Visualization of Culprit Perforators in Anterolateral Pontine Infarction: High-Resolution Magnetic Resonance Imaging Study

Diagnosis of Unruptured Intracranial Aneurysms Using Proton-Density Magnetic Resonance Angiography: A Comparison With High-Resolution Time-of-Flight Magnetic Resonance Angiography

OBJECTIVE

Differentiating intracranial aneurysms from normal variants using CT angiography (CTA) or MR angiography (MRA) poses significant challenges. This study aimed to evaluate the efficacy of proton-density MRA (PD-MRA) compared to high-resolution time-of-flight MRA (HR-MRA) in diagnosing aneurysms among patients with indeterminate findings on conventional CTA or MRA.

MATERIALS AND METHODS

In this retrospective analysis, we included patients who underwent both PD-MRA and HR-MRA from August 2020 to July 2022 to assess lesions deemed indeterminate on prior conventional CTA or MRA examinations. Three experienced neuroradiologists independently reviewed the lesions using HR-MRA and PD-MRA with reconstructed voxel sizes of 0.253 mm3 or 0.23 mm3, respectively. A neurointerventionist established the gold standard with digital subtraction angiography. We compared the performance of HR-MRA, PD-MRA (0.253-mm3 voxel), and PD-MRA (0.23-mm3 voxel) in diagnosing aneurysms, both per lesion and per patient. The Fleiss kappa statistic was used to calculate inter-reader agreement.

RESULTS

The study involved 109 patients (average age 57.4 ± 11.0 years; male:female ratio, 11:98) with 141 indeterminate lesions. Of these, 78 lesions (55.3%) in 69 patients were confirmed as aneurysms by the reference standard. PD-MRA (0.253-mm3 voxel) exhibited significantly higher per-lesion diagnostic performance compared to HR-MRA across all three readers: sensitivity ranged from 87.2%-91.0% versus 66.7%-70.5%; specificity from 93.7%-96.8% versus 58.7%-68.3%; and accuracy from 90.8%-92.9% versus 63.8%-69.5% (P ≤ 0.003). Furthermore, PD-MRA (0.253-mm3 voxel) demonstrated significantly superior per-patient specificity and accuracy compared to HR-MRA across all evaluators (P ≤ 0.013). The diagnostic accuracy of PD-MRA (0.23-mm3 voxel) surpassed that of HR-MRA and was comparable to PD-MRA (0.253-mm3 voxel). The kappa values for inter-reader agreements were significantly higher in PD-MRA (0.820-0.938) than in HR-MRA (0.447-0.510).

CONCLUSION

PD-MRA outperformed HR-MRA in diagnostic accuracy and demonstrated almost perfect inter-reader consistency in identifying intracranial aneurysms among patients with lesions initially indeterminate on CTA or MRA.

Higher Prevalence of Diabetes in Pontine Infarction than in Other Posterior Circulation Strokes

BACKGROUND

Pontine infarction is the major subtype of posterior circulation stroke, and diabetes is more common in pontine infarction patients than in anterior circulation stroke patients. Whether the prevalence of diabetes remains homogenous within the posterior circulation stroke population is unclear. The present study is aimed at investigating the prevalence of diabetes in pontine infarction and comparing it to other subtypes of posterior circulation stroke.

METHODS

We conducted a multicenter case-control study. Patients with posterior circulation stroke were screened. The subjects were divided into pontine infarction and nonpontine infarction groups.

RESULTS

From November 1, 2018, to February 28, 2021, a total of 6145 stroke patients were screened and 2627 patients had posterior circulation strokes. After excluding cardioembolic stroke, as well as its other determined and undetermined causes, 1549 patients with 754 pontine infarctions were included in the analysis. The prevalence of diabetes in the pontine infarction group was higher than that in the nonpontine infarction group (42.7% vs. 31.4%, P < 0.05). After adjusting for confounding factors, diabetes was an independent risk factor for pontine infarction (OR 1.63, 95% CI 1.27-2.09, P < 0.05). For small vessel occlusion, diabetes was also more common in the pontine infarction group (43.2% vs. 30.0%, P < 0.05). Multivariate analysis also showed that diabetes was an independent risk factor for pontine infarction (OR 1.80, 95% CI 1.32-2.46, P < 0.05).

CONCLUSION

In comparison with the nonpontine infarction subtype of posterior circulation stroke, patients with pontine infarction had a higher prevalence of diabetes, and diabetes was an independent risk factor for pontine infarction.

Added diagnostic values of three-dimensional high-resolution proton density-weighted magnetic resonance imaging for unruptured intracranial aneurysms in the circle-of-Willis: Comparison with time-of-flight magnetic resonance angiography

Background

Advanced imaging methods can enhance the identification of aneurysms of the infundibula, which can reduce unnecessary follow-ups or further work-up, fear, and anxiety in patients.

Purpose

This study aimed to evaluate the added diagnostic value of three-dimensional proton density-weighted vessel wall magnetic resonance imaging (3D-PD MRI) in identifying aneurysms from index lesions refer to vascular bulging lesions without vessels arising from the apex, observed using volume-rendered TOF-MRA in the circle-of-Willis compared with time-of-flight magnetic resonance angiography (TOF-MRA).

Study type

Retrospective.

Population

A total of 299 patients who underwent 3D-PD MRI, digital subtraction angiography (DSA), and TOF-MRA between January 2012 and December 2016 were retrospectively enrolled in this study.

Field strength/sequence

3 Tesla, 3D-PD MRI.

Assessment

Three neuroradiologists independently evaluated TOF-MRA and 3D-PD MRI combined with TOF-MRA for the determination of intracranial aneurysms in patients with index lesions within the circle of Willis. Final diagnoses were made by another neuroradiologist and neurointerventionist in consensus using DSA as the reference standard. The diagnostic performance and proportions of undetermined lesions on TOF-MRA and 3D-PD MRI with TOF-MRA were assessed based on the final diagnoses.

Statistical tests

The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for the diagnosis of unruptured intracranial aneurysms were calculated for each imaging modality.

Results

Of 452 lesions identified on volume-rendered TOF-MRA images, 173 (38%) aneurysms and 276 (61%) infundibula were finally diagnosed on DSA. 3D-PD MRI with TOF-MRA showed superior diagnostic performance (p = .001; accuracy, 85.5% versus 95.4%), superior area under the receiver operating characteristic curve over TOF-MRA (p = .001; 0.837 versus 0.947), and a lower proportion of undetermined lesions than TOF-MRA (p = .001; 25.1% versus 2.3%).

Data conclusion

For unruptured intracranial aneurysms in the circle of Willis, 3D-PD MRI can complement TOF-MRA to improve diagnostic performance and lower the proportion of undetermined lesions.

Deep-learning-based image quality enhancement of compressed sensing magnetic resonance imaging of vessel wall: comparison of self-supervised and unsupervised approaches

While high-resolution proton density-weighted magnetic resonance imaging (MRI) of intracranial vessel walls is significant for a precise diagnosis of intracranial artery disease, its long acquisition time is a clinical burden. Compressed sensing MRI is a prospective technology with acceleration factors that could potentially reduce the scan time. However, high acceleration factors result in degraded image quality. Although recent advances in deep-learning-based image restoration algorithms can alleviate this problem, clinical image pairs used in deep learning training typically do not align pixel-wise. Therefore, in this study, two different deep-learning-based denoising algorithms-self-supervised learning and unsupervised learning-are proposed; these algorithms are applicable to clinical datasets that are not aligned pixel-wise. The two approaches are compared quantitatively and qualitatively. Both methods produced promising results in terms of image denoising and visual grading. While the image noise and signal-to-noise ratio of self-supervised learning were superior to those of unsupervised learning, unsupervised learning was preferable over self-supervised learning in terms of radiomic feature reproducibility.