Myelographic Techniques for the Localization of CSF-Venous Fistulas: Updates in 2024.

CSF-venous fistulas (CVFs) are a common cause of spontaneous intracranial hypotension. Despite their relatively frequent occurrence, they can be exceedingly difficult to detect on imaging. Since the initial description of CVFs in 2014, the recognition and diagnosis of this type of CSF leak has continually increased. As a result of multi-institutional efforts, a wide spectrum of imaging modalities and specialized techniques for CVF detection is now available. It is important for radiologists to be familiar with the multitude of available techniques, because each has unique advantages and drawbacks. In this article, we review the spectrum of imaging modalities available for the detection of CVFs, explain the advantages and disadvantages of each, provide typical imaging examples, and discuss provocative maneuvers that may improve the conspicuity of CVFs. Discussed modalities include conventional CT myelography, dynamic myelography, digital subtraction myelography, conebeam CT myelography, decubitus CT myelography by using conventional energy-integrating detector scanners, decubitus photon counting CT myelography, and intrathecal gadolinium MR myelography. Additional topics to be discussed include optimal patient positioning, respiratory techniques, and intrathecal pressure augmentation.

CT-Guided Epidural Contrast Injection for the Identification of Dural Defects.

Post-dural puncture headache (PDPH) is an increasingly recognized cause of chronic headache. Outside of clinical history and myelography that requires an additional dural puncture, there is no reliable diagnostic test to evaluate for persistent dural defects. We describe the injection of iodinated contrast into the dorsal epidural space under CT guidance in five patients as a potential tool to visualize persistent dural defects.ABBREVIATIONS: PDPH = post-dural puncture headache; SIH = spontaneous intracranial hypotension; DSM = digital subtraction myelography; CTM = CT myelography.

Soft tissue aneurysmal bone cyst presenting as an enlarging neck mass: Case report and review of the head and neck literature.

Soft tissue aneurysmal bone cysts (STABCs) are rare neoplasms histopathologically identical to aneurysmal bone cysts. These benign lesions are characterized by thin, peripheral ossification and no skeletal continuity. STABC may be difficult to distinguish from myositis ossificans (MO) and malignant entities from imaging and fine needle aspiration, due to rarity and overlapping features. We present a case of a STABC occurring in the paraspinal cervical muscles. The imaging, histopathology, molecular analysis, and treatment are discussed. Four other published cases of STABC of the head and neck are reviewed.

Photon-Counting CT in the Head and Neck: Current Applications and Future Prospects.

Photon-counting detectors (PCDs) represent a major milestone in the evolution of CT imaging. CT scanners using PCD systems have already been shown to generate images with substantially greater spatial resolution, superior iodine contrast-to-noise ratio, and reduced artifact compared with conventional energy-integrating detector-based systems. These benefits can be achieved with considerably decreased radiation dose. Recent studies have focused on the advantages of PCD-CT scanners in numerous anatomic regions, particularly the coronary and cerebral vasculature, pulmonary structures, and musculoskeletal imaging. However, PCD-CT imaging is also anticipated to be a major advantage for head and neck imaging. In this paper, we review current clinical applications of PCD-CT in head and neck imaging, with a focus on the temporal bone, facial bones, and paranasal sinuses; minor arterial vasculature; and the spectral capabilities of PCD systems.

Visual acuity and optic nerve size assessed by magnetic resonance imaging in optic nerve hypoplasia.

We investigated the relationship between optic nerve (ON) size and visual acuity in children with optic nerve hypoplasia (ONH). The medical records of patients <19 years with ONH who underwent brain magnetic resonance imaging (MRI) and visual acuity assessment were reviewed. ON diameter at orbital and cisternal segments was assessed independently by two neuroradiologists and compared with visual acuity. ON diameter <1.7 mm represented a cutoff, below which was significantly associated with visual acuity of 20/200 or worse (P = 0.04) and above which was significantly associated with visual acuity of 20/40 or better (P = 0.004). ON diameter measured with MRI may provide an early prognostic indication of visual potential for children with ONH.

Prevalence of Tendon Rupture and Tendinopathies Among Patients with Atherosclerotic Cardiovascular Disease Derived From United States Administrative Claims Data.

INTRODUCTION: The prevalence of tendon rupture and tendinopathies (TRT) has not been determined in a large population of patients with atherosclerotic cardiovascular disease (ASCVD). We investigated TRT prevalence among patients with ASCVD and in the general population, using data from the Symphony Health Integrated Dataverse, a large US medical and pharmacy claims database. METHODS: This retrospective, observational study included patients aged ≥ 19 years from the claims database during the identification period (January 2019 to December 2020) and 12 months of continuous enrollment. The primary outcome was evidence of TRT in the 12 months following the index date (first ASCVD diagnosis in the ASCVD cohort; first claim in the claims database in the overall population). Diagnostic codes (ICD-10 and/or CPT) were used to define ASCVD and TRT diagnosis. RESULTS: The ASCVD cohort and overall population included 5,589,273 and 61,715,843 patients, respectively. In the ASCVD cohort, use of medications with a potential or known association with TRT was identified in 67.9% (statins), 17.7% (corticosteroids), and 16.7% (fluoroquinolones) of patients. Bempedoic acid use was reported in 1556 (< 0.1%) patients. TRT prevalence during 12-month follow-up was 3.4% (ASCVD cohort) and 1.9% (overall population). Among patients with ASCVD, 83.5% experienced TRT in only one region of the body. Factors most associated with TRT in the ASCVD cohort were increasing age, most notably in those aged 45-|64 years (odds ratio [OR] 2.19; 95% confidence interval [CI] 2.07-2.32), obesity (OR 1.51; 95% CI 1.50-1.53), and rheumatoid arthritis (OR 1.47; 95% CI 1.45-1.79). Use of statins or bempedoic acid was not associated with increased TRT risk. CONCLUSION: Patients with ASCVD may have greater risk of TRT than the general population, which may be driven by an increased prevalence of comorbidities and use of medications with a potential or known association with TRT.

Patients with atherosclerosis, the main cause of heart attacks, strokes, and peripheral vascular disease, typically require several drugs to control the disease. Some of the drugs used to treat atherosclerosis have been linked to a higher occurrence of tendon tears (or ruptures) or swelling/inflammation of the tendons (tendinopathies). However, there may be other factors present in these patients that increase the risk of tendon injuries that are not related to these drugs. This study used the medical records of over 5.5 million patients with atherosclerosis and over 63 million patients reflecting the general population in the United States to determine the prevalence of tendon injury. Additionally, the researchers looked at other factors that might be related to a higher risk of tendon injury in each group. Over a 12-month period, tendon injuries occurred in 3.4% of patients with atherosclerosis and 1.8% of patients in the general population. In patients with atherosclerosis, factors such as being obese, older (45­64 years), or having rheumatoid arthritis were also linked to an increased risk of tendon injuries. There was no association seen between statin or bempedoic acid use and tendon injuries. These results may help healthcare providers to determine the underlying risk of tendon injuries and guide treatment of this patient population.

Atherosclerotic carotid artery disease Radiomics: A systematic review with meta-analysis and radiomic quality score assessment.

BACKGROUND: Stroke, a leading global cause of mortality and neurological disability, is often associated with atherosclerotic carotid artery disease. Distinguishing between symptomatic and asymptomatic carotid artery disease is crucial for appropriate treatment decisions. Radiomics, a quantitative image analysis technique, and ML have emerged as promising tools in medical imaging, including neuroradiology. This systematic review and meta-analysis aimed to evaluate the methodological quality of studies employing radiomics for atherosclerotic carotid artery disease analysis and ML algorithms for culprit plaque identification using CT or MRI. MATERIALS AND METHODS: Pubmed, WoS and Scopus databases were searched for relevant studies published from January 2005 to May 2023. RQS assessed methodological quality of studies included in the review. QUADAS-2 assessed the risk of bias. A meta-analysis and three meta regressions were conducted on study performance based on model type, imaging modality and segmentation method. RESULTS: RQS assessed methodological quality, revealing an overall low score and consistent findings with other radiology domains. QUADAS-2 indicated an overall low risk, except for a single study with high bias. The meta-analysis demonstrated that radiomics-based ML models for predicting culprit plaques had a satisfactory performance, with an AUC of 0.85, surpassing clinical models. However, combining radiomics with clinical features yielded the highest AUC of 0.89. Meta-regression analyses confirmed these findings. MRI-based models slightly outperformed CT-based ones, but the difference was not significant. CONCLUSION: In conclusion, radiomics and ML hold promise for assessing carotid plaque vulnerability, aiding in early cerebrovascular event prediction. Combining radiomics with clinical data enhances predictive performance.

Machine Learning Detects Symptomatic Plaques in Patients With Carotid Atherosclerosis on CT Angiography.

BACKGROUND: This study aimed to develop and validate a computed tomography angiography based machine learning model that uses plaque composition data and degree of carotid stenosis to detect symptomatic carotid plaques in patients with carotid atherosclerosis. METHODS: The machine learning based model was trained using degree of stenosis and the volumes of 13 computed tomography angiography derived intracarotid plaque subcomponents (eg, lipid, intraplaque hemorrhage, calcium) to identify plaques associated with cerebrovascular events. The model was internally validated through repeated 10-fold cross-validation and tested on a dedicated testing cohort according to discrimination and calibration. RESULTS: This retrospective, single-center study evaluated computed tomography angiography scans of 268 patients with both symptomatic and asymptomatic carotid atherosclerosis (163 for the derivation set and 106 for the testing set) performed between March 2013 and October 2019. The area-under-receiver-operating characteristics curve by machine learning on the testing cohort (0.89) was significantly higher than the areas under the curve of traditional logit analysis based on the degree of stenosis (0.51, P<0.001), presence of intraplaque hemorrhage (0.69, P<0.001), and plaque composition (0.78, P<0.001), respectively. Comparable performance was obtained on internal validation. The identified plaque components and associated cutoff values that were significantly associated with a higher likelihood of symptomatic status after adjustment were the ratio of intraplaque hemorrhage to lipid volume (≥50%, 38.5 [10.1-205.1]; odds ratio, 95% CI) and percentage of intraplaque hemorrhage volume (≥10%, 18.5 [5.7-69.4]; odds ratio, 95% CI). CONCLUSIONS: This study presented an interpretable machine learning model that accurately identifies symptomatic carotid plaques using computed tomography angiography derived plaque composition features, aiding clinical decision-making.

Deep Learning-Based Reconstruction of 3D T1 SPACE Vessel Wall Imaging Provides Improved Image Quality with Reduced Scan Times: A Preliminary Study.

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.

Carotid artery calcium score: Definition, classification, application, and limits.

INTRODUCTION: In the current paper, the "carotid artery calcium score" method is presented with the target to offer a metric method to quantify the amount of calcification in the carotid artery. MODEL AND DEFINITION: The Volume of Interest (VOI) should be extracted and those voxels, with a Hounsfield Unit (HU) value ≥130, should be considered. The total weight value is determined by calculating the sum of the HU attenuation values of all voxels with values ≥130 HU. This value should be multiplied by the conversion factor ("or voxel size") and divided by a weighting factor, the attenuation threshold to consider a voxel as calcified (and therefore 130 HU): this equation determines the Carotid Artery Calcium Score (CACS). RESULTS: In order to provide the demonstration of the potential feasibility of the model, the CACS was calculated in 131 subjects (94 males; mean age 72.7 years) for 235 carotid arteries (in 27 subjects, unilateral plaque was present) considered. The CACS value ranged from 0.67 to 11716. A statistically significant correlation was found (rho value = 0.663, p value = .0001) between the CACS in the right and left carotid plaques. Moreover, a statistically significant correlation between the age and the total CACS was present (rho value = 0.244, p value = .005), whereas no statistically significant difference was found in the distribution of CACS by gender (p = .148). The CACS was also tested at baseline and after contrast and no statistically significant difference was found. CONCLUSION: In conclusion, this method is of easy application, and it weights at the same time the volume and the degree of calcification in a unique parameter. This method needs to be tested to verify its potential utility, similar to the coronary artery calcium score, for the risk stratification of the occurrence of cerebrovascular events of the anterior circulation. Further studies using this new diagnostic tool to determine the prognostic value of carotid calcium quantification are needed.

SNAPSHOT USA 2021: A third coordinated national camera trap survey of the United States.

SNAPSHOT USA is a multicontributor, long-term camera trap survey designed to survey mammals across the United States. Participants are recruited through community networks and directly through a website application (https://www.snapshot-usa.org/). The growing Snapshot dataset is useful, for example, for tracking wildlife population responses to land use, land cover, and climate changes across spatial and temporal scales. Here we present the SNAPSHOT USA 2021 dataset, the third national camera trap survey across the US. Data were collected across 109 camera trap arrays and included 1711 camera sites. The total effort equaled 71,519 camera trap nights and resulted in 172,507 sequences of animal observations. Sampling effort varied among camera trap arrays, with a minimum of 126 camera trap nights, a maximum of 3355 nights, a median 546 nights, and a mean 656 ± 431 nights. This third dataset comprises 51 camera trap arrays that were surveyed during 2019, 2020, and 2021, along with 71 camera trap arrays that were surveyed in 2020 and 2021. All raw data and accompanying metadata are stored on Wildlife Insights (https://www.wildlifeinsights.org/), and are publicly available upon acceptance of the data papers. SNAPSHOT USA aims to sample multiple ecoregions in the United States with adequate representation of each ecoregion according to its relative size. Currently, the relative density of camera trap arrays varies by an order of magnitude for the various ecoregions (0.22-5.9 arrays per 100,000 km2), emphasizing the need to increase sampling effort by further recruiting and retaining contributors. There are no copyright restrictions on these data. We request that authors cite this paper when using these data, or a subset of these data, for publication. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.

Radiomics and artificial intelligence: General notions and applications in the carotid vulnerable plaque.

Carotid atherosclerosis plays a substantial role in cardiovascular morbidity and mortality. Given the multifaceted impact of this disease, there has been increasing interest in harnessing artificial intelligence (AI) and radiomics as complementary tools for the quantitative analysis of medical imaging data. This integrated approach holds promise not only in refining medical imaging data analysis but also in optimizing the utilization of radiologists' expertise. By automating time consuming tasks, AI allows radiologists to focus on more pertinent responsibilities. Simultaneously, the capacity of AI in radiomics to extract nuanced patterns from raw data enhances the exploration of carotid atherosclerosis, advancing efforts in terms of (1) early detection and diagnosis, (2) risk stratification and predictive modeling, (3) improving workflow efficiency, and (4) contributing to advancements in research. This review provides an overview of general concepts related to radiomics and AI, along with their application in the field of carotid vulnerable plaque. It also offers insights into various research studies conducted on this topic across different imaging techniques.

The left atrial appendage: An enigmatic friend or foe and implications of closure.

The left atrial appendage (LAA) is often thought of as a vestigial organ serving as a nidus for clot formation in those with atrial fibrillation (A-fib). The LAA, however, has unique anatomy which allows it to serve special functions in the human body. Closing the LAA has been shown to decrease the risk of thromboembolic events in patients who cannot tolerate anticoagulation. Several methods of closure exist including percutaneous endocardial closure, epicardial closure, and surgical clipping. In addition to decreasing stroke risk, there appears to be physiologic changes that occur after LAA closure. This comprehensive review aims to describe the functions of the LAA, compare the different methods of closure, and propose a new method for identifying which patients may benefit from LAA closure versus anticoagulation based on each patients' individual comorbidities rather than their contraindications.

A review of histopathologic and radiologic features of non-atherosclerotic pathologies of the extracranial carotid arteries.

Diseases of the carotid arteries can be classified into different categories based on their origin. Atherosclerotic carotid disease remains the most encountered arterial wall pathology. However, other less-common non-atherosclerotic diseases can have detrimental clinical consequences if not appropriately recognized. The underlying histological features of each disease process may result in imaging findings that possess features that are obvious of the disease. However, some carotid disease processes may have histological characteristics that manifest as non-specific radiologic findings. The purpose of this manuscript is to review various non-atherosclerotic causes of carotid artery disease as well as their histologic-radiologic characteristics to aid in the appropriate recognition of these less-commonly encountered pathologies.

Radiology-pathology correlation: Giant tumefactive perivascular spaces.

Dilated perivascular spaces (PVSs) are common and easily recognized on imaging. However, rarer giant tumefactive PVSs (GTPVSs) can have unusual multilocular cystic configurations, and are often confused for other pathologic entities, including neoplasms, cystic infarctions, and neuroepithelial cysts. Because GTPVSs are scarcely encountered and even more infrequently operated upon, many radiologists are unaware of the imaging and pathologic features of these lesions. Here, a case of a resected GTPVS is presented, highlighting both its radiologic and histologic characteristics, and discussing how such lesions can be differentiated from their closest mimickers on imaging.

Impact of SUSAN Denoising and ComBat Harmonization on Machine Learning Model Performance for Malignant Brain Neoplasms.

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.