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.

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.

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: Intra-cranial vessel wall imaging (IC-VWI) 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 (DLBIR) T1 SPACE sequences were evaluated, comparing non-contrast sequences in ten healthy controls and post-contrast sequences in five consecutive patients. Images were reviewed on a Likert-like scale by four fellowship-trained neuroradiologists. Scores (range 1-4) were separately assigned for eleven vessel segments in terms of vessel wall and lumen delineation. Additionally, images were evaluated in terms of overall background noise, image sharpness and homogenous CSF signal. Segment-wise scores were compared using paired samples t-tests. RESULTS: The scan time for the clinical and DLBIR sequences were 7:26 minutes and 5:23 minutes respectively. DLBIR images showed consistently higher wall signal and lumen visualization scores, with the differences being statistically significant in the majority of vessel segments on both pre and post contrast images. DLBIR images had lower background noise, higher image sharpness and uniform CSF signal. Depiction of intracranial pathologies was better or similar on the DLBIR images. CONCLUSIONS: Our preliminary findings suggest that DLBIR optimized IC-VWI 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 ICVWI in clinical practice and should be further validated on a larger cohort. ABBREVIATIONS: DL deep learning; VWI = vessel wall imaging.

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.

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.

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.

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.

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.

Benefits of Photon-Counting CT Myelography for Localization of Dural Tears in Spontaneous Intracranial Hypotension.

Photon-counting CT is an increasingly used technology with numerous advantages over conventional energy-integrating detector CT. These include superior spatial resolution, high temporal resolution, and inherent spectral imaging capabilities. Recently, photon-counting CT myelography was described as an effective technique for the detection of CSF-venous fistulas, a common cause of spontaneous intracranial hypotension. It is likely that photon-counting CT myelography will also have advantages for the localization of dural tears, a separate type of spontaneous spinal CSF leak that requires different myelographic techniques for accurate localization. To our knowledge, prior studies on photon-counting CT myelography have been limited to techniques for detecting CSF-venous fistulas. In this technical report, we describe our technique and early experience with photon-counting CT myelography for the localization of dural tears.

High yield clinical applications for photon counting CT in neurovascular imaging.

Photon-counting CT (PCCT) uses a novel X-ray detection mechanism that confers many advantages over that used in traditional energy integrating CT. As PCCT becomes more available, it is important to thoroughly understand its benefits and highest yield areas for improvements in diagnosis of various diseases. Based on our early experience, we have identified several areas of neurovascular imaging in which PCCT shows promise. Here, we describe the benefits in diagnosing arterial and venous diseases in the head, neck, and spine. Specifically, we focus on applications in head and neck CT angiography (CTA), spinal CT angiography, and CT myelography for detection of CSF-venous fistulas. Each of these applications highlights the technological advantages of PCCT in neurovascular imaging. Further understanding of these applications will not only benefit institutions incorporating PCCT into their practices but will also help guide future directions for implementation of PCCT for diagnosing other pathologies in neuroimaging.

Intracranial findings in spontaneous intracranial hypotension: Does the severity of abnormalities correspond with certainty and/or multifocality of cerebrospinal fluid leaks?

BACKGROUND AND PURPOSE: Spontaneous intracranial hypotension (SIH) is caused by spinal cerebrospinal fluid (CSF) leaks. This study assessed whether the certainty and/or multifocality of CSF leaks is associated with the severity of intracranial sequelae of SIH. MATERIALS AND METHODS: A retrospective review was completed of patients with suspected SIH that underwent digital subtraction myelogram (DSM) preceded by brain MRI. DSMs were evaluated for the presence or absence of a CSF leak, categorized both as positive/negative/indeterminate and single versus multifocal. Brain MRIs were assessed for intracranial sequelae of SIH based on two probabilistic scoring systems (Dobrocky and Mayo methods). For each system, both an absolute "numerical" score (based on tabulation of findings) and "categorized" score (classification of probability) were tabulated. RESULTS: 174 patients were included; 113 (64.9%) were female, average age 52.0 ± 14.3 years. One or more definite leaks were noted in 76 (43.7%) patients; an indeterminate leak was noted in 22 (12.6%) patients. 16 (16.3%) had multiple leaks. There was no significant difference in the severity of intracranial findings between patients with a single versus multiple leaks (p values ranged from .36 to .70 using categorized scores and 0.22-0.99 for numerical scores). Definite leaks were more likely to have both higher categorized intracranial scores (Mayo p = .0008, Dobrocky p = .006) and numerical scores (p = .0002 for Mayo and p = .006 for Dobrocky). CONCLUSIONS: Certainty of a CSF leak on diagnostic imaging is associated with severity of intracranial sequelae of SIH, with definite leaks having significantly more intracranial findings than indeterminate leaks. Multifocal leaks do not cause greater intracranial abnormalities.

Imaging Findings Post-Stereotactic Radiosurgery for Vestibular Schwannoma: A Primer for the Radiologist.

Noninvasive tumor control of vestibular schwannomas through stereotactic radiosurgery allows high rates of long-term tumor control and has been used primarily for small- and medium-sized vestibular schwannomas. The posttreatment imaging appearance of the tumor, temporal patterns of growth and treatment response, as well as extratumoral complications can often be both subtle or confusing and should be appropriately recognized. Herein, the authors present an imaging-based review of expected changes as well as associated complications related to radiosurgery for vestibular schwannomas.

Identifying Patients with CSF-Venous Fistula Using Brain MRI: A Deep Learning Approach.

BACKGROUND AND PURPOSE: Spontaneous intracranial hypotension is an increasingly recognized condition. Spontaneous intracranial hypotension is caused by a CSF leak, which is commonly related to a CSF-venous fistula. In patients with spontaneous intracranial hypotension, multiple intracranial abnormalities can be observed on brain MR imaging, including dural enhancement, "brain sag," and pituitary engorgement. This study seeks to create a deep learning model for the accurate diagnosis of CSF-venous fistulas via brain MR imaging. MATERIALS AND METHODS: A review of patients with clinically suspected spontaneous intracranial hypotension who underwent digital subtraction myelogram imaging preceded by brain MR imaging was performed. The patients were categorized as having a definite CSF-venous fistula, no fistula, or indeterminate findings on a digital subtraction myelogram. The data set was split into 5 folds at the patient level and stratified by label. A 5-fold cross-validation was then used to evaluate the reliability of the model. The predictive value of the model to identify patients with a CSF leak was assessed by using the area under the receiver operating characteristic curve for each validation fold. RESULTS: There were 129 patients were included in this study. The median age was 54 years, and 66 (51.2%) had a CSF-venous fistula. In discriminating between positive and negative cases for CSF-venous fistulas, the classifier demonstrated an average area under the receiver operating characteristic curve of 0.8668 with a standard deviation of 0.0254 across the folds. CONCLUSIONS: This study developed a deep learning model that can predict the presence of a spinal CSF-venous fistula based on brain MR imaging in patients with suspected spontaneous intracranial hypotension. However, further model refinement and external validation are necessary before clinical adoption. This research highlights the substantial potential of deep learning in diagnosing CSF-venous fistulas by using brain MR imaging.

Prevalence of Scarpa's ganglion enhancement on high-resolution MRI imaging.

BACKGROUND AND PURPOSE: The vestibular ganglion, or Scarpa's ganglion, is a cluster of afferent vestibular neurons within the internal auditory canal (IAC). There is minimal literature describing enhancement of this region on magnetic resonance imaging (MRI) and its correlation to clinical symptoms. Here, we sought to find the prevalence of enhancement at Scarpa's ganglion, and determine whether such enhancement correlates with demographics or clinical symptoms. MATERIALS AND METHODS: A retrospective review was performed of consecutive patients with an MRI of the IAC between 3/1/2021 and 5/20/2021. Two neuroradiologists independently reviewed for T1 and FLAIR enhancement of the Scarpa's ganglion on post-contrast fat-saturated T1 and post-contrast FLAIR images. Discrepancies were agreed upon by consensus. Clinical variables (hearing loss, vestibular symptoms, tinnitus, and MRI indication) were gathered from a retrospective chart review. RESULTS: Eighty-nine patients were included (51 female); the mean age was 58 (range 19-85). The most common MRI indication was hearing loss (n = 53). FLAIR enhancement was present on the right in 7 patients, on the left in 7 patients, and bilaterally in 6 patients. No enhancement was seen on post-contrast T1 images. There was no statistically significant correlation between consensus FLAIR on at least one side and age (p = .74), gender (p = .29), hearing loss (p = .32), hearing loss side (p = .39), type of hearing loss (p = .87), vestibular symptoms (p = .71), or tinnitus (p = .81). CONCLUSIONS: Enhancement is present in the minority of patients on post-contrast FLAIR images. If seen, it should be considered an uncommon but not unexpected finding with no clinical significance.

Safety and histopathologic yield of percutaneous CT-guided biopsies of the skull base, orbit, and calvarium.

PURPOSE: Although CT-guided biopsies of the calvarium, skull base, and orbit are commonly performed, the best approaches, efficacy, and safety of such procedures remain scantly described in the literature. This retrospective review of percutaneous biopsies illustrates several approaches to challenging biopsy targets and provides a review of procedural planning considerations and histopathologic yield. METHODS: A retrospective review of CT-guided biopsies of the skull base, calvarium, and orbit between 1/1/2010 and 10/30/2020 was conducted. Patient demographics and procedural factors were recorded, including lesion size and location, biopsy approach, and needle gauge. Outcomes were also noted, including CT dose length product, complications, and histopathologic yield. RESULTS: Sixty-one CT-guided biopsies were included in the final analysis: 34 skull base, 23 calvarial, and 4 orbital lesions. The initial diagnostic yield was 32/34 (94%) for skull base lesions, with one false-negative and one non-diagnostic sample. Twenty-one of twenty-three (91%) biopsies in the calvarium were initially diagnostic, with one false-negative and one non-diagnostic sample. In the orbit, 4/4 biopsies were diagnostic. The total complication rate for the cohort was 4/61 (6.6%). Three complications were reported in skull base procedures (2 immediate and 1 delayed). A single complication was reported in a calvarial biopsy, and no complications were reported in orbital biopsies. CONCLUSION: Percutaneous CT-guided core needle biopsies can be performed safely and with a high diagnostic yield for lesions in the skull base, calvarium, and orbit.

Pituitary volume changes in pregnancy and the post-partum period.

PURPOSE: Imaging changes in the pituitary volume during pregnancy remains scantly researched. This study set out to assess the differences in total, anterior, and posterior pituitary volume in pregnant women compared to nulliparous and post-partum women. MATERIALS AND METHODS: A retrospective review was completed of women that had undergone MRI imaging of the brain. Patients were divided into three cohorts: pregnant, nulliparous, and post-partum (defined as being within 12 months of delivery). Anterior and posterior pituitary volumes were manually measured. RESULTS: 171 patients were included, of which 68 were pregnant, 52 were post-partum, and 51 were nulliparous. The average anterior (621.0 ± 171.6 mm3) and total (705.4 ± 172.2 mm3) pituitary volumes were significantly larger in pregnant patients than nulliparous women (522.6 ± 159.8 mm3 and 624.5 ± 163.7 mm3, respectively) (p = .002 and p = .01, respectively). The posterior pituitary volume was significantly smaller in pregnant women (84.4 ± 32.9 mm3) compared to both post-partum (101.2 ± 42.0 mm3) and nulliparous (102.0 ± 46.1 mm3) women (p = .02 for both). CONCLUSIONS: The anterior and total pituitary volumes are significantly larger during pregnancy persisting into the post-partum period. The posterior pituitary volume, conversely, decreases during pregnancy, and returns to its normal size in the post-partum period.