Fluid-attenuated Inversion Recovery Research Articles

Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2–4 glioma patients: a deep Radiomics Approach

BackgroundThe role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2–4 gliomas.MethodsThis retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data.ResultsWe comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients’ age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617].ConclusionsImaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.

Artificial intelligence for volumetric measurement of cerebral white matter hyperintensities on thick-slice fluid-attenuated inversion recovery (FLAIR) magnetic resonance images from multiple centers

We aimed to develop a new artificial intelligence software that can automatically extract and measure the volume of white matter hyperintensities (WMHs) in head magnetic resonance imaging (MRI) using only thick-slice fluid-attenuated inversion recovery (FLAIR) sequences from multiple centers. We enrolled 1092 participants in Japan, comprising the thick-slice Private Dataset. Based on 207 randomly selected participants, neuroradiologists annotated WMHs using predefined guidelines. The annotated images of participants were divided into training (n = 138) and test (n = 69) datasets. The WMH segmentation model comprised a U-Net ensemble and was trained using the Private Dataset. Two other models were trained for validation using either both thin- and thick-slice MRI datasets or the thin-slice dataset alone. The voxel-wise Dice similarity coefficient (DSC) was used as the evaluation metric. The model trained using only thick-slice MRI showed a DSC of 0.820 for the test dataset, which is comparable to the accuracy of human readers. The model trained with the additional thin-slice dataset showed only a slightly improved DSC of 0.822. This automatic WMH segmentation model comprising a U-Net ensemble trained on a thick-slice FLAIR MRI dataset is a promising new method. Despite some limitations, this model may be applicable in clinical practice.

Reversible Cytotoxic Edema in Patients with COVID-19 Associated Encephalitis Presenting Status Epilepticus

Coronavirus disease 2019 (COVID-19) is a serious infectious disease with multisystem alteration including neurological complications. COVID-19 associated encephalitis is a potentially fatal viral infection of the brain. Diffusion-weighted images (DWI) are a useful evaluation modality of cytotoxic edema in patients with encephalitis. We report on the reversible DWI change in a patient with COVID-19 associated encephalitis, who had diffused cytotoxic edema in the affected temporal lobe and accompanied status epilepticus. Sequential imaging showed that the cytotoxic edema in DWI confirmed recovery to normal after 12 days in the presence of cortical edema in fluid-attenuated inversion recovery image after aggressive neurocritical management. Thus, prompt, proper management is indispensable during the acute period and DWI may be a valuable tool for reflecting the clinical course of COVID-19 associated encephalitis.

Leveraging radiomics and machine learning to differentiate radiation necrosis from recurrence in patients with brain metastases.

Radiation necrosis (RN) can be difficult to radiographically discern from tumor progression after stereotactic radiosurgery (SRS). The objective of this study was to investigate the utility of radiomics and machine learning (ML) to differentiate RN from recurrence in patients with brain metastases treated with SRS. Patients with brain metastases treated with SRS who developed either RN or tumor reccurence were retrospectively identified. Image preprocessing and radiomic feature extraction were performed using ANTsPy and PyRadiomics, yielding 105 features from MRI T1-weighted post-contrast (T1c), T2, and fluid-attenuated inversion recovery (FLAIR) images. Univariate analysis assessed significance of individual features. Multivariable analysis employed various classifiers on features identified as most discriminative through feature selection. ML models were evaluated through cross-validation, selecting the best model based on area under the receiver operating characteristic (ROC) curve (AUC). Specificity, sensitivity, and F1 score were computed. Sixty-six lesions from 55 patients were identified. On univariate analysis, 27 features from the T1c sequence were statistically significant, while no features were significant from the T2 or FLAIR sequences. For clinical variables, only immunotherapy use after SRS was significant. Multivariable analysis of features from the T1c sequence yielded an AUC of 76.2% (standard deviation [SD] ± 12.7%), with specificity and sensitivity of 75.5% (± 13.4%) and 62.3% (± 19.6%) in differentiating radionecrosis from recurrence. Radiomics with ML may assist the diagnostic ability of distinguishing RN from tumor recurrence after SRS. Further work is needed to validate this in a larger multi-institutional cohort and prospectively evaluate it's utility in patient care.

Inferior Frontal Sulcal Hyperintensities on Brain MRI Are Associated with Amyloid Positivity beyond Age—Results from the Multicentre Observational DELCODE Study

Inferior frontal sulcal hyperintensities (IFSHs) on fluid-attenuated inversion recovery (FLAIR) sequences have been proposed to be indicative of glymphatic dysfunction. Replication studies in large and diverse samples are nonetheless needed to confirm them as an imaging biomarker. We investigated whether IFSHs were tied to Alzheimer’s disease (AD) pathology and cognitive performance. We used data from 361 participants along the AD continuum, who were enrolled in the multicentre DELCODE study. The IFSHs were rated visually based on FLAIR magnetic resonance imaging. We performed ordinal regression to examine the relationship between the IFSHs and cerebrospinal fluid-derived amyloid positivity and tau positivity (Aβ42/40 ratio ≤ 0.08; pTau181 ≥ 73.65 pg/mL) and linear regression to examine the relationship between cognitive performance (i.e., Mini-Mental State Examination and global cognitive and domain-specific performance) and the IFSHs. We controlled the models for age, sex, years of education, and history of hypertension. The IFSH scores were higher in those participants with amyloid positivity (OR: 1.95, 95% CI: 1.05–3.59) but not tau positivity (OR: 1.12, 95% CI: 0.57–2.18). The IFSH scores were higher in older participants (OR: 1.05, 95% CI: 1.00–1.10) and lower in males compared to females (OR: 0.44, 95% CI: 0.26–0.76). We did not find sufficient evidence linking the IFSH scores with cognitive performance after correcting for demographics and AD biomarker positivity. IFSHs may reflect the aberrant accumulation of amyloid β beyond age.

Spontaneous intracranial hypotension: Exploring the viability of non-contrast FLAIR as a substitute for contrast-enhanced T1WI in assessing pachymeningeal thickening.

To avoid contrast administration in spontaneous intracranial hypotension (SIH), some studies suggest accepting diffuse pachymeningeal hyperintensity (DPMH) on non-contrast fluid-attenuated inversion recovery (FLAIR) as an equivalent sign to diffuse pachymeningeal enhancement (DPME) on contrast-enhanced T1WI (T1ce), despite lacking thoroughperformance metrics. This study aimed to comprehensively explore its feasibility. In this single-center retrospective study, between April 2021 and November 2023, brain MRI examinations of 43 patients clinically diagnosed with SIH were assessed using 1.5 and 3.0 Tesla MRI scanners. Two radiologists independentlyassessed the presence or absence of DPMH on FLAIR and DPME on T1ce, with T1ce serving as a gold-standard for pachymeningeal thickening. The contribution of the subdural fluid collections to DPMH was investigated with quantitative measurements. Using Cohen's kappa statistics, interobserver agreement was assessed. In 39 out of 43 patients (90.7%), pachymeningeal thickening was observed on T1ce. FLAIR sequence produced an accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 72.1%, 71.8%, 75.0%, 96.6%, and 21.4% respectively, for determining pachymeningeal thickening. FLAIR identified pachymeningeal thickening in 28 cases; however, among these, 21 cases (75%) revealed that the pachymeningeal hyperintense signal was influenced by subdural fluid collections. False-negative rate for FLAIR was 28.2% (11/39). The lack of complete correlation between FLAIR and T1ce in identifying pachymeningeal thickening highlights the need for caution in removing contrast agent administration from the MRI protocol of SIH patients, as it reveals a major criterion (i.e., pachymeningeal enhancement) of Bern score.

Endovascular thrombectomy in wake-up stroke guided by arterial spin-labeling and fluid-attenuated inversion recovery versus diffusion-weighted imaging mismatch on MRI.

This purpose of this study is to investigate the effectiveness and safety of utilizing the arterial spin-labeling (ASL) combined with diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) combined with DWI double mismatch in the endovascular treatment of patients diagnosed with wake-up stroke (WUS). In this single-center trial, patients diagnosed with WUS underwent thrombectomy if acute ischemic lesions were observed on DWI indicating large precerebral circulation occlusion. Patients with no significant parenchymal hypersignal on FLAIR and ASL imaging showing a hypoperfusion tissue to infarct core volume ratio of at least 1.2 were included. The participants were divided into groups receiving endovascular thrombectomy plus medical therapy or medical therapy alone, based on their subjective preference. Functional outcomes were assessed using the ordinal score on the modified Rankin scale (mRs) at 90 days, along with the rate of functional independence. In this study, a total of 77 patients were included, comprising 38 patients in the endovascular therapy group and 39 patients in the medical therapy group. The endovascular therapy group exhibited more favorable changes in the distribution of functional prognosis measured by mRs at 90 days, compared to the medical therapy group (adjusted common odds ratio, 3.25; 95% CI, 1.03 to 10.26; P < 0.01). Additionally, the endovascular therapy group had a higher proportion of patients achieving functional independence (odds ratio, 4.0; 95% CI, 1.36 to 11.81; P < 0.01). Importantly, there were no significant differences observed in the incidence of intracranial hemorrhage or mortality rates between the two groups. Guided by the ASL-DWI and FLAIR-DWI double mismatch, endovascular thrombectomy combined with standard medical treatment appears to yield superior functional outcomes in patients with WUS and large vessel occlusion compared to standard medical treatment alone.

FLAMES overlaying anti-N-methyl-D-aspartate receptor encephalitis: a case report and literature review

BackgroundIn recent years, simultaneous or sequential occurrence of MOG antibody disease and anti-NMDAR encephalitis in the same patient has been reported with increasing frequency. Scholars refer to the overlapping occurrence of these two disorders as MOG antibody disease and anti-NMDAR encephalitis overlap syndrome (MNOS). Cortical T2-weighted fluid-attenuated inversion recovery (FLAIR) -hyperintense lesions in anti-MOG-associated encephalitis with seizures (FLAMES) is a rare clinical phenotype of MOGAD in which cortical FLAIR high-signal lesions are unilateral, with little spread to the cortex and meninges bilaterally. Although cases of FLAMES have been consistently reported. However, to our knowledge, such cases of FLAMES combined with NMDARE are rare.Case presentationHere, we describe a case of FLAMES combined with anti-NMDARE. The patient was a young male, 29 years old, admitted to our hospital with isolated seizures, whose MRI showed unilateral thalamic and bilateral frontal and parietal leptomeningeal involvement. Since we were unaware of the possibility of bilateral meningo-cortical MOGAD manifestations, the case was initially diagnosed as viral encephalitis and was given antiviral therapy. The diagnosis was not clarified until anti-NMDAR-IgG and MOG-IgG positivity was detected in the cerebrospinal fluid and serum. The patient was then treated with high-dose corticosteroids and his symptoms responded well to the steroids. Therefore, this case expands the clinical spectrum of MNOS overlap syndrome. In addition, we describe the clinical features of MNOS by summarizing the existing literature and exploring the possible mechanisms of its immune response.ConclusionsOur case serves as a reminder to clinicians that when patients present with atypical clinical manifestations such as seizures, consideration should be given to MNOS and conduct testing for various relevant autoantibodies (including MOG abs) and viruses in both serum and cerebrospinal fluid, as it is easy to misdiagnose the disease as other CNS diseases, such as viral meningoencephalitis. This syndrome exhibits a high responsiveness to steroids, highlighting the critical importance of recognizing the clinical and neuroimaging features of this overlap syndrome for prompt diagnosis and treatment. Furthermore, it enriches the disease spectrum of MNOS.

Simulation of acquisition shifts in T2 weighted fluid-attenuated inversion recovery magnetic resonance images to stress test artificial intelligence segmentation networks.

To provide a simulation framework for routine neuroimaging test data, which allows for "stress testing" of deep segmentation networks against acquisition shifts that commonly occur in clinical practice for T2 weighted (T2w) fluid-attenuated inversion recovery magnetic resonance imaging protocols. The approach simulates "acquisition shift derivatives" of MR images based on MR signal equations. Experiments comprise the validation of the simulated images by real MR scans and example stress tests on state-of-the-art multiple sclerosis lesion segmentation networks to explore a generic model function to describe the F1 score in dependence of the contrast-affecting sequence parameters echo time (TE) and inversion time (TI). The differences between real and simulated images range up to 19% in gray and white matter for extreme parameter settings. For the segmentation networks under test, the F1 score dependency on TE and TI can be well described by quadratic model functions (). The coefficients of the model functions indicate that changes of TE have more influence on the model performance than TI. We show that these deviations are in the range of values as may be caused by erroneous or individual differences in relaxation times as described by literature. The coefficients of the F1 model function allow for a quantitative comparison of the influences of TE and TI. Limitations arise mainly from tissues with a low baseline signal (like cerebrospinal fluid) and when the protocol contains contrast-affecting measures that cannot be modeled due to missing information in the DICOM header.

Radiomics for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences.

Using radiomics to predict O6-methylguanine-DNA methyltransferase promoter methylation status in patients with newly diagnosed glioblastoma and compare the performances of different MRI sequences. Preoperative MRI scans from 215 patients were included in this retrospective study. After image preprocessing and feature extraction, two kinds of machine-learning models were established and compared for their performances. One kind was established using all MRI sequences (T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient), and the other kind was based on single MRI sequence as listed above. For the machine-learning model based on all sequences, a total of seven radiomic features were selected with the Maximum Relevance and Minimum Redundancy algorithm. The predictive accuracy was 0.993 and 0.750 in the training and validation sets, respectively, and the area under curves were 1.000 and 0.754 in the two sets, respectively. For the machine-learning model based on single sequence, the numbers of selected features were 8, 10, 10, 13, 9, 7 and 6 for T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient, respectively, with predictive accuracies of 0.797-1.000 and 0.583-0.694 in the training and validation sets, respectively, and the area under curves of 0.874-1.000 and 0.538-0.697 in the two sets, respectively. Specifically, T1-weighted image-based model performed best, while contrast enhancement-based model performed worst in the independent validation set. The machine-learning models based on seven different single MRI sequences performed differently in predicting O6-methylguanine-DNA methyltransferase status in glioblastoma, while the machine-learning model based on the combination of all sequences performed best.

Brain MRI sequence and view plane identification using deep learning

Brain magnetic resonance imaging (MRI) scans are available in a wide variety of sequences, view planes, and magnet strengths. A necessary preprocessing step for any automated diagnosis is to identify the MRI sequence, view plane, and magnet strength of the acquired image. Automatic identification of the MRI sequence can be useful in labeling massive online datasets used by data scientists in the design and development of computer aided diagnosis (CAD) tools. This paper presents a deep learning (DL) approach for brain MRI sequence and view plane identification using scans of different data types as input. A 12-class classification system is presented for commonly used MRI scans, including T1, T2-weighted, proton density (PD), fluid attenuated inversion recovery (FLAIR) sequences in axial, coronal and sagittal view planes. Multiple online publicly available datasets have been used to train the system, with multiple infrastructures. MobileNet-v2 offers an adequate performance accuracy of 99.76% with unprocessed MRI scans and a comparable accuracy with skull-stripped scans and has been deployed in a tool for public use. The tool has been tested on unseen data from online and hospital sources with a satisfactory performance accuracy of 99.84 and 86.49%, respectively.

Radiomic-and dosiomic-based clustering development for radio-induced neurotoxicity in pediatric medulloblastoma.

Texture analysis extracts many quantitative image features, offering a valuable, cost-effective, and non-invasive approach for individual medicine. Furthermore, multimodal machine learning could have a large impact for precision medicine, as texture biomarkers can underlie tissue microstructure. This study aims to investigate imaging-based biomarkers of radio-induced neurotoxicity in pediatric patients with metastatic medulloblastoma, using radiomic and dosiomic analysis. This single-center study retrospectively enrolled children diagnosed with metastatic medulloblastoma (MB) and treated with hyperfractionated craniospinal irradiation (CSI). Histological confirmation of medulloblastoma and baseline follow-up magnetic resonance imaging (MRI) were mandatory. Treatment involved helical tomotherapy (HT) delivering a dose of 39 Gray (Gy) to brain and spinal axis and a posterior fossa boost up to 60Gy. Clinical outcomes, such as local and distant brain control and neurotoxicity, were recorded. Radiomic and dosiomic features were extracted from tumor regions on T1, T2, FLAIR (fluid-attenuated inversion recovery) MRI-maps, and radiotherapy dose distribution. Different machine learning feature selection and reduction approaches were performed for supervised and unsupervised clustering. Forty-eight metastatic medulloblastoma patients (29 males and 19 females) with a mean age of 12 ± 6years were enrolled. For each patient, 332 features were extracted. Greater level of abstraction of input data by combining selection of most performing features and dimensionality reduction returns the best performance. The resulting one-component radiomic signature yielded an accuracy of 0.73 with sensitivity, specificity, and precision of 0.83, 0.64, and 0.68, respectively. Machine learning radiomic-dosiomic approach effectively stratified pediatric medulloblastoma patients who experienced radio-induced neurotoxicity. Strategy needs further validation in external dataset for its potential clinical use in ab initio management paradigms of medulloblastoma.

The central vein sign in the diagnosis of multiple sclerosis

Objective: to determine the specificity and sensitivity of the central vein sign (CVS) for the diagnosis of multiple sclerosis (MS). Material and methods. We analyzed 76 patients. The main group consisted of 40 patients with a confirmed diagnosis of MS according to the McDonald criteria, 2017. The comparison group included 20 patients with migraine and 16 patients with cerebral microangiopathy. All patients underwent a standardized MRI scan of the brain, including 3D FLAIR and SWI modes. The total number of T2/FLAIR hyperintense lesions in the brain and the number of lesions with CVS were calculated. Results. In the group of MS patients, the total and relative number of lesions with CVS was higher than in the comparison group (p&lt;0.001). The 40% lesion rule had a sensitivity of 97.5% and a specificity of 94.4%. The sensitivity of the 3-lesion rule was 92.5% and the specificity 66.7%. The use of the 5-lesion rule had a sensitivity of 75% and a specificity of 83.7%. Conclusion. The data obtained indicate a significant diagnostic role for SCV in MS. The use of the 5-lesion rule provides high sensitivity and specificity with relative ease of use.

Recurrent demyelination attacks after BNT162b2 vaccination: Two case reports and literature review

We describe two cases with recurrent demyelinating attacks following BioNTech BNT162b2 immunization. No reports of any recurring demyelinating attack cases connected to COVID-19 immunization. Case-1: A 37-year-old male patient was admitted due to diplopia and ptosis. Neurological examination showed isolated oculomotor cranial paralysis on the right side. The second dose of BNT162b2 was given 10 days ago. On T2- and fluid-attenuated inversion recovery (FLAIR)-weighted magnetic resonance imaging (MRI), hyperintense lesions were seen in the pons on T1, with no evidence of contrast enhancement. We diagnosed the patient with central pontine myelinolysis associated with BNT162b2. Six months later after the first attack, a new lesion appeared in the same region. Case-2: He applied to the hospital at the age of 57 years, complaining of numbness in his hands and feet, confusion, and cooperation disorder. During the neurological assessment, apathy, cooperation, and orienting disorders were found. Hyperintense lesions were seen in both hemispheres, with cortico-subcortical localization on MRI. The patient received the BNT162b2 two weeks ago. After the initial attack six months ago, new clinical signs and an increase in demyelinating lesions were found. Despite early corticosteroid treatment, BNT162b2 immunization may be associated with repeated demyelination attacks. In patients with diffusion restriction on MRI, we might suggest using corticosteroid therapy for approximately one year, a lot longer than the literatures suggested.

DeepFLAIR: A neural network approach to mitigate signal and contrast loss in temporal lobes at 7 Tesla FLAIR images

Background and purposeHigher magnetic field strength introduces stronger magnetic field inhomogeneities in the brain, especially within temporal lobes, leading to image artifacts. Particularly, T2-weighted fluid-attenuated inversion recovery (FLAIR) images can be affected by these artifacts. Here, we aimed to improve the FLAIR image quality in temporal lobe regions through image processing of multiple contrast images via machine learning using a neural network. MethodsThirteen drug-resistant MR-negative epilepsy patients (age 29.2 ± 9.4y, 5 females) were scanned on a 7 T MRI scanner. Magnetization-prepared (MP2RAGE) and saturation-prepared with 2 rapid gradient echoes, multi-echo gradient echo with four echo times, and the FLAIR sequence were acquired. A voxel-wise neural network was trained on extratemporal-lobe voxels from the acquired structural scans to generate a new FLAIR-like image (i.e., deepFLAIR) with reduced temporal lobe inhomogeneities. The deepFLAIR was evaluated in temporal lobes through signal-to-noise (SNR), contrast-to-noise (CNR) ratio, the sharpness of the gray-white matter boundary and joint-histogram analysis. Saliency mapping demonstrated the importance of each input image per voxel. ResultsSNR and CNR in both gray and white matter were significantly increased (p < 0.05) in the deepFLAIR's temporal ROIs, compared to the FLAIR. The gray-white matter boundary sharpness was either preserved or improved in 10/13 right-sided temporal regions and was found significantly increased in the ROIs. Multiple image contrasts were influential for the deepFLAIR reconstruction with the MP2RAGE second inversion image being the most important. ConclusionsThe deepFLAIR network showed promise to restore the FLAIR signal and reduce contrast attenuation in temporal lobe areas. This may yield a valuable tool, especially when artifact-free FLAIR images are not available.

Comparison of MRI findings of hypothalamic-optic chiasmatic gliomas and craniopharyngiomas.

Differential diagnosis of hypothalamic-optic chiasmatic gliomas (HOCGs) and craniopharyngiomas on magnetic resonance imaging (MRI) can be quite challenging. To compare the MRI features of HOCGs and cranipharyngiomas. Patients diagnosed with HOCG or craniopharyngioma in histopathological evaluation between 2012 and 2022 and who underwent preoperative contrast-enhanced brain MRI were included. Various MRI features were retrospectively evaluated for each lesion: T2-weighted imaging and fluid attenuation inversion recovery hyperintensity, calcification, cystic change, T1-weighted (T1W) imaging hyperintensity of the cystic component, hemorrhage, involvement of sellar, suprasellar or other adjacent structures, lobulated appearance, presence of hydrocephalus, and contrast enhancement pattern. Apparent diffusion coefficient (ADC) values were also evaluated and compared. Among 38 patients included, 13 (34%) had HOCG and 25 (66%) had craniopharyngioma. Craniopharyngiomas had a significantly higher rate of cystic changes, calcification, and T1W imaging hyperintensity of the cystic component than HOCGs (P <0.05). Of HOCGs, 92% had chiasm involvement, 23% had optic nerve involvement, and 31% had brain stem involvement. On the other hand, chiasm involvement was observed in 8% of craniopharyngiomas, but none had optic nerve and/or brain stem involvement (P <0.05). While 62% (8/13) of HOCGs had diffuse homogeneous enhancement, 80% (20/25) of craniopharyngiomas had a diffuse heterogeneous enhancement pattern. Mean ADC values were significantly higher in craniopharyngiomas compared to HOCGs (2.1 vs. 1.6 ×10-3mm2/s, P <0.05). Although some neuroimaging findings may overlap, features such as presence of cyst and calcification, brain stem and optic pathway involvement, different enhancement patterns, and ADC values may be helpful in the differential diagnosis of HOCGs and craniopharyngiomas.

Resolution of white matter hyperintensity after surgical revascularization in moyamoya disease – A report of three cases

Background: Moyamoya disease often presents white matter hyperintensity (WMH) lesions on fluid-attenuated inversion recovery (FLAIR) images, which is generally accepted as irreversible. We, herein, describe three cases of moyamoya disease with WMH lesions that regressed or disappeared after surgical revascularization. Case Description: This report included two pediatric and one young adult case that developed transient ischemic attacks or ischemic stroke due to bilateral Moyamoya disease. Before surgery, five of their six hemispheres had WMH lesions in the subcortical and/or periventricular white matter on FLAIR images. The lesions included morphologically two different patterns: “Striated” and “patchy” morphology. In all of them, combined bypass surgery was successfully performed on both sides, and no cerebrovascular events occurred during follow-up periods. On follow-up magnetic resonance examinations, the “striated” WMH lesions completely disappeared within six months, while the “patchy” WMH lesions slowly regressed over 12 months. Conclusion: Based on radiological findings and the postoperative course of the WMH lesions, the “striated” WMH lesions may represent the inflammation or edema along the neuronal axons due to cerebral ischemia, while the “patchy” WMH lesions may represent vasogenic edema in the white matter through the blood-brain barrier breakdown. Earlier surgical revascularization may resolve these WMH lesions in Moyamoya disease.

Enhancing Cerebral Amyloid Angiopathy-Related Inflammation Diagnosis With PET Using Pittsburgh Compound B.

Cerebral amyloid angiopathy-related inflammation is a rare encephalopathy characterized by inflammation against amyloid protein accumulated in cerebral small vessels. A 50-year-old man was presented with a subacute consciousness disorder. Brain MRI revealed high intensity lesions in the white matter of the right parietal and occipital lobes on fluid-attenuated inversion recovery sequences and cerebral microbleeds in the right parietal and occipital lobes on T2*-weighted images. Pittsburgh compound B-PET demonstrated accumulation in the right temporoparietal lobe, confirming a potential diagnosis of probable cerebral amyloid angiopathy-related inflammation without brain biopsy. Steroid pulse therapy was initiated, with good results.

Multi-contrast echo-planar imaging sequence (Echo-planar imaging mix) in clinical situations demanding faster MRI-brain scans

Objectives: The excellent resolution offered by magnetic resonance imaging (MRI) has a trade-off in the form of scan duration. The purpose of the present study was to assess the clinical utility of echo-planar imaging mix (EPIMix), an echo-planar imaging-based MRI sequence for the brain with a short acquisition time. Materials and Methods: This was a retrospective observational study of 50 patients, who could benefit from faster MRI brain scans. The T1, T2, fluid attenuated inversion recovery, diffusion-weighted imaging (DWI), and T2*/susceptibility-weighted imaging sequences were acquired, conventionally and with EPIMix. Conventional and EPIMix images were assessed by two radiologists for overall quality, motion, and susceptibility artifacts and scored on a Likert scale. The scores given for conventional and EPIMix images were compared. The diagnostic performance of EPIMix was also assessed by the ability to detect clinically relevant findings. Results: The acquisition time for conventional MRI was 11 min and 45 s and for EPIMix 1 min and 15 s. All EPIMix images were sufficient for diagnostic use. On assessment of the diagnostic performance, it was excellent for ischemic and hemorrhagic strokes. Smaller lesions, lesions adjacent to bone, and post-operative tumors were difficult to identify. Moderate to perfect agreement (Kappa values 0.41–1) was seen between radiologists for all categories except skull base, calvarial, and orbital lesions. Image quality, artifact assessment showed excellent interobserver agreement (&gt;90%) for the scores. All EPIMix images showed reduced motion artifacts. The EPIMix-DWI was comparable to conventional-DWI in terms of quality and artifacts. The remaining sequences showed reduced quality and increased susceptibility. Conclusion: The EPIMix has a significantly reduced acquisition time than conventional MRI and could be used instead of conventional MRI in situations demanding faster scans such as suspected acute ischemic or hemorrhagic stroke. In other clinical scenarios, it could help tailor the MRI examination for each patient.

Vascular Aging in the Choroid Plexus: A 7T Ultrasmall Superparamagnetic Iron Oxide (USPIO)-MRI Study.

The choroid plexus (ChP), a densely vascularized structure, has drawn increasing attention for its involvement in brain homeostasis and waste clearance. While the volumetric changes have been explored in many imaging studies, few studies have investigated the vascular degeneration associated with aging in the ChP. To investigate the sub-structural characteristics of the ChP, particularly the vascular compartment using high-resolution 7T imaging enhanced with Ferumoxytol, an ultrasmall super-paramagnetic iron oxide, which greatly increase the susceptibility contrast for vessels. Prospective. Forty-nine subjects without neurological disorders (age: 21-80 years; 42 ± 17 years; 20 females). 7-T with 2D and 3D T2* GRE, 3D MPRAGE T1, 2D TSE T2, and 2D FLAIR. The vascular and stromal compartments of the ChP were segmented using K-means clustering on post-contrast 2D GRE images. Visual and qualitative assessment of ChP vascular characteristics were conducted independently by three observers. Vascular density (Volvessel/VolChP ratio) and susceptibility change (Δχ) induced by Ferumoxytol were analyzed on 3D GRE-derived susceptibility-weighted imaging and quantitative susceptibility mapping, respectively. Independent t-test, Mann-Whitney U test, and Chi-square test were utilized for group comparisons. The relationship between age and ChP's vascular alterations was examined using Pearson's correlation. Intra-class coefficient was calculated for inter-observer agreement. A P value <0.05 was considered statistically significant. 2D GRE images demonstrated superior contrast and accurate delineation of ChP substructures (ICC = 0.86). Older subjects exhibited a significantly smaller vascular density (16.5 ± 4.34%) and lower Δχ (22.10 ± 12.82 ppb) compared to younger subjects (24.85 ± 6.84% and 34.64 ± 12.69 ppb). Vascular density and mean Δχ within the ChP negatively correlated with age (r = -0.48, and r = -0.45). Ferumoxytol-enhanced 7T images can demonstrate ChP alterations in elderly with decreased vascular density and expansion of nonvascular compartment. 1 TECHNICAL EFFICACY: Stage 2.