Imaging the Cerebral Veins in Pediatric Patients: Beyond Dural Venous Sinus Thrombosis.

The range of intracranial venous anomalies in children differs from that in adults. As a commonly encountered highly morbid disease, sinovenous thrombosis has been discussed extensively in the literature, and the associated imaging considerations are similar in pediatric and adult patients. The authors shift the focus to less frequently discussed cerebral venous diseases in pediatric patients. First, the practical embryology pertinent to malformations, syndromes, and variants such as vein of Galen aneurysmal malformation, Sturge-Weber syndrome, and developmental venous anomalies are discussed. Second, anatomic considerations that are applicable to neuroimaging in pediatric patients with cerebral venous anomalies are reviewed. In the discussion of anatomy, special attention is given to the medullary venous system that serves the cerebral white matter, superficial cortical veins (tributaries of the dural venous sinuses), and bridging veins, which carry blood from the superficial cortical veins through the potential subdural space into the dural venous sinuses. Third, the selection of imaging modalities (US, CT and CT venography, and MRI) is addressed, and various MR venographic pulse sequences (time-of-flight, phase-contrast, and contrast-enhanced sequences) are compared. Finally, a broad variety of congenital and acquired superficial and deep venous diseases in children are reviewed, with emphasis on less frequently discussed entities involving the medullary (eg, deep medullary venous engorgement and thrombosis, periventricular hemorrhagic venous infarction due to germinal matrix hemorrhage), cortical (eg, cortical venous thrombosis), and bridging (eg, acute and chronic manifestations of injury in abusive head trauma) veins, as well as the deep veins and dural venous sinuses (eg, varix). © RSNA, 2023 Quiz questions for this article are available through the Online Learning Center. Online supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article.

A First Estimate of the Annual Prevalence of Basivertebral Nerve Ablation Candidates in a Spine Clinic.

BACKGROUND: Emerging literature supports the use of basivertebral nerve ablation (BVNA) for a specific cohort of patients with chronic low back pain and Type 1 or Type 2 Modic changes from vertebral levels L3-S1. The early literature warrants further evaluation. Studies establishing the efficacy of BVNA use highly selective patient criteria. OBJECTIVE: Provide a first estimate of the prevalence of BVNA candidates in a spine clinic over a year using the foundational studies patient selection criteria? METHODS: A retrospective review of four fellowhsip trained spine physiatrists patient encounters at a large academic medical center using relevant ICD-10 codes to isolate chronic low back pain without radiating symptoms from January 1, 2019 to January 1, 2020. Charts were then reviewed by a team of physicians for exclusionary criteria from the foundational studies which have demonstrated benefit from BVNA. MRI's from qualifying charts which did not meet exclusionary criteria were then independently reviewed by four physician for localization and characterization of Modic changes. RESULTS: The relevant diagnostic codes query yielded 338 unique patient records. Based on exclusionary criteria or lack of imaging availability, 318 charts were eliminated. The remaining 20 charts qualified for imaging review. There were 11 charts in which there was 100% agreement between all reviewers regarding the presence and either Type 1 or Type 2 Modic changes between vertebral levels L3 to S1. Accordingly, the prevalence of eligibility for BVNA was 3% (11/338, 95% CI 1-5%). CONCLUSION: The population which may benefit from BVNA is small. Our study demonstrated that over a year, the prevalence for BVNA candidacy using the foundational studies criteria was 3% (95% CI 1% - 5%). While physicians may be tempted to use less stringent selection criteria in practice, upon doing so they cannot cite the foundational studies as evidence for the outcomes they expect to achieve. Those outcomes will require more studies which formally assess the benefits of BVNA when selection criteria are relaxed.

Current Perspectives of Artificial Intelligence in Pediatric Neuroradiology: An Overview.

Artificial Intelligence, Machine Learning, and myriad related techniques are becoming ever more commonplace throughout industry and society, and radiology is by no means an exception. It is essential for every radiologists of every subspecialty to gain familiarity and confidence with these techniques as they become increasingly incorporated into the routine practice in both academic and private practice settings. In this article, we provide a brief review of several definitions and techniques that are commonly used in AI, and in particular machine vision, and examples of how they are currently being applied to the setting of clinical neuroradiology. We then review the unique challenges that the adoption and application of faces within the subspecialty of pediatric neuroradiology, and how these obstacles may be overcome. We conclude by presenting specific examples of how AI is currently being applied within the field of pediatric neuroradiology and the potential opportunities that are available for future applications.

Artificial intelligence in stroke imaging: Current and future perspectives.

Artificial intelligence (AI) is a fast-growing research area in computer science that aims to mimic cognitive processes through a number of techniques. Supervised machine learning, a subfield of AI, includes methods that can identify patterns in high-dimensional data using labeled 'ground truth' data and apply these learnt patterns to analyze, interpret, or make predictions on new datasets. Supervised machine learning has become a significant area of interest within the medical community. Radiology and neuroradiology in particular are especially well suited for application of machine learning due to the vast amount of data that is generated. One devastating disease for which neuroimaging plays a significant role in the clinical management is stroke. Within this context, AI techniques can play pivotal roles for image-based diagnosis and management of stroke. This overview focuses on the recent advances of artificial intelligence methods - particularly supervised machine learning and deep learning - with respect to workflow, image acquisition and reconstruction, and image interpretation in patients with acute stroke, while also discussing potential pitfalls and future applications.

Synthesize High-Quality Multi-Contrast Magnetic Resonance Imaging From Multi-Echo Acquisition Using Multi-Task Deep Generative Model.

Multi-echo saturation recovery sequence can provide redundant information to synthesize multi-contrast magnetic resonance imaging. Traditional synthesis methods, such as GE's MAGiC platform, employ a model-fitting approach to generate parameter-weighted contrasts. However, models' over-simplification, as well as imperfections in the acquisition, can lead to undesirable reconstruction artifacts, especially in T2-FLAIR contrast. To improve the image quality, in this study, a multi-task deep learning model is developed to synthesize multi-contrast neuroimaging jointly using both signal relaxation relationships and spatial information. Compared with previous deep learning-based synthesis, the correlation between different destination contrast is utilized to enhance reconstruction quality. To improve model generalizability and evaluate clinical significance, the proposed model was trained and tested on a large multi-center dataset, including healthy subjects and patients with pathology. Results from both quantitative comparison and clinical reader study demonstrate that the multi-task formulation leads to more efficient and accurate contrast synthesis than previous methods.

CT Attenuation of Acute Subdural Hematomas in Patients with Anemia.

BACKGROUND AND PURPOSE: Isodense and hypodense acute subdural hematomas have been reported in the literature in anemic patients. The purpose of this study is to see if there is a statistically significant difference between the Hounsfield unit measurements of acute subdural hematomas in anemic and nonanemic patients. METHODS: A total of 109 patients were analyzed. We measured the most hyperdense component of the subdural hematoma and compared these measurements for both anemic and nonanemic patients. RESULTS: All patients with anemia had a hyperdense component to their subdural hematomas during the acute period. No statistically significant difference was found in the density of the subdural hematomas between the two groups. More heterogeneous subdural hematomas were found in the anemic group than the nonanemic group, which suggests that anemia alone is not a sufficient explanation for acute homogenous isodense and hypodense subdural hematomas. CONCLUSION: A hyperdense subdural component was present in all acute subdural hematomas in anemic patients. Therefore, anemia alone is not a sufficient explanation for a homogenous low-density acute subdural hematoma.

The Neuroimaging Spectrum of Septum Posticum Derangement and Associated Thoracic Myelopathy.

BACKGROUND: Arachnoid cysts and meningeal membranes are among the differential diagnostic considerations of extra-medullary causes of thoracic myelopathy. In this case series of 7 patients, we present compressive meningeal membranes mimicking dorsal arachnoid cyst. The propensity of the meningeal membranes for the dorsal aspect of upper thoracic spine may reflect derangements of the septum posticum. OBJECTIVE: To provide the spectrum of imaging appearances and clinical presentations of pathology of the septum posticum to improve imaging utilization and to better guide treatment planning. METHODS: Seven patients aged 40 to 75 with MRI findings of ventral displacement and dorsal cord compression in the upper thoracic spine were further evaluated with CT-myelograms. The primary indication was to exclude dorsal arachnoid cyst. Two patients with progressive symptoms and lower extremity weakness were operated for decompression. RESULTS: CT-myelogram excluded space occupying lesions and cord herniation in all cases. Intradural dorsal meningeal webs and membranes were inconsistently visualized. In the 2 operated cases, thick coalescing membranes and hyperdynamic turbulent CSF flow were severely compressing the thoracic cord. CONCLUSION: Derangements of septum posticum may present a spectrum of findings that should be considered in the differential of thoracic myelopathy. Flattening of the posterior cord margin is a reliable imaging clue for a dorsal extra-medullary compressive lesion. Cord compression results from combination of adhesive membranes and turbulent CSF flow. The clinical course may be difficult to predict. Periodic imaging follow up can be helpful to confirm stability of findings in expectantly managed cases.