusing SWI and ADC map with LRD medical image fusion algorithm, we can increase AUC for low and high-grade glioma separation to 0.978. Our work has led us to conclude that, by fusing SWI and ADC map with LRD medical image fusion algorithm, we reach the highest diagnostic accuracy for low and high-grade glioma differentiation and we can use LRD medical fusion algorithm for glioma grading.

White matter hyperintensities and mild behavioural impairment: Findings from the MEMENTO Study

AbstractBackgroundMild behavioural impairment (MBI) is a neurobehavioural syndrome characterized by the onset and persistence of neuropsychiatric symptoms (NPS) in later life. While the ATN framework provides a robust overview of Alzheimer’s pathology, it does not fully capture the impact of small‐vessel cerebrovascular disease in the pathogenesis of dementia. White matter hyperintensities (WMH) may independently contribute to the development of Alzheimer’s dementia and predict risk of incident dementia in mild cognitive impairment (MCI). Few studies have established the link between NPS and WMH in MCI populations.MethodWe included 760 participants from the French MEMENTO study with MCI and WMH data. WMH volumetry on T2‐weighted fluid attenuated inversion recovery images was identified using WHASA software, visually verified with the Fazekas and Schmidt scale, and expressed in cm3. Mean age was 72.7 (SD 8.0) and 57.9% of participants were female. Neuropsychiatric Inventory items were transformed to MBI domains using a published algorithm. MBI status was based on symptom persistence: NPS at both baseline and 6‐months was considered MBI+ (n = 227), whereas non‐persistent NPS (i.e., NPS at either time point but not both) or no NPS at either baseline or 6‐months was considered MBI‐ (n=549). We investigated the association between MBI status and the outcome variable, WMH, using a linear regression model adjusted for age, sex, education, Mini‐Mental Status Exam (MMSE) score, and total intracranial volume.ResultMBI was present in 29.9% of participants (n = 227). MBI+ (i.e., persistent NPS) was significantly associated with lower MMSE score (p < 0.001) and male sex (p = 0.025). Compared to MBI‐, MBI+ was associated with 9.9% higher WMH volume [p = 0.006 (95% CI 2.8% to 18%), Table 1].ConclusionIn a memory clinic sample of older adults with MCI, MBI+, operationalized as persistent NPS, is associated with higher WMH. These findings extend the evidence base linking MBI with known dementia biomarkers and raise the possibility that some forms of MBI can be caused by vascular brain injury.

Free water in T2 FLAIR white matter hyperintensity lesions

AbstractBackgroundWhite matter (WM) free water (FW) is likely associated with cerebral small vessel disease (CSVD). FW is the fraction of unconstrained water within an image voxel, which can be estimated from diffusion‐weighted images. T2‐weighted Fluid‐Attenuated Inversion Recovery (FLAIR) white matter hyperintensity (WMH) is a widely used index to assess the damages caused by CSVD. It is critical to characterize how FW content is altered in WMH lesions. In this work, we proposed a data processing framework to assess FW distributions in WMH and normal‐appearing WM as well as in different WM fiber tracts.MethodSingle‐shell diffusion‐weighted image (SS‐DWI) and T2 FLAIR image data of 133 cognitively normal (CN) adults (Table 1) were obtained from the ADNI dataset (http://adni.loni.usc.edu). FW maps were generated from SS‐DWI images using the DIPY software package (https://dipy.org/). WMH lesions were identified with the lesion segmentation toolbox implemented in SPM. We co‐registered FW and WMH maps of individual subjects in the MNI space. Then we derived group WM fiber tracts using the tract‐based spatial statistics (TBSS). Finally, we examined the localization of WMH and FW in the major WM tracts.ResultFigure 1 shows the FW map, FLAIR image, and WMH lesion from a representative subject. Figure 2 shows group average and individual FW distributions in WMH and normal‐appearing WM. Two sample T‐Test found that FW content in WMH is significantly higher than that in normal‐appearing WM (mean FW and standard deviation FW in WMH are 0.430 and 0.112, mean FW and standard deviation FW in normal‐appearing WM are 0.203 and 0.06, P < 1×10‐30). TBSS based analysis showed that: 1) WMH lesions are mainly located in the corona radiata, posterior thalamic radiation, tapetum, superior fronto‐occipital fasciculus, superior longitudinal fasciculus, corpus callosum, and sagittal stratum; 2) Compared with mean FW in normal‐appearing WM, elevated FW content is mainly at the fornix, tapetum, cingulum hippocampus, superior fronto‐occipital fasciculus, corpus callosum, sagittal stratum, and posterior thalamic radiation.ConclusionWe developed an image processing pipeline to estimate FW changes in WMH lesions and normal‐appearing WM. Free water content is elevated in WMH lesions, which is likely driven by CSVD.

Abstracts from Hydrocephalus 2021: The Thirteenth Meeting of the International Society for Hydrocephalus and Cerebrospinal Fluid Disorders

Abstracts from Hydrocephalus 2021: The Thirteenth Meeting of the International Society for Hydrocephalus and Cerebrospinal Fluid Disorders

Segmentation of Cerebral Small Vessel Diseases-White Matter Hyperintensities Based on a Deep Learning System.

Objective: Reliable quantification of white matter hyperintensities (WHMs) resulting from cerebral small vessel diseases (CSVD) is essential for understanding their clinical impact. We aim to develop and clinically validate a deep learning system for automatic segmentation of CSVD-WMH from fluid-attenuated inversion recovery (FLAIR) imaging using large multicenter data.Method: A FLAIR imaging dataset of 1,156 patients diagnosed with CSVD associated WMH (median age, 54 years; 653 males) obtained between September 2018 and September 2019 from Beijing Tiantan Hospital was retrospectively analyzed in this study. Locations of CSVD-WMH on the FLAIR scans were manually marked by two experienced neurologists. Using the manually labeled data of 996 patients (development set), a U-shaped novel 2D convolutional neural network (CNN) architecture was trained for automatic segmentation of CSVD-WMH. The segmentation performance of the network was evaluated with per pixel and lesion level dice scores using an independent internal test set (n = 160) and a multi-center external test set (n = 90, three medical centers). The clinical suitability of the segmentation results, classified as acceptable, acceptable with minor revision, acceptable with major revision, and not acceptable, was analyzed by three independent neuroradiologists. The inter-neuroradiologists agreement rate was assessed by the Kendall-W test.Results: On the internal and external test sets, the proposed CNN architecture achieved per pixel and lesion level dice scores of 0.72 (external test set), and they were significantly better than the state-of-the-art deep learning architectures proposed for WMH segmentation. In the clinical evaluation, neuroradiologists observed the segmentation results for 95% of the patients were acceptable or acceptable with a minor revision.Conclusions: A deep learning system can be used for automated, objective, and clinically meaningful segmentation of CSVD-WMH with high accuracy.

Autoimmune basal ganglia encephalitis associated with anti-recoverin antibodies: A case report

Autoimmune basal ganglia encephalitis causes neurological symptoms such as parkinsonism associated with basal ganglia lesions. Here, we report a case of autoimmune basal ganglia encephalitis without retinal lesions or malignancy harboring anti-recoverin antibodies. The patient was a 67-year-old Japanese woman who developed anorexia, parkinsonism, and disturbance of consciousness 7 days before admission. Brain magnetic resonance imaging showed hyperintense bilateral basal ganglia lesions on fluid-attenuated inversion recovery images. 18F-fluorodeoxyglucose-positron emission tomography showed no malignancy in the trunk, and dopamine transporter single-photon emission computed tomography with dopamine transporters revealed reduced radiotracer uptake in the basal ganglia. Further, anti-recoverin IgG antibodies were detected in serum immunoblot. Based on the clinical and imaging findings, the patient was diagnosed with autoimmune basal ganglia encephalitis with anti-recoverin antibodies and administered high-dose immunoglobulins (HD-IVIG), which led to an improvement in clinical symptoms. Anti-recoverin antibodies are paraneoplastic antibodies that explicitly bind to Ca2+-binding proteins in the retina and cause retinopathy. This pathological sequence is defined as cancer-associated retinopathy (CAR). However, in our case, autoimmune basal ganglia encephalitis developed without CAR syndrome or malignancy. Clinicians should be aware of the possibility of autoimmune basal ganglia encephalitis showing anti-recoverin antibodies but no CAR syndrome or malignancy, which should be treated with HD-IVIG therapy.

The Clinical Significance of the Hyperintense Acute Reperfusion Marker Sign in Subacute Infarction Patients

Purpose: The hyperintense acute reperfusion marker (HARM) is characterized by the delayed enhancement of the subarachnoid or subpial space observed on postcontrast fluid-attenuated inversion recovery (FLAIR) images, and is considered a cerebral reperfusion marker for various brain disorders, including infarction. In this study, we evaluated the cerebral distribution patterns of HARM for discriminating between an enhancing subacute infarction and an enhancing mass located in the cortex and subcortical white matter. Materials and methods: We analyzed consecutive patients who experienced a subacute ischemic stroke, were hospitalized, and underwent conventional brain magnetic resonance imaging including postcontrast FLAIR within 14 days from symptom onset, as well as those who had lesions corresponding to a clinical sign detected by diffusion-weighted imaging and postcontrast T1-weighted imaging between May 2019 and May 2021. A total of 199 patients were included in the study. Of them, 94 were finally included in the subacute infarction group. During the same period, 76 enhancing masses located in the cortex or subcortical white matter, which were subcategorized as metastasis, malignant glioma, and lymphoma, were analyzed. We analyzed the overall incidence of HARM in subacute ischemic stroke cases, and compared the enhancement patterns between cortical infarctions and cortical masses. Results: Among 94 patients with subacute stroke, 78 patients (83%) presented HARM, and among 76 patients with subcortical masses, 48 patients (63%) presented peripheral rim enhancement. Of 170 subcortical enhancing lesions, 88 (51.8%) showed HARM, and 78 (88.6%) were determined to be subacute infarction. Among 94 patients with subacute stroke, 48 patients (51%) had diffusion restrictions, and HARM was found in 39 patients (81.2%). Of the 46 patients (49%) without diffusion restriction, 39 patients (84.8%) showed HARM. Conclusions: The presence of HARM was significantly associated with subacute infarctions. For the masses, a peripheral rim enhancement pattern was observed around the mass rather than the cerebral sulci on postcontrast FLAIR.

Radiomics for the Prediction of Epilepsy in Patients With Frontal Glioma.

ObjectiveThis study was conducted in order to investigate the association between radiomics features and frontal glioma-associated epilepsy (GAE) and propose a reliable radiomics-based model to predict frontal GAE.MethodsThis retrospective study consecutively enrolled 166 adult patients with frontal glioma (111 in the training cohort and 55 in the testing cohort). A total 1,130 features were extracted from T2 fluid-attenuated inversion recovery images, including first-order statistics, 3D shape, texture, and wavelet features. Regions of interest, including the entire tumor and peritumoral edema, were drawn manually. Pearson correlation coefficient, 10-fold cross-validation, area under curve (AUC) analysis, and support vector machine were adopted to select the most relevant features to build a clinical model, a radiomics model, and a clinical–radiomics model for GAE. The receiver operating characteristic curve (ROC) and AUC were used to evaluate the classification performance of the models in each cohort, and DeLong’s test was used to compare the performance of the models. A two-sided t-test and Fisher’s exact test were used to compare the clinical variables. Statistical analysis was performed using SPSS software (version 22.0; IBM, Armonk, New York), and p <0.05 was set as the threshold for significance.ResultsThe classification accuracy of seven scout models, except the wavelet first-order model (0.793) and the wavelet texture model (0.784), was <0.75 in cross-validation. The clinical–radiomics model, including 17 magnetic resonance imaging-based features selected among the 1,130 radiomics features and two clinical features (patient age and tumor grade), achieved better discriminative performance for GAE prediction in both the training [AUC = 0.886, 95% confidence interval (CI) = 0.819–0.940] and testing cohorts (AUC = 0.836, 95% CI = 0.707–0.937) than the radiomics model (p = 0.008) with 82.0% and 78.2% accuracy, respectively.ConclusionRadiomics analysis can non-invasively predict GAE, thus allowing adequate treatment of frontal glioma. The clinical–radiomics model may enable a more precise prediction of frontal GAE. Furthermore, age and pathology grade are important risk factors for GAE.

Is There a Role for Surgical Resection of Multifocal Glioblastoma? A Retrospective Analysis of 100 Patients.

Glioblastoma with multiple localizations (mGBMs) can be defined as multifocal, where enhancing lesions present a connection visible on magnetic resonance imaging fluid-attenuated inversion recovery imaging, or multicentric, in the absence of a clear dissemination pathway. To evaluate the role of the extent of resection (EOR) in the treatment of mGBMs and its correlation with overall survival (OS) and progression free survival (PFS). One hundred patients with mGBMs were treated at our Institution between 2009 and 2019. Clinical, radiological, and follow-up data were collected. EOR of the contrast-enhancing part of lesions was classified as gross total resection (GTR, absence of tumor remnant), subtotal resection (STR, residual tumor<30%of the initial mass), partial resection (PR, residual tumor>30%of the initial mass), and needle or open biopsy (residual tumor>75%of the initial mass). Approximately 15% of patients underwent GTR, 14% STR, 32% PR, and 39% biopsy. Median OS was 17 mo for GTR, 11 mo for STR, 7 mo for PR, and 5 mo for biopsy. Greater EOR was associated with a significantly longer OS than biopsy. GTR and STR were associated with a longer PFS in Kaplan-Meier survival analyses. After adjusting for age, Karnofsky performance status (KPS), number of lesions, and adjunctive therapy in multivariable Cox regression analyses, GTR, STR, and PR were still associated with OS, but only GTR remained associated with PFS. Our study suggests that EOR may positively influence survival of patients with mGBM. Surgical resection can be a reasonable option when performance and access to adjuvant treatment can be preserved.

Hyperintense posterior cerebral artery sign in patients with reversible cerebral vasoconstriction syndrome.

Background: This study investigated hyperintense vessel signs (HVS) on fluid-attenuated inversion recovery imaging in the P1–2 portions of posterior cerebral arteries (PCAs) as a “hyperintense PCA sign” and HVS of cortical arteries. We retrospectively examined whether these signs would be useful in diagnosing reversible cerebral vasoconstriction syndrome (RCVS) in the acute phase.Methods: Eighty patients with RCVS who underwent initial magnetic resonance imaging (MRI) within 7 days of onset were included in this study. HVS and related clinical factors were examined.Results: On initial MRI of RCVS patients, hyperintense PCA sign and HVS of cortical arteries were seen in 21 cases (26%) and 38 cases (48%), respectively. In patients showing hyperintense PCA sign, vasoconstriction of the A2–3 portion was a significant clinical factor. Conversely, vasoconstriction of the M1 and P1 portions and the presence of white matter hyperintensity on initial and chronic-stage MRI were significantly associated with the presence of HVS in cortical arteries.Conclusion: Because rich collateral flow exists around PCAs, the frequency of hyperintense PCA sign is not high. However, hyperintense PCA sign findings in patients with suspected RCVS offer credible evidence of extreme flow decreases due to vasoconstriction in peripheral PCAs and other arteries associated with the collateral circulation of PCAs. Conversely, HVS in cortical arteries tend to reflect slow antegrade circulation due to vasoconstriction of peripheral vessel and major trunks. Both signs appear useful for auxiliary diagnosis of acute-phase RCVS.

Radiomics features based on T2-weighted fluid-attenuated inversion recoveryMRI predict the expression levels of CD44 and CD133 in lower grade gliomas.

Objective: To verify the association between CD44 and CD133 expression levels and the prognosis of patients with lower grade gliomas (LGGs) and constructing radiomic models to predict those two genes'expression levels before surgery. Materials & methods: Genomic data of patients with LGGand the corresponding T2-weighted fluid-attenuated inversion recovery images were downloaded from The Cancer Genome Atlasand The Cancer Imaging Archive, which were utilized for prognosis analysis, radiomic featureextraction and modelconstruction, respectively. Results & conclusion: CD44 and CD133 expression levels in LGG can significantly affect the prognosis of patients with LGG. Based on the T2-weighted fluid-attenuated inversion recovery images, the radiomic features can effectively predict the expression levels of CD44 and CD133 before surgery.

Cerebral Microbleeds and Treatment Effect of Intravenous Thrombolysis in Acute Stroke: An Analysis of the WAKE-UP Randomized Clinical Trial.

Background and ObjectivesCerebral microbleeds (CMBs) are common in patients with acute ischemic stroke and are associated with increased risk of intracerebral hemorrhage (ICH) after intravenous thrombolysis. Whether CMBs modify the treatment effect of thrombolysis is unknown.MethodsWe performed a prespecified analysis of the prospective randomized controlled multicenter Efficacy and Safety of MRI-Based Thrombolysis in Wake-Up Stroke (WAKE-UP) trial including patients with acute ischemic stroke with unknown time of symptom onset and diffusion-weighted imaging–fluid-attenuated inversion recovery mismatch on MRI receiving alteplase or placebo. Patients were screened and enrolled between September 2012 and June 2017 (with final follow-up in September 2017). Patients were randomized to treatment with IV thrombolysis with alteplase at 0.9 mg/kg body weight or placebo. CMB status (presence, number, and distribution) was assessed after study completion by 3 raters blinded to clinical information following a standardized protocol. Outcome measures were excellent functional outcome at 90 days, defined by modified Rankin Scale (mRS) score ≤1, and symptomatic ICH according to National Institutes of Neurological Disease and Stroke trial criteria 22 to 36 hours after treatment.ResultsOf 503 patients enrolled in the WAKE-UP trial, 459 (91.3%; 288 [63%] men) were available for analysis. Ninety-eight (21.4%) had at least 1 CMB on baseline imaging; 45 (9.8%) had exactly 1 CMB; 37 (8.1%) had 2 to 4 CMBs; and 16 (3.5%) had ≥5 CMBs. Presence of CMBs was associated with a nonsignificant increased risk of symptomatic ICH (11.2% vs 4.2%; adjusted odds ratio [OR] 2.32, 95% confidence interval [CI] 0.99–5.43, p = 0.052) but had no effect on functional outcome at 90 days (mRS score ≤1: 45.8% vs 50.7%; adjusted OR 0.99, 95% CI 0.59–1.64, p = 0.955). Patients receiving alteplase had better functional outcome (mRS score ≤1: 54.6% vs 44.6%, adjusted OR 1.61, 95% CI 1.07–2.43, p = 0.022) without evidence of heterogeneity in relation to CMB presence (p of the interactive term = 0.546). Results were similar for subpopulations with strictly lobar (presumed cerebral amyloid angiopathy related) or not strictly lobar CMB distribution.DiscussionIn the randomized-controlled WAKE-UP trial, we saw no evidence of reduced treatment effect of alteplase in patients with acute ischemic stroke with ≥1 CMBs. Additional studies are needed to determine the treatment effect of alteplase and its benefit-harm ratio in patients with a larger number of CMBs.Trial Registration InformationClinicalTrials.gov identifier NCT01525290; ClinicalTrialsRegister.EU identifier 2011-005906-32.Classification of EvidenceThis study provides Class II evidence that for patients with acute ischemic stroke with unknown time of onset and diffusion-weighted imaging–fluid-attenuated inversion recovery mismatch who received IV alteplase, CMBs are not significantly associated with functional outcome at 90 days.

Feasibility of Brain Imaging Using a Digital Surround Technology Body Coil: A Study Based on SRGAN-VGG Convolutional Neural Networks.

Brain imaging using conventional head coils presents several problems in routine magnetic resonance (MR) examination, such as anxiety and claustrophobic reactions during scanning with a head coil, photon attenuation caused by the MRI head coil in positron emission tomography (PET)/MRI, and coil constraints in intraoperative MRI or MRI-guided radiotherapy. In this paper, we propose a super resolution generative adversarial (SRGAN-VGG) network-based approach to enhance low-quality brain images scanned with body coils. Two types of T1 fluid-attenuated inversion recovery (FLAIR) images scanned with different coils were obtained in this study: joint images of the head-neck coil and digital surround technology body coil (H+B images) and body coil images (B images). The deep learning (DL) model was trained using images acquired from 36 subjects and tested in 4 subjects. Both quantitative and qualitative image quality assessment methods were performed during evaluation. Wilcoxon signed-rank tests were used for statistical analysis. Quantitative image quality assessment showed an improved structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) in gray matter and cerebrospinal fluid (CSF) tissues for DL images compared with B images (P <.01), while the mean square error (MSE) was significantly decreased (P <.05). The analysis also showed that the natural image quality evaluator (NIQE) and blind image quality index (BIQI) were significantly lower for DL images than for B images (P <.0001). Qualitative scoring results indicated that DL images showed an improved SNR, image contrast and sharpness (P<.0001). The outcomes of this study preliminarily indicate that body coils can be used in brain imaging, making it possible to expand the application of MR-based brain imaging.

Elevated Aortic Pulse Wave Velocity Relates to Longitudinal Gray and White Matter Changes.

To determine whether baseline aortic stiffness, measured by aortic pulse wave velocity (PWV), relates to longitudinal cerebral gray or white matter changes among older adults. Baseline cardiac magnetic resonance imaging will be used to assess aortic PWV while brain magnetic resonance imaging will be used to assess gray matter and white matter hyperintensity (WMH) volumes at baseline, 18 months, 3 years, 5 years, and 7 years. Approach and Results: Aortic PWV (m/s) was quantified from cardiac magnetic resonance. Multimodal 3T brain magnetic resonance imaging included T1-weighted imaging for quantifying gray matter volumes and T2-weighted fluid-attenuated inversion recovery imaging for quantifying WMHs. Mixed-effects regression models related baseline aortic PWV to longitudinal gray matter volumes (total, frontal, parietal, temporal, occipital, hippocampal, and inferior lateral ventricle) and WMH volumes (total, frontal, parietal, temporal, and occipital) adjusting for age, sex, race/ethnicity, education, cognitive diagnosis, Framingham stroke risk profile, APOE (apolipoprotein E)-ε4 carrier status, and intracranial volume. Two hundred seventy-eight participants (73±7 years, 58% male, 87% self-identified as non-Hispanic White, 159 with normal cognition, and 119 with mild cognitive impairment) from the Vanderbilt Memory & Aging Project (n=335) were followed on average for 4.9±1.6 years with PWV measurements occurring from September 2012 to November 2014 and longitudinal brain magnetic resonance imaging measurements occurring from September 2012 to June 2021. Higher baseline aortic PWV was related to greater decrease in hippocampal (β=-3.6 [mm3/y]/[m/s]; [95% CI, -7.2 to -0.02] P=0.049) and occipital lobe (β=-34.2 [mm3/y]/[m/s]; [95% CI, -67.8 to -0.55] P=0.046) gray matter volume over time. Higher baseline aortic PWV was related to greater increase in WMH volume over time in the temporal lobe (β=17.0 [mm3/y]/[m/s]; [95% CI, 7.2-26.9] P<0.001). All associations may be driven by outliers. In older adults, higher baseline aortic PWV related to greater decrease in gray matter volume and greater increase in WMHs over time. Because of unmet cerebral metabolic demands and microvascular remodeling, arterial stiffening may preferentially affect certain highly active brain regions like the temporal lobes. These same regions are affected early in the course of Alzheimer disease.

Automated Machine-Learning Framework Integrating Histopathological and Radiological Information for Predicting IDH1 Mutation Status in Glioma.

Diffuse gliomas are the most common malignant primary brain tumors. Identification of isocitrate dehydrogenase 1 (IDH1) mutations aids the diagnostic classification of these tumors and the prediction of their clinical outcomes. While histology continues to play a key role in frozen section diagnosis, as a diagnostic reference and as a method for monitoring disease progression, recent research has demonstrated the ability of multi-parametric magnetic resonance imaging (MRI) sequences for predicting IDH genotypes. In this paper, we aim to improve the prediction accuracy of IDH1 genotypes by integrating multi-modal imaging information from digitized histopathological data derived from routine histological slide scans and the MRI sequences including T1-contrast (T1) and Fluid-attenuated inversion recovery imaging (T2-FLAIR). In this research, we have established an automated framework to process, analyze and integrate the histopathological and radiological information from high-resolution pathology slides and multi-sequence MRI scans. Our machine-learning framework comprehensively computed multi-level information including molecular level, cellular level, and texture level information to reflect predictive IDH genotypes. Firstly, an automated pre-processing was developed to select the regions of interest (ROIs) from pathology slides. Secondly, to interactively fuse the multimodal complementary information, comprehensive feature information was extracted from the pathology ROIs and segmented tumor regions (enhanced tumor, edema and non-enhanced tumor) from MRI sequences. Thirdly, a Random Forest (RF)-based algorithm was employed to identify and quantitatively characterize histopathological and radiological imaging origins, respectively. Finally, we integrated multi-modal imaging features with a machine-learning algorithm and tested the performance of the framework for IDH1 genotyping, we also provided visual and statistical explanation to support the understanding on prediction outcomes. The training and testing experiments on 217 pathologically verified IDH1 genotyped glioma cases from multi-resource validated that our fully automated machine-learning model predicted IDH1 genotypes with greater accuracy and reliability than models that were based on radiological imaging data only. The accuracy of IDH1 genotype prediction was 0.90 compared to 0.82 for radiomic result. Thus, the integration of multi-parametric imaging features for automated analysis of cross-modal biomedical data improved the prediction accuracy of glioma IDH1 genotypes.

Technical note: A spiral fluid-attenuated inversion recovery magnetic resonance imaging technique for stereotactic radiosurgery treatment planning for trigeminal neuralgia.

Magnetic resonance imaging (MRI) is commonly used in treatment planning for stereotactic radiosurgery (SRS) of trigeminal neuralgia (TN). With current MRI techniques, the delineation of the trigeminal nerve root entry zone (REZ) may be degraded due to poor contrast and artifacts. The purpose of this work is to develop an MRI technique with better delineation of the trigeminal nerve REZ to improve SRS treatment planning for TN. A spiral fluid-attenuated inversion recovery (FLAIR) MRI technique was developed to improve image quality by improving tissue contrast, fluid suppression, artifact reduction, and signal-to-noise ratio (SNR). A concomitant-phase compensation method based on spiral gradient waveforms was implemented to minimize artifacts due to magnetic field change induced by the metal frame used in Gamma Knife treatment planning. The image quality of spiral FLAIR was assessed in four healthy volunteers. The geometric accuracy was quantitatively evaluated by registering spiral FLAIR to computed tomography (CT) images and comparing it with existing MRI techniques. The spiral FLAIR technique demonstrated better delineation of the trigeminal nerve REZ, improved tissue contrast of the brain stem, and minimized flow artifacts, compared to steady-state free precession (SSFP) MRI. Spiral FLAIR also improved fluid suppression, SNR, and artifacts, which contributed to better delineation of the trigeminal nerve REZ compared to conventional Cartesian FLAIR. The measured mean (± standard deviation) distance between spiral FLAIR and CT images is 0.98 ± 0.56mm, comparable to 0.40 ± 0.26mm in 3T T1 spoiled gradient echo (T1-SPGR), 0.59 ± 0.25mm in 3T SSFP, 0.66 ± 0.38mm in 1.5T T1-SPGR, and 0.61 ± 0.25mm in 1.5T Cartesian FLAIR. A spiral FLAIR technique with improved image quality and good geometric accuracy provides a potential alternative for treatment planning in SRS for TN patients.

The central vein sign in multiple sclerosis lesions: Susceptibility relaxation optimization from a routine MRI multiecho gradient echo sequence.

The white matter lesion central vein sign (CVS) is an emerging biomarker for multiple sclerosis (MS) differential diagnosis. Currently, CVS is detected on susceptibility weighted imaging (SWI) with suboptimal contrast. We developed an imaging method called susceptibility relaxation optimization (SRO) to improve CVS visualization. This was a retrospective study of MS patients who had MRI in June 2018 with routine 3D multiecho gradient echo (GRE) and T2-weighted fluid-attenuated inversion recovery (FLAIR) sequences. SRO and SWI images were reconstructed from GRE data. MS lesions were identified on FLAIR image. The CVS detection rate, the image quality score of CVS conspicuity (range 0-3), and central vein-to-lesion contrast were compared between SRO and SWI images. In 20 MS patients (mean age 45 ± 9 years; 15 women), SRO significantly increased CVS detection rate compared to SWI (53.3%, 274/514 vs. 32.9%, 169/514; p<.001, McNemar's test). The median image quality score for SRO was 2 compared to 1 for SWI (p<.001, Wilcoxon signed-rank test). The median overall image quality score for SRO was 7 compared to 6 for SWI (p = .003; Wilcoxon signed-rank test). Central vein-to-lesion contrast was 0.12 ± 0.12 in SRO compared to 0.031 ± 0.075 in SWI (p<.001, t-test). SRO yields better central vein contrast and increases CVS detection rate compared to SWI.

MRI-based radiomics signature and clinical factor for predicting H3K27M mutation in pediatric high-grade gliomas located in the midline of the brain.

To develop a nomogram based on MRI radiomics and clinical features for preoperatively predicting H3K27M mutation in pediatric high-grade gliomas (pHGGs) with a midline location of the brain. The institutional database was reviewed to identify patients with pHGGs with a midline location of the brain who underwent tumor biopsy with preoperative MRI scans between June 2016 and June 2021. A total of 107 patients with pHGGs, including 79 patients with H3K27M mutation, were consecutively included and randomly divided into training and test sets. Radiomics features were extracted from fluid-attenuated inversion recovery (FLAIR), diffusion-weighted (DW) and post-contrast T1-weighted images, and apparent diffusion coefficient (ADC) maps. The minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression were performed for radiomics signature construction. Clinical and radiological features were analyzed to select clinical predictors. A nomogram was then developed by incorporating the radiomics signature and selected clinical predictors. Nine radiomics features were selected to construct the radiomics signature, which showed a favorable discriminatory ability in training and test sets with an area under the curve (AUC) of 0.95 and 0.92, respectively. Ring enhancement was identified as an independent clinical predictor (p < 0.01). The nomogram, constructed with radiomics signature and ring enhancement, showed good calibration and discrimination in training and testing sets (AUC: 0.95 and 0.90 respectively). The nomogram which combined radiomics signature and ring enhancement had a satisfactory ability to predict H3K27M mutation in pHGGs with a midline of the brain. • Conventional MRI features were not powerful enough to predict H3K27M mutation status in pediatric high-grade gliomas (pHGGs) with a midline location of the brain. • An MRI-based radiomics signature showed satisfactory ability to predict H3K27M mutation status of pHGGs located in the midline of the brain. • Associating the radiomics signature with clinical factors improved predictive performance.