3D Enhancement Color Maps in the Characterization of Intracranial Atherosclerotic Plaques

7T-high resolution MRI-derived radiomic analysis for the identification of symptomatic intracranial atherosclerotic plaques

INTRODUCTION

High-resolution magnetic resonance imaging (HR-MRI) allows for detailed visualization of intracranial atherosclerotic plaques. Radiomics can be used as a tool for objective quantification of the plaque's characteristics. We analyzed the radiomics features (RFs) obtained from 7 T HR-MRI of patients with intracranial atherosclerotic disease (ICAD) to determine distinct characteristics of culprit and non-culprit plaques.

METHODS

Patients with stroke due to ICAD underwent HR-MRI. Culprit plaques in the vascular territory of the stroke were identified. Degree of stenosis, area degree of stenosis and plaque burden were calculated. A three-dimensional segmentation of the plaque was performed, and RFs were obtained. A machine learning model for prediction and identification of culprit plaques using significantly different RFs was evaluated.

RESULTS

The study included 33 patients with ICAD as stroke etiology. Univariate analysis revealed 24 RFs in pre-contrast MRI, 21 in post-contrast MRI, 13 RFs that were different between pre and post contrast MRIs. Additionally, six shape-based RFs significantly differed from culprit and non-culprit plaques. The random forest model achieved an accuracy rate of 81% (88% sensitivity and 75% specificity) in identifying culprit plaques in the independent testing dataset. This model successfully identified the culprit plaques in all patients during the testing phase.

DISCUSSION

Symptomatic plaques had a distinct signature RFs compared to other plaques within the same subject. A machine learning model built with RFs successfully identified the symptomatic atherosclerotic plaques in most cases. Radiomics is a promising tool for stratification of plaques in patients with ICAD.

Deep learning improves quality of intracranial vessel wall MRI for better characterization of potentially culprit plaques

Intracranial vessel wall imaging (VWI), which requires both high spatial resolution and high signal-to-noise ratio (SNR), is an ideal candidate for deep learning (DL)-based image quality improvement. Conventional VWI (Conv-VWI, voxel size 0.51 × 0.51 × 0.45 mm3) and denoised super-resolution DL-VWI (0.28 × 0.28 × 0.45 mm3) of 117 patients were analyzed in this retrospective study. Quality of the images were compared qualitatively and quantitatively. Diagnostic performance for identifying potentially culprit atherosclerotic plaques, using lesion enhancement and presence of intraplaque hemorrhage (IPH), was evaluated. DL-VWI significantly outperformed Conv-VWI in all image quality ratings (all P < .001). DL-VWI demonstrated higher SNR and contrast-to-noise ratio (CNR) than Conv-VWI, both in normal walls (basilar artery; SNR 4.83 ± 1.23 vs. 3.02 ± 0.59, P < .001) and lesions (contrast-enhanced images; SNR 22.12 ± 11.68 vs. 8.33 ± 3.26, P < .001). In the assessment of 86 lesions, DL-VWI showed higher confidence of detection (4.56 ± 0.55 vs. 2.62 ± 0.77, P < .001), more concordant IPH characterization (Cohen's Kappa 0.85 vs. 0.59) and greater enhancement. For culprit plaque identification, IPH exhibited higher sensitivity in DL-VWI compared to Conv-VWI (70.6% vs. 23.5%) and excellent specificity (94.3% vs. 94.3%). Deep learning application of intracranial vessel wall images successfully improved the quality and resolution of the images. This aided in detecting vessel wall lesions and intraplaque hemorrhage, and in identifying potentially culprit atherosclerotic plaques.

MR Imaging of the Cerebral Aneurysmal Wall for Assessment of Rupture Risk

The evaluation of unruptured intracranial aneurysms requires a comprehensive and multifaceted approach. The comprehensive analysis of aneurysm wall enhancement through high-resolution MRI, in tandem with advanced processing techniques like finite element analysis, quantitative susceptibility mapping, and computational fluid dynamics, has begun to unveil insights into the intricate biology of aneurysms. This enhanced understanding of the etiology, progression, and eventual rupture of aneurysms holds the potential to be used as a tool to triage patients to intervention versus observation. Emerging tools such as radiomics and machine learning are poised to contribute significantly to this evolving landscape of diagnostic refinement.

Qualitative and quantitative plaque enhancement on high-resolution vessel wall imaging predicts symptomatic intracranial atherosclerotic stenosis

BACKGROUND AND PURPOSE

Intracranial atherosclerotic stenosis (ICAS) is a major cause of ischemic stroke (IS), and high-resolution vessel wall imaging (HR-VWI) can be used to assess the plaque characteristics of ICAS. This study aimed to qualitatively and quantitatively assess plaque enhancement of ICAS and to investigate the relationship between plaque enhancement, plaque morphological features, and IS.

METHODS

Data from adult patients with ICAS from April 2018 to July 2022 were retrospectively collected, and all patients underwent HR-VWI examination. Plaque enhancement was qualitatively and quantitatively assessed, and the plaque-to-pituitary stalk contrast ratio (CR) indicated the degree of plaque enhancement. Plaque characteristics, such as plaque burden and area, were quantitatively measured using HR-VWI. Furthermore, receiver-operating characteristic (ROC) analysis was performed to assess the ability of CR to discriminate plaque enhancement. The patients were divided into a symptomatic ICAS group and an asymptomatic ICAS group according to the clinical and imaging characteristics. Univariate and multivariate analyses were performed to investigate which factors were significantly associated with plaque enhancement and symptomatic ICAS. The plaque burden and CR were compared using linear regression.

RESULTS

A total of 91 patients with ICAS were enrolled in this study. ICAS plaque burden was significantly associated with plaque enhancement (p = .037), and plaque burden was linearly positively correlated with CR (R = 0.357, p = .001). ROC analysis showed that the cutoff value of CR for plaque enhancement was 0.56 (specificity of 81.8%). Both plaque enhancement and plaque burden were significantly associated with symptomatic ICAS, and only plaque enhancement was an independent risk factor after multivariate analysis.

CONCLUSION

Plaque burden was an independent risk factor for plaque enhancement and showed a linear positive correlation with CR. The cutoff value of CR for plaque enhancement was 0.56, and CR ≥ 0.56 was significantly associated with symptomatic ICAS, which was independently associated with plaque enhancement.

High resolution 7T MR imaging in characterizing culprit intracranial atherosclerotic plaques

BACKGROUND

Current imaging modalities underestimate the severity of intracranial atherosclerotic disease (ICAD). High resolution vessel wall imaging (HR-VWI) MRI is a powerful tool in characterizing plaques. We aim to show that HR-VWI MRI is more accurate at detecting and characterizing intracranial plaques compared to digital subtraction angiography (DSA), time-of-flight (TOF) MRA, and computed tomography angiogram (CTA).

METHODS

Patients with symptomatic ICAD prospectively underwent 7T HR-VWI. We calculated: degree of stenosis, plaque burden (PB), and remodeling index (RI). The sensitivity of detecting a culprit plaque for each modality as well as the correlations between different variables were analyzed. Interobserver agreement on the determination of a culprit plaque on every imaging modality was evaluated.

RESULTS

A total of 44 patients underwent HR-VWI. Thirty-four patients had CTA, 18 TOF-MRA, and 18 DSA. The sensitivity of plaque detection was 88% for DSA, 78% for TOF-MRA, and 76% for CTA. There's significant positive correlation between PB and degree of stenosis on HR-VWI MRI (p < 0.001), but not between PB and degree of stenosis in DSA (p = 0.168), TOF-MRA (p = 0.144), and CTA (p = 0.253). RI had a significant negative correlation with degree of stenosis on HR-VWI MRI (p = 0.003), but not on DSA (p = 0.783), TOF-MRA (p = 0.405), or CTA (p = 0.751). The best inter-rater agreement for culprit plaque detection was with HR-VWI (p = 0.001).

CONCLUSIONS

The degree of stenosis measured by intra-luminal techniques does not fully reflect the true extent of ICAD. HR-VWI is a more accurate tool in characterizing atherosclerotic plaques and may be the default imaging modality in clinical practice.