Giant cell arteritis (GCA) is the most common primary large vessel systemic vasculitis in the Western World. Even though the involvement of scalp and intracranial vessels has received much attention in the neuroradiology literature, GCA, being a systemic vasculitis, can involve multiple other larger vessels including the aorta and its major head and neck branches. Herein, the authors present a pictorial review of the various cranial, extracranial, and orbital manifestations of GCA. An increased awareness of this entity may help with timely and accurate diagnosis, helping expedite therapy and preventing serious complications.

The inner ear contains many fissures and canals that can mimic pathology. Photon-counting CT allows greater spatial and contrast resolution of these structures over traditional energy-integrating CT detectors. Small channels containing nerves, arteries, and normal anatomy such as the cochlear cleft and cochlear and vestibular aqueducts are commonly encountered on temporal bone imaging. The improved visualization of these structures poses challenges for radiologists who are new to photon-counting CT. This article updates the existing temporal bone anatomy literature with a detailed anatomic review of the inner ear and major nerves frequently encountered when reviewing temporal bone imaging.

Background and purpose: Even though glioblastoma (GB) and brain metastases (BM) can be differentiated using radiomics, it remains unclear if the model performance may vary based on the contrast-enhanced sequence used. Our aim was to evaluate the radiomics-based model performance for differentiation between GB and brain metastases BM using MPRAGE and volumetric interpolated breath-hold examination (VIBE) T1-contrast-enhanced sequence. Materials and methods: T1 contrast-enhanced (T1-CE) MPRAGE and VIBE sequences acquired in 108 patients (31 GBs and 77 BM) during the same MRI session were retrospectively evaluated. After standardized image preprocessing and segmentation, radiomics features were extracted from necrotic and enhancing tumor components. Pearson correlation analysis of radiomics features from tumor subcomponents was also performed. A total of 90 machine learning pipelines were evaluated using a 5-fold cross-validation. Performance was measured by mean area under the curve (AUC)-receiver operating characteristic (ROC), log loss, and Brier scores. Results: A feature-wise comparison showed that the radiomics features between sequences were strongly correlated, with the highest correlation for shape-based features. The mean AUC across the top 10 pipelines ranged between 0.851 and 0.890 with T1-CE MPRAGE and between 0.869 and 0.907 with the T1-CE VIBE sequence. The top-performing models for the MPRAGE sequence commonly used support vector machines, while those for the VIBE sequence used either support vector machines or random forest. Common feature-reduction methods for top-performing models included linear combination filter and least absolute shrinkage and selection operator for both sequences. For the same machine learning feature-reduction pipeline, model performances were comparable (AUC-ROC difference range, -0.078-0.046). Conclusions: Radiomics features derived from T1-CE MPRAGE and VIBE sequences are strongly correlated and may have similar overall classification performance for differentiating GB from BM.

Primary cranial neurolymphomatosis (PCNL) is a rare subtype of primary CNS lymphoma (PCNSL) in which infiltrative lymphomatous involvement is confined to cranial nerves. Here, we report a case of PCNL with successful genomic profiling. A 57-year-old male had a lengthy prediagnostic phase spanning approximately 30 months, characterized by multiple episodes of cranial neuropathies managed by steroids. At the time of diagnosis, the patient had right-sided cranial neuropathies involving cranial nerves (CN) V, VI, and VII. Pathological findings of the right cavernous lesion biopsy were consistent with large B-cell lymphoma-infiltrating nerve fibers. The clinical course was aggressive and refractory, characterized by relentless progression with the development of cervical spinal neurolymphomatosis, cerebrospinal fluid involvement, and ependymal and intraparenchymal cerebral involvement, despite multiple lines of therapy, including chemoimmunotherapy, Bruton's tyrosine kinase inhibitor, radiation, autologous stem cell transplant, chimeric antigen receptor T-cell therapy (CAR-T), and whole-brain radiation. The patient survived for 22 months from the time of the initial diagnosis and 52 months after the first episode of cranial neuropathy. Next-generation sequencing identified mutations (MYD88, CD79b, and PIM1) that are frequently observed in PCNSL. The unusual findings included a total of 22 mutations involving PIM1, indicating a highly active aberrant somatic hypermutation and two missense CXCR4 mutations. CXCR4 mutations have never been described in PCNSL and may have implications for disease biology and therapeutic interventions. We provide a literature review to further elucidate PCNL.

BACKGROUND AND PURPOSE:An early and accurate diagnosis of multiple sclerosis remains challenging in clinical neurology. Established diagnostic methods have less than desirable sensitivity and specificity. An accurate, noninvasive diagnostic test for MS could have a major impact on diagnostic criteria. We compared the frequency of detection of the central vein sign (CVS) in white matter lesions of MS and controls on 7T T2*-weighted and SWI to 3T SWI. Additionally, we assessed the diagnostic performance of 7T T2*, 7T SWI, and 3T SWI for MS. MATERIALS AND METHODS:A retrospective case-control study was performed of patients with MS having both 7T MRI and 3T MRI. A control group of patients without MS was selected. Diagnosis of MS was established by board-certified neurologists with fellowship training in autoimmune neurology in line with the 2017 McDonald criteria. Percentage of lesions with a CVS was blindly measured for each technique. Diagnostic performance was computed by sensitivity, specificity, and positive and negative likelihood ratios (LRs). RESULTS:Sixty-one patients with MS (903 lesions) and 39 controls (1088 lesions) were included. 7T T2* showed significantly more CVS (87%) than both 7T SWI (73%) and 3T SWI (31%) (all P < .001). CVS was identified in the control group in ≤6% of lesions on all sequences. Using a threshold of >40% of lesions with CVS on 7T T2* and >15% on 7T SWI, both sequences had an accuracy = 100%, sensitivity = 100%, specificity = 100%, infinite positive LR, and zero negative LR. Using an optimal threshold of >12%, 3T SWI had an accuracy = 96.0%, sensitivity = 93.4%, specificity = 100%, infinite positive LR, and negative LR = 0.066. CONCLUSIONS:7T MRI had 100% sensitivity and specificity for the diagnosis of MS and is superior to 3T. Future revisions to MS diagnostic criteria may consider recommendations for 7T MRI and inclusion of CVS as a biomarker.

Fragile X tremor/ataxia syndrome (FXTAS) is an adult-onset neurodegenerative disorder characterized by premutation expansion of fragile X mental retardation 1 (FMR1) gene. It is a common single-gene cause of tremor, ataxia, and cognitive decline in adults. FXTAS affects the central, peripheral and autonomic nervous systems, leading to a range of neurological symptoms from dementia to dysautonomia. A characteristic imaging feature of FXTAS is symmetric T2 hyperintensity in the deep white matter of the cerebellar hemispheres and middle cerebral peduncle. However, recent studies have reported additional findings on diffusion weighted images (DWI), such as a symmetric high-intensity band-like signal at the cerebral corticomedullary junction. These findings, along with the characteristic cerebellar signal alterations, overlap with imaging findings seen in adult-onset neuronal intranuclear inclusion disease (NIID). Importantly, recent pathology studies have shown that both FXTAS and NIID can manifest intranuclear inclusion bodies, posing a diagnostic challenge and potential for misdiagnosis. We describe a 58-year-old man with FXTAS who received an erroneous diagnosis based on imaging and histopathology results. We emphasize the potential pitfalls in distinguishing NIID from FXTAS and stress the importance of genetic analysis in all cases with suspected NIID and FXTAS for confirmation. Additionally, we present the 7T MRI brain findings of FXTAS.

The past decade has seen exponential advancements in molecular markers and the genetics of tumors, recognizing the limitations of conventional histopathology for grading, classification, and prognostication. Such advances have resulted in changes to classification systems, for example, with the incorporation of objective molecular and genetic information into the 2021 World Health Organization (WHO) classification of central nervous system tumors. The fifth edition of the WHO classification of head and neck tumors (HN5) (beta online version, 2022) also introduced major changes based on molecular markers, including additions, deletions, and reclassifications of entities, with the idea of being more objective and standardized. These changes are highly relevant to therapy decisions, prognosis, and clinical research and for patients with resistant diseases to explore options in clinical trials. The HN5, for the first time, included a radiologist as a member of the writing team to incorporate pertinent imaging findings into the classification. It is important for the radiologist, as an integral part of the multidisciplinary team, to be up to date about these changes for a better understanding of tumor biology, to integrate this into their clinical practice, and to provide more value in their interpretations. The authors provide a basic understanding of pathology and genetics for the radiologist, highlighting the molecular changes in epithelial (including squamous cell) and nonepithelial tumors of the head and neck. The authors also highlight newly recognized and reclassified tumor entities and provide a brief discussion on the genetic tumor syndromes.

Kidney failure (KF) refers to a progressive decline in glomerular filtration rate to below 15 ml/min per 1.73 m2, necessitating renal replacement therapy with dialysis or renal transplant. The hemodynamic and metabolic alterations in KF combined with a proinflammatory and coagulopathic state leads to complex multisystemic complications. The imaging hallmark of systemic manifestations of KF is bone resorption caused by secondary hyperparathyroidism. Other musculoskeletal complications include brown tumor, osteosclerosis, calcinosis, soft-tissue calcification, and amyloid arthropathy. Cardiovascular complications and infections are the leading causes of death in KF. Cardiovascular complications include accelerated atherosclerosis, cardiomyopathy, pericarditis, myocardial calcinosis, and venous thromboembolism. Neurologic complications such as encephalopathy, osmotic demyelination, cerebrovascular disease, and opportunistic infections are also frequently encountered. Pulmonary complications include edema and calcifications. Radiography and CT are used in assessing musculoskeletal and thoracic complications, while MRI plays a key role in assessing neurologic and cardiovascular complications. CT iodinated contrast material is generally avoided in patients with KF except in situations where the benefit of contrast-enhanced CT outweighs the risks and in patients already undergoing maintenance dialysis. At MRI, group II gadolinium-based contrast material can be safely administered in patients with KF. The authors discuss the extrarenal systemic manifestations of KF, the choice of imaging modality in their assessment, and imaging findings of complications. ©RSNA, 2024 Supplemental material is available for this article.

BACKGROUND AND PURPOSE: Feature variability in radiomics studies due to technical and magnet strength parameters is well-known and may be addressed through various preprocessing methods. However, very few studies have evaluated the downstream impact of variable preprocessing on model classification performance in a multiclass setting. We sought to evaluate the impact of Smallest Univalue Segment Assimilating Nucleus (SUSAN) denoising and Combining Batches harmonization on model classification performance. MATERIALS AND METHODS: A total of 493 cases (410 internal and 83 external data sets) of glioblastoma, intracranial metastatic disease, and primary CNS lymphoma underwent semiautomated 3D-segmentation post-baseline image processing (BIP) consisting of resampling, realignment, coregistration, skull-stripping, and image normalization. Post-BIP, 2 sets were generated, one with and another without SUSAN denoising. Radiomics features were extracted from both data sets and batch-corrected to produce 4 data sets: (a) BIP, (b) BIP with SUSAN denoising, (c) BIP with Combining Batches, and (d) BIP with both SUSAN denoising and Combining Batches harmonization. Performance was then summarized for models using a combination of 6 feature-selection techniques and 6 machine learning models across 4 mask-sequence combinations with features derived from 1 to 3 (multiparametric) MRI sequences. RESULTS: Most top-performing models on the external test set used BIP+SUSAN denoising-derived features. Overall, the use of SUSAN denoising and Combining Batches harmonization led to a slight but generally consistent improvement in model performance on the external test set. CONCLUSIONS: The use of image-preprocessing steps such as SUSAN denoising and Combining Batches harmonization may be more useful in a multi-institutional setting to improve model generalizability. Models derived from only T1 contrast-enhanced images showed comparable performance to models derived from multiparametric MRI.

Temporal lobe epilepsy is a common form of epilepsy that is often associated with hippocampal sclerosis (HS). Although HS is commonly considered a binary assessment in radiologic evaluation, it is known that histopathologic changes occur in distinct clusters. Some subtypes of HS only affect certain subfields, resulting in minimal changes to the overall volume of the hippocampus. This is likely a major reason why whole hippocampal volumetrics have underperformed versus expert readers in the diagnosis of HS. With recent advancements in MRI technology, it is now possible to characterize the substructure of the hippocampus more accurately. However, this is not consistently addressed in radiographic evaluations. The histologic subtype of HS is critical for prognosis and treatment decision-making, necessitating improved radiologic classification of HS. The International League Against Epilepsy (ILAE) has issued a consensus classification scheme for subtyping HS histopathologic changes. This review aims to explore how the ILAE subtypes of HS correlate with radiographic findings, introduce a grading system that integrates radiologic and pathologic reporting in HS, and outline an approach to detecting HS subtypes by using MRI. This framework will not only benefit current clinical evaluations, but also enhance future studies involving high-resolution MRI in temporal lobe epilepsy.

Photon-counting CT (PCT) allows for improved spatial and contrast resolution compared with traditional energy-integrating detector CT. PCT offers markedly improved visualization of previously described structures, as well as those that were previously beyond the resolution of imaging. Although the anatomic details of the external ear and middle ear structures have been described previously, the rich detail of these structures has not been comprehensively reviewed in the radiology literature. The microarchitecture of the middle ear ossicles and bony protuberances are particularly well visualized on PCT. This review updates the existing literature with a detailed anatomic review of the external ear and the middle ear on temporal bone CT.

Cauda Equina Neuroendocrine Tumors (CE-NET), previously referred to as paragangliomas are a rare subset of spinal tumors, with limited data on imaging. Herein, we present a retrospective review of clinical and imaging findings of CE-NETs in ten patients who were evaluated at our institution over the past two decades. All patients had well-defined intradural lesions in the lumbar spine which demonstrated slow growth. A review of imaging findings revealed the presence of an eccentric vascular pedicle along the dorsal aspect of the tumor in 8 of the 10 patients (eccentric vessel sign), a distinctive finding that has not previously been reported with this tumor and may help improve the accuracy of imaging-based diagnosis. In all cases, a gross-total resection was performed, with resolution of symptoms in most of the cases.

OBJECTIVES:Detection of infratentorial demyelinating lesions in multiple sclerosis (MS) presents a challenge in magnetic resonance imaging (MRI), a difficulty that is further heightened in 7 T MRI. This study aimed to assess the efficacy of a novel MRI approach, lesion-attenuated magnetization-prepared gradient echo acquisition (LAMA), for detecting demyelinating lesions within the posterior fossa and upper cervical spine on 7 T MRI and contrast its performance with conventional double-inversion recovery (DIR) and T2-weighted turbo spin echo sequences. MATERIALS AND METHODS:We conducted a retrospective cross-sectional study in 42 patients with a confirmed diagnosis of MS. All patients had 7 T MRI that incorporated LAMA, 3D DIR, and 2D T2-weighted turbo spin echo sequences. Three readers assessed lesion count in the brainstem, cerebellum, and upper cervical spinal cord using both DIR and T2-weighted images in one session. In a separate session, LAMA was analyzed alone. Contrast-to-noise ratio was also compared between LAMA and the conventional sequences. Lesion counts between methods were assessed using nonparametric Wilcoxon signed rank test. Interrater agreement in lesion detection was estimated by intraclass correlation coefficients. RESULTS:LAMA identified a significantly greater number of lesions than DIR + T2 (mean 6.4 vs 3.0; P < 0.001). LAMA also exhibited better interrater agreement (intraclass correlation coefficient [95% confidence interval], 0.75 [0.41-0.88] vs 0.61 [0.35-0.78]). The contrast-to-noise ratio for LAMA (3.7 ± 0.9) significantly exceeded that of DIR (1.94 ± 0.7) and T2 (1.2 ± 0.7) (all P 's < 0.001). In cases with no lesions detected using DIR + T2, at least 1 lesion was identified in 83.3% with LAMA. Across all analyzed brain regions, LAMA consistently detected more lesions than DIR + T2. CONCLUSIONS:LAMA significantly improves the detection of infratentorial demyelinating lesions in MS patients compared with traditional methods. Integrating LAMA with standard magnetization-prepared 2 rapid acquisition gradient echo acquisition provides a valuable tool for accurately characterizing the extent of MS disease.

BACKGROUND AND PURPOSE:Intracranial vessel wall imaging is technically challenging to implement, given the simultaneous requirements of high spatial resolution, excellent blood and CSF signal suppression, and clinically acceptable gradient times. Herein, we present our preliminary findings on the evaluation of a deep learning-optimized sequence using T1-weighted imaging. MATERIALS AND METHODS:Clinical and optimized deep learning-based image reconstruction T1 3D Sampling Perfection with Application optimized Contrast using different flip angle Evolution (SPACE) were evaluated, comparing noncontrast sequences in 10 healthy controls and postcontrast sequences in 5 consecutive patients. Images were reviewed on a Likert-like scale by 4 fellowship-trained neuroradiologists. Scores (range, 1-4) were separately assigned for 11 vessel segments in terms of vessel wall and lumen delineation. Additionally, images were evaluated in terms of overall background noise, image sharpness, and homogeneous CSF signal. Segment-wise scores were compared using paired samples t tests. RESULTS:The scan time for the clinical and deep learning-based image reconstruction sequences were 7:26 minutes and 5:23 minutes respectively. Deep learning-based image reconstruction images showed consistently higher wall signal and lumen visualization scores, with the differences being statistically significant in most vessel segments on both pre- and postcontrast images. Deep learning-based image reconstruction had lower background noise, higher image sharpness, and uniform CSF signal. Depiction of intracranial pathologies was better or similar on the deep learning-based image reconstruction. CONCLUSIONS:Our preliminary findings suggest that deep learning-based image reconstruction-optimized intracranial vessel wall imaging sequences may be helpful in achieving shorter gradient times with improved vessel wall visualization and overall image quality. These improvements may help with wider adoption of intracranial vessel wall imaging in clinical practice and should be further validated on a larger cohort.

Metabolic syndrome comprises a set of risk factors that include abdominal obesity, impaired glucose tolerance, hypertriglyceridemia, low high-density lipoprotein levels, and high blood pressure, at least three of which must be fulfilled for diagnosis. Metabolic syndrome has been linked to an increased risk of cardiovascular disease and type 2 diabetes mellitus. Multimodality imaging plays an important role in metabolic syndrome, including diagnosis, risk stratification, and assessment of complications. CT and MRI are the primary tools for quantification of excess fat, including subcutaneous and visceral adipose tissue, as well as fat around organs, which are associated with increased cardiovascular risk. PET has been shown to detect signs of insulin resistance and may detect ectopic sites of brown fat. Cardiovascular disease is an important complication of metabolic syndrome, resulting in subclinical or symptomatic coronary artery disease, alterations in cardiac structure and function with potential progression to heart failure, and systemic vascular disease. CT angiography provides comprehensive evaluation of the coronary and systemic arteries, while cardiac MRI assesses cardiac structure, function, myocardial ischemia, and infarction. Liver damage results from a spectrum of nonalcoholic fatty liver disease ranging from steatosis to fibrosis and possible cirrhosis. US, CT, and MRI are useful in assessing steatosis and can be performed to detect and grade hepatic fibrosis, particularly using elastography techniques. Metabolic syndrome also has deleterious effects on the pancreas, kidney, gastrointestinal tract, and ovaries, including increased risk for several malignancies. Metabolic syndrome is associated with cerebral infarcts, best evaluated with MRI, and has been linked with cognitive decline. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Pickhardt in this issue.

3D-Edge Enhancing Gradient Echo MRI (EDGE) is a promising method for identifying focal cortical dysplasia (FCD) because of its superior contrast-to-noise ratio. Gains in signal-to-noise ratio (SNR) and spatial resolution using 7T MRI should further enhance the utility of EDGE; however, the exquisite sensitivity to the optimal inversion time makes translation to 7T challenging given the extreme nonuniformities in the transmit field (B1+). By utilizing a combined MP2RAGE-EDGE sequence, the efficiency of the acquisition can be optimized to improve B1+ transmit uniformity and SNR. Using this approach, we report the first experience of using 7T EDGE MRI for the diagnosis of FCD in epilepsy, showing the feasibility of achieving high spatial resolution, SNR, and image contrast uniformity. EDGE-MP2RAGE sequence improves the detection of gray-white matter junction abnormalities associated with FCD and is a valuable, time-efficient addition to a 7T MR protocol in patients with focal epilepsy.

Hyperviscosity syndrome (HVS) is an emergent complication of Waldenström macroglobulinemia (WM) characterized by visual, neurologic, and rarely auditory impairment. We report a 69-year-old female with MYD88 and CXCR4-mutant WM who developed HVS resulting in bilateral blindness and deafness associated with neurologic manifestations including confusion, severe generalized weakness, and imbalance. Ophthalmologic evaluation revealed bilateral central retinal vein occlusion (CRVO), diffuse retinal hemorrhages, macular edema, and serous macular detachments (SMD). Magnetic resonance imaging of the brain showed bleeding in the inner ears. Management was challenging as her WM was resistant to systemic therapies including bendamustine + rituximab (BR) and rituximab + bortezomib + dexamethasone (RVD). Bruton's tyrosine kinase inhibitors could not be used initially due to ongoing lower gastrointestinal bleeding. She required five total sessions of plasma exchange and was finally initiated on zanubrutinib, achieving a partial response. She also received intravitreal bevacizumab with rapid resolution of the retinal hemorrhages but with little improvement of the SMD. She had partial restoration of her hearing in the right ear and only slight improvement in her bilateral visual deficits. The management of HVS in frail, elderly patients with therapy-resistant WM can be challenging. In these cases, plasma exchange is required until an effective systemic therapy can be safely instituted. Genomic profiling is important in the management of WM as it can predict treatment resistance and guide therapeutic decisions.

•Rosai-Dorfman-Destombes disease is a rare benign idiopathic proliferative disease where lymph node sinuses are infiltrated with distinctive histiocytes.•Majority of the patients present with massive cervical lymphadenopathy, with some having extranodal diseases.•Unilateral ocular manifestations are reported in 11% of cases, whereas, bilateral orbital masses are even rare, with only a handful of cases being described till date.•Here, we present a case of Rosai-Dorfman-Destombes disease presenting as isolated bilateral orbital masses.

The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society. Since its inception, BraTS has been focusing on being a common benchmarking venue for brain glioma segmentation algorithms, with well-curated multi-institutional multi-parametric magnetic resonance imaging (mpMRI) data. Gliomas are the most common primary malignancies of the central nervous system, with varying degrees of aggressiveness and prognosis. The RSNA-ASNR-MICCAI BraTS 2021 challenge targets the evaluation of computational algorithms assessing the same tumor compartmentalization, as well as the underlying tumor's molecular characterization, in pre-operative baseline mpMRI data from 2,040 patients. Specifically, the two tasks that BraTS 2021 focuses on are: a) the segmentation of the histologically distinct brain tumor sub-regions, and b) the classification of the tumor's O[6]-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. The performance evaluation of all participating algorithms in BraTS 2021 will be conducted through the Sage Bionetworks Synapse platform (Task 1) and Kaggle (Task 2), concluding in distributing to the top ranked participants monetary awards of 60,000 collectively.