AJNR. American journal of neuroradiology最新文献

Background and purpose: Ultra-high-resolution (UHR) photon-counting-detector (PCD) CT improves image resolution but increases noise, necessitating use of smoother reconstruction kernels that reduce resolution below the system's 0.110 mm maximum spatial resolution. To address this, a denoising convolutional neural network (CNN) was developed to reduce noise in images reconstructed with the available sharpest reconstruction kernel while preserving resolution for enhanced temporal bone visualization.

Materials and methods: With IRB approval, CNN was trained on 6 clinical temporal bone patient cases (1,885 images) and tested on 20 independent cases using a dual-source PCD-CT (NAEOTOM Alpha, Siemens). Images were reconstructed using iterative reconstruction at strength 3 (QIR3) with both clinical routine (Hr84) and the sharpest available head kernel (Hr96). The CNN was applied to images reconstructed with Hr96 and QIR1. Three image series (Hr84-QIR3, Hr96-QIR3, and Hr96-CNN) for each case were randomized for review by two neuroradiologists, assessing overall quality and delineation of the modiolus, stapes footplate, and incudomallear joint.

Results: CNN reduced noise by 80% compared to Hr96-QIR3 and 50% relative to Hr84-QIR3, while maintaining high resolution. When compared to the conventional method at the same kernel (Hr96-QIR3), Hr96-CNN significantly decreased image noise (from 204.63 HU to 47.35 HU) and improved SSIM (from 0.72 to 0.99). Hr96-CNN images ranked higher than Hr84-QIR3 and Hr96-QIR3 in overall quality (p<0.001). Readers preferred Hr96-CNN for all three structures.

Conclusions: The proposed CNN significantly reduced image noise in UHR PCD-CT, enabling the use of sharpest kernel. This combination greatly enhanced diagnostic image quality and anatomical visualization.ABBREVIATIONS: PCD = Photon-counting-detector; UHR = Ultra-high-resolution; IR = Iterative reconstruction; CNN = Convolutional neural network; SSIM: Structural similarity index.

The 2021 World Health Organization Classification of Tumors of the Central Nervous System (CNS5), introduced significant changes, impacting tumors ranging from glial to ependymal neoplasms. Ependymal tumors were previously classified and graded based on histopathology, which had limited clinical and prognostic utility. The updated CNS5 classification now divides ependymomas into 10 subgroups based on anatomic location (supratentorial, posterior fossa, and spinal compartment) and genomic markers. Supratentorial tumors are defined by zinc finger translocation associated (ZFTA) (formerly v-rel avian reticuloendotheliosis viral oncogene [RELA]), or yes-associated protein 1 (YAP1) fusion; posterior fossa tumors are classified into groups A (PFA) and B (PFB), spinal ependymomas are defined by MYCN amplification. Subependymomas are present across all these anatomic compartments. The new classification kept an open category of "not elsewhere classified" or "not otherwise specified" if no pathogenic gene fusion is identified or if the molecular diagnosis is not feasible. Although there is significant overlap in the imaging findings of these tumors, a neuroradiologist needs to be familiar with updated CNS5 classification to understand tumor behavior, for example, the higher tendency for tumor recurrence along the dural flap for ZFTA fusion-positive ependymomas. On imaging, supratentorial ZFTA-fused ependymomas are preferentially located in the cerebral cortex, carrying predominant cystic components. YAP1-MAMLD1-fused ependymomas are intra- or periventricular with prominent multinodular solid components and have significantly better prognosis than ZFTA-fused counterparts. PFA ependymomas are aggressive paramedian masses with frequent calcification, seen in young children, originating from the lateral part of the fourth ventricular roof. PFB ependymomas are usually midline, noncalcified solid-cystic masses seen in adolescents and young adults arising from the fourth ventricular floor. PFA has a poorer prognosis, higher recurrence, and higher metastatic rate than PFB. Myxopapillary spinal ependymomas are now considered grade II due to high recurrence rates. Spinal-MYCN ependymomas are aggressive tumors with frequent leptomeningeal spread, relapse, and poor prognosis. Subependymomas are noninvasive, intraventricular, slow-growing benign tumors with an excellent prognosis. Currently, the molecular classification does not enhance the clinicopathologic understanding of subependymoma and myxopapillary categories. However, given the molecular advancements, this will likely change in the future. This review provides an updated molecular classification of ependymoma, discusses the individual imaging characteristics, and briefly outlines the latest targeted molecular therapies.

Background and purpose: Gadolinium-based contrast agents are widely used for meningioma imaging; however, concerns exist regarding their side effects, cost, and environmental impact. At the standard gadolinium dose, most meningiomas show avid contrast enhancement, suggesting that administering a smaller dose may be feasible. The purpose of this study was to evaluate the impact of a lower gadolinium dose on the differentiation between meningiomas and adjacent intracranial tissues.

Materials and methods: One hundred eight patients with presumed or confirmed meningiomas who underwent a brain MRI at multiple doses of gadolinium were included in the study. The patients' MRIs were categorized into 3 groups based on the gadolinium dose administered: micro (approximately 25% of the standard dose), low (approximately 62% of the standard dose), and standard dose. Multireader qualitative visual assessment and quantitative relative signal differences calculations were performed to evaluate tumor differentiation from the cortex and from the dural venous sinus. The relative signal differences for each dose were analyzed by using ANOVA for quantitative assessment and the McNemar test for qualitative assessment. Additionally, noninferiority testing was used to compare the low and micro doses to the standard dose.

Results: Decreasing the gadolinium dose to a low dose or micro dose resulted in a statistically significant decrease in signal difference between the tumor and the adjacent brain tissue (P < .02). However, on visual assessment, the low dose was noninferior to the standard dose. The proportion of cases with suboptimal differentiation was significantly higher for the micro dose than for the standard dose, both for the differentiation between the tumor and the cortex (P = .041) and the differentiation between the tumor and the sinus (P < .001).

Conclusions: Reducing the gadolinium dose to 62% of the standard level still allows for sufficient visual delineation of meningiomas from surrounding tissues. However, further reduction to 25% substantially compromises the ability to distinguish the tumor from adjacent structures and is, therefore, not advisable.

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.

Background and purpose: Low-field 64 mT portable brain MRI has recently shown diagnostic promise for MS. This study aimed to evaluate the utility of portable MRI (pMRI) in assessing dissemination in space (DIS) in patients presenting with optic neuritis and determine whether deploying pMRI in the MS clinic can shorten the time from symptom onset to MRI.

Materials and methods: Newly diagnosed patients with optic neuritis referred to a tertiary academic MS center from July 2022 to January 2024 underwent both point-of-care pMRI and subsequent 3T conventional MRI (cMRI). Images were evaluated for periventricular (PV), juxtacortical (JC), and infratentorial (IT) lesions. DIS was determined on brain MRI per 2017 McDonald criteria. Test characteristics were computed by using cMRI as the reference. Interrater and intermodality agreement between pMRI and cMRI were evaluated by using the Cohen κ. Time from symptom onset to pMRI and cMRI during the study period was compared with the preceding 1.5 years before pMRI implementation by using Kruskal-Wallis with post hoc Dunn tests.

Results: Twenty patients (median age: 32.5 years [interquartile range {IQR}, 28-40]; 80% women) were included, of whom 9 (45%) and 5 (25%) had DIS on cMRI and pMRI, respectively. Median time interval between pMRI and cMRI was 7 days (IQR, 3.5-12.5). Interrater agreement was very good for PV (95%, κ = 0.89), and good for JC and IT lesions (90%, κ = 0.69 for both). Intermodality agreement was good for PV (90%, κ = 0.80) and JC (85%, κ = 0.63), and moderate for IT lesions (75%, κ = 0.42) and DIS (80%, κ = 0.58). pMRI had a sensitivity of 56% and specificity of 100% for DIS. The median time from symptom onset to pMRI was significantly shorter (8.5 days [IQR 7-12]) compared with the interval to cMRI before pMRI deployment (21 days [IQR 8-49], n = 50) and after pMRI deployment (15 days [IQR 12-29], n = 30) (both P < .01). Time from symptom onset to cMRI in those periods was not significantly different (P = .29).

Conclusions: In patients with optic neuritis, pMRI exhibited moderate concordance, moderate sensitivity, and high specificity for DIS compared with cMRI. Its integration into the MS clinic reduced the time from symptom onset to MRI. Further studies are warranted to evaluate the role of pMRI in expediting early MS diagnosis and as an imaging tool in resource-limited settings.

Spinal CSF leak care has evolved during the past several years due to pivotal advances in its diagnosis and treatment. To the reader of the American Journal of Neuroradiology (AJNR), it has been impossible to miss the exponential increase in groundbreaking research on spinal CSF leaks and spontaneous intracranial hypotension (SIH). While many clinical specialties have contributed to these successes, the neuroradiologist has been instrumental in driving this transformation due to innovations in noninvasive imaging, novel myelographic techniques, and image-guided therapies. In this editorial, we will delve into the exciting advancements in spinal CSF leak diagnosis and treatment and celebrate the vital role of the neuroradiologist at the forefront of this revolution, with particular attention paid to CSF leak-related work published in the AJNR.

Background and purpose: Previous studies have reported metal accumulation and microstructure changes in deep gray nuclei (DGN) in Wilson disease (WD). However, there are limited studies that investigate whether there is metal accumulation and microstructure changes in DGN of patients with WD with normal-appearing routine MRI. This study aimed to evaluate multiparametric changes in DGN of WD and whether the findings correlate with clinical severity in patients with WD.

Materials and methods: The study enrolled 28 patients with WD (19 with neurologic symptoms) and 25 controls. Fractional anisotropy (FA), mean diffusivity (MD), and magnetic susceptibility in globus pallidus, pontine tegmentum, dentate nucleus, red nucleus, head of caudate nucleus, putamen, substantia nigra, and thalamus were extracted. Correlations between imaging data and the Unified Wilson's Disease Rating Scale (UWDRS) neurologic subitems were explored.

Results: FA, MD, and susceptibility values were higher in multiple DGN of patients with WD than controls (P < .05). Patients with WD without abnormal signals in DGN on routine MRI also had higher FA, MD, and susceptibility values than controls (P < .017). We found that UWDRS neurologic subscores correlated with FA and susceptibility values of DGN (P < .05). In addition, we also found that FA and susceptibility values in specific structures correlated with specific neurologic symptoms of WD (ie, tremor, parkinsonism, dysarthria, dystonia, and ataxia) (P < .05).

Conclusions: Patients with WD have increased FA, MD, and susceptibility values even before the lesion is morphologically apparent on routine MRI. The increased FA and susceptibility values correlate with clinical severity of WD.

Background and purpose: The underlying transcriptomic signatures driving brain functional alterations in MS and neuromyelitis optica spectrum disorder (NMOSD) are still unclear.

Materials and methods: Regional fractional amplitude of low-frequency fluctuation (fALFF) values were obtained and compared among 209 patients with MS, 90 patients with antiaquaporin-4 antibody (AQP4)+ NMOSD, 49 with AQP4- NMOSD, and 228 healthy controls from a discovery cohort. We used partial least squares (PLS) regression to identify the gene transcriptomic signatures associated with disease-related fALFF alterations. The biologic process and cell type-specific signature of the identified PLS genes were explored by enrichment analysis. The correlation between PLS genes and clinical variables was explored. A prospective independent cohort was used to validate the brain fALFF alterations and the repeatability of identified genes.

Results: MS, AQP4+ NMOSD, and AQP4- NMOSD showed decreased fALFF in cognition-related regions and deep gray matter, while NMOSD (both AQP4+ and AQP4-) additionally demonstrated lower fALFF in the visual region. The overlapping PLS1- genes (indicating that the genes were overexpressed as regional fALFF decreased) were enriched in response to regulation of the immune response in all diseases, and the PLS1- genes were specifically enriched in the epigenetics profile in MS, membrane disruption and cell adhesion in AQP4+ NMOSD, and leukocyte activation in AQP4- NMOSD. For the cell type transcriptional signature, microglia and astrocytes accounted for the decreased fALFF. The fALFF-associated PLS1- genes directly correlated with Expanded Disability Status Scale of MS and disease duration across disorders.

Conclusions: We revealed the functional activity alterations and their underlying shared and specific gene transcriptional signatures in MS, AQP4+ NMOSD, and AQP4- NMOSD.

The ASNR Neuroradiology Division Chief Working Group's 2023 survey, with responses from 62 division chiefs, provides insights into turnaround times, faculty recruitment, moonlighting opportunities, and academic funds. In emergency cases, 61% aim for a turnaround time of less than 45-60 minutes, with two-thirds meeting this expectation more than 75% of the time. For inpatient CT and MR imaging scans, 54% achieve a turnaround time of 4-8 hours, with three-quarters meeting this expectation at least 50% of the time. Outpatient scans have an expected turnaround time of 24-48 hours, which is met in 50% of cases. Faculty recruitment strategies included 35% offering sign-on bonuses, with a median of $30,000. Additionally, 23% provided bonuses to fellows during fellowship to retain them in the practice upon completion of their fellowship. Internal moonlighting opportunities for faculty were offered by 70% of divisions, with a median pay of $250 per hour. The median annual academic fund for a full-time neuroradiology faculty member was $6000, typically excluding license fees but including American College of Radiology and American Board of Radiology membership, leaving $4000 for professional expenses. This survey calls for further dialogue on adapting and innovating academic institutions to meet evolving needs in neuroradiology.