Cerebral Blood Flow Patterns in Patients With Low-Flow Carotid Artery Stenosis, a 4D-PCMRI Assessment

BACKGROUND

Compromised cerebral blood flow can contribute to future ischemic events in patients with symptomatic carotid artery disease. However, there is limited knowledge of the effects on cerebral hemodynamics resulting from a reduced internal carotid artery (ICA) blood flow rate (BFR).

PURPOSE

Investigate how reduced ICA-BFR, relates to BFR in the cerebral arteries.

STUDY TYPE

Prospective.

SUBJECTS

Thirty-eight patients, age 72 ± 6 years (11 female).

FIELD STRENGTH/SEQUENCE

3-Tesla, four-dimensional phase-contrast magnetic resonance imaging (4D-PCMRI).

ASSESSMENT

Patients with ischemic stroke or transient ischemic attack were evaluated regarding the degree of stenosis. 4D-PCMRI was used to measure cerebral BFR in 38 patients with symptomatic carotid stenosis (≥50%). BFR in the cerebral arteries was assessed in two subgroups based on symptomatic ICA-BFR: reduced ICA-flow (<160 mL/minutes) and preserved ICA-flow (≥160 mL/minutes). BFR laterality was defined as a difference in the paired ipsilateral-contralateral arteries.

STATISTICAL TESTS

Patients were grouped based on ICA-BFR (reduced vs. preserved). Statistical tests (independent sample t-test/paired t-test) were used to compare groups and hemispheres. Significance was determined at P < 0.05.

RESULTS

The degree of stenosis was not significantly different, 80% (95% confidence interval [CI] = 73%-87%) in the reduced ICA-flow vs. 72% (CI = 66%-76%) in the preserved ICA-flow; P = 0.09. In the reduced ICA-flow group, a significantly reduced BFR was found in the ipsilateral middle cerebral artery and anterior cerebral artery (A1), while significantly increased in the contralateral A1. Retrograde BFR was found in the posterior communicating artery and ophthalmic artery. Significant BFR laterality was present in all paired arteries in the reduced ICA-flow group, contrasting the preserved ICA-flow group (P = 0.14-0.93).

DATA CONCLUSIONS

4D-PCMRI revealed compromised cerebral BFR due to carotid stenosis, not possible to detect by solely analyzing the degree of stenosis. In patients with reduced ICA-flow, collaterals were not sufficient to maintain symmetrical BFR distribution to the two hemispheres.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 3.

Machine Learning-Based Segmentation of the Thoracic Aorta with Congenital Valve Disease Using MRI

Subjects with bicuspid aortic valves (BAV) are at risk of developing valve dysfunction and need regular clinical imaging surveillance. Management of BAV involves manual and time-consuming segmentation of the aorta for assessing left ventricular function, jet velocity, gradient, shear stress, and valve area with aortic valve stenosis. This paper aims to employ machine learning-based (ML) segmentation as a potential for improved BAV assessment and reducing manual bias. The focus is on quantifying the relationship between valve morphology and vortical structures, and analyzing how valve morphology influences the aorta's susceptibility to shear stress that may lead to valve incompetence. The ML-based segmentation that is employed is trained on whole-body Computed Tomography (CT). Magnetic Resonance Imaging (MRI) is acquired from six subjects, three with tricuspid aortic valves (TAV) and three functionally BAV, with right-left leaflet fusion. These are used for segmentation of the cardiovascular system and delineation of four-dimensional phase-contrast magnetic resonance imaging (4D-PCMRI) for quantification of vortical structures and wall shear stress. The ML-based segmentation model exhibits a high Dice score (0.86) for the heart organ, indicating a robust segmentation. However, the Dice score for the thoracic aorta is comparatively poor (0.72). It is found that wall shear stress is predominantly symmetric in TAVs. BAVs exhibit highly asymmetric wall shear stress, with the region opposite the fused coronary leaflets experiencing elevated tangential wall shear stress. This is due to the higher tangential velocity explained by helical flow, proximally of the sinutubal junction of the ascending aorta. ML-based segmentation not only reduces the runtime of assessing the hemodynamic effectiveness, but also identifies the significance of the tangential wall shear stress in addition to the axial wall shear stress that may lead to the progression of valve incompetence in BAVs, which could guide potential adjustments in surgical interventions.