Dual Source Photon-Counting Computed Tomography-Part II: Clinical Overview of Neurovascular Applications

Carotid stenosis and cryptogenic stroke

OBJECTIVES

Cryptogenic stroke represents a type of ischemic stroke with an unknown origin, presenting a significant challenge in both stroke management and prevention. According to the Trial of Org 10,172 in Acute Stroke Treatment criteria, a stroke is categorized as being caused by large artery atherosclerosis only when there is >50% luminal narrowing of the ipsilateral internal carotid artery. However, nonstenosing carotid artery plaques can be an underlying cause of ischemic stroke. Indeed, emerging evidence documents that some features of plaque vulnerability may act as an independent risk factor, regardless of the degree of stenosis, in precipitating cerebrovascular events. This review, drawing from an array of imaging-based studies, explores the predictive values of carotid imaging modalities in the detection of nonstenosing carotid plaque (<50%), that could be the cause of a cerebrovascular event when some features of vulnerability are present.

METHODS

Google Scholar, Scopus, and PubMed were searched for articles on cryptogenic stroke and those reporting the association between cryptogenic stroke and imaging features of carotid plaque vulnerability.

RESULTS

Despite extensive diagnostic evaluations, the etiology of a considerable proportion of strokes remains undetermined, contributing to the recurrence rate and persistent morbidity in affected individuals. Advances in imaging modalities, such as magnetic resonance imaging, computed tomography scans, and ultrasound examination, facilitate more accurate detection of nonstenosing carotid artery plaque and allow better stratification of stroke risk, leading to a more tailored treatment strategy.

CONCLUSIONS

Early detection of nonstenosing carotid plaque with features of vulnerability through carotid imaging techniques impacts the clinical management of cryptogenic stroke, resulting in refined stroke subtype classification and improved patient management. Additional research is required to validate these findings and recommend the integration of these state-of-the-art imaging methodologies into standard diagnostic protocols to improve stroke management and prevention.

Quantifying Carotid Stenosis: History, Current Applications, Limitations, and Potential: How Imaging Is Changing the Scenario

Carotid artery stenosis is a major cause of morbidity and mortality. The journey to understanding carotid disease has developed over time and radiology has a pivotal role in diagnosis, risk stratification and therapeutic management. This paper reviews the history of diagnostic imaging in carotid disease, its evolution towards its current applications in the clinical and research fields, and the potential of new technologies to aid clinicians in identifying the disease and tailoring medical and surgical treatment.

Machine learning detects symptomatic patients with carotid plaques based on 6-type calcium configuration classification on CT angiography

OBJECTIVES

While the link between carotid plaque composition and cerebrovascular vascular (CVE) events is recognized, the role of calcium configuration remains unclear. This study aimed to develop and validate a CT angiography (CTA)-based machine learning (ML) model that uses carotid plaques 6-type calcium grading, and clinical parameters to identify CVE patients with bilateral plaques.

MATERIAL AND METHODS

We conducted a multicenter, retrospective diagnostic study (March 2013-May 2020) approved by the institutional review board. We included adults (18 +) with bilateral carotid artery plaques, symptomatic patients having recently experienced a carotid territory ischemic event, and asymptomatic patients either after 3 months from symptom onset or with no such event. Four ML models (clinical factors, calcium configurations, and both with and without plaque grading [ML-All-G and ML-All-NG]) and logistic regression on all variables identified symptomatic patients. Internal validation assessed discrimination and calibration. External validation was also performed, and identified important variables and causes of misclassifications.

RESULTS

We included 790 patients (median age 72, IQR [61-80], 42% male, 64% symptomatic) for training and internal validation, and 159 patients (age 68 [63-76], 36% male, 39% symptomatic) for external testing. The ML-All-G model achieved an area-under-ROC curve of 0.71 (95% CI 0.58-0.78; p < .001) and sensitivity 80% (79-81). Performance was comparable on external testing. Calcified plaque, especially the positive rim sign on the right artery in older and hyperlipidemic patients, had a major impact on identifying symptomatic patients.

CONCLUSION

The developed model can identify symptomatic patients using plaques calcium configuration data and clinical information with reasonable diagnostic accuracy.

CLINICAL RELEVANCE

The analysis of the type of calcium configuration in carotid plaques into 6 classes, combined with clinical variables, allows for an effective identification of symptomatic patients.

KEY POINTS

• While the association between carotid plaques composition and cerebrovascular events is recognized, the role of calcium configuration remains unclear. • Machine learning of 6-type plaque grading can identify symptomatic patients. Calcified plaques on the right artery, advanced age, and hyperlipidemia were the most important predictors. • Fast acquisition of CTA enables rapid grading of plaques upon the patient's arrival at the hospital, which streamlines the diagnosis of symptoms using ML.

Utility of follow-up ultra-high-resolution CT angiography with model-based iterative reconstruction after flow diverter treatment for cerebral aneurysms

PURPOSE

Follow-up examinations after flow diverter (FD) treatment for cerebral aneurysms typically involve magnetic resonance imaging (MRI) or digital subtraction angiography (DSA). However, MRI is prone to vascular defects due to metal artifacts from FD, and DSA carries a risk of ischemic complications. In the context of computed tomography angiography (CTA), this study compares the efficacy of ultra-high-resolution CT (UHRCT) and novel reconstruction techniques, such as model-based iterative reconstruction (MBIR), against conventional methods such as filtered back projection (FBP) and hybrid iterative reconstruction (IR), to determine if they are a viable alternative to DSA in clinical settings.

MATERIALS AND METHODS

A phantom study was conducted with the full-width half-maximum considered as the FD thickness. This study compared three reconstruction methods: MBIR, FBP, and hybrid IR. A clinical study was also conducted with 21 patients who underwent follow-up CTA after FD treatment. The FD's visibility was assessed using a 4-point scale in FBP, hybrid IR, and MBIR compared to cone-beam CT (CBCT) with angiographic systems.

RESULTS

In the phantom study, FBP, hybrid IR, and MBIR visualized thinner FD thicknesses and improved detail rendering in that order. MBIR proved to be significantly superior in both the phantom and clinical study.

CONCLUSION

UHRCT with MBIR is highly effective for follow-up evaluations after FD treatment and may become the first-choice modality in the future.