Comparative analysis of blood flow velocity in middle cerebral artery stenosis by transcranial doppler ultrasonography and contrast-enhanced high-resolution magnetic resonance imaging

Abstract

Background

This study aimed to investigate the optimal cutoff value of transcranial Doppler (TCD) ultrasonography for detecting middle cerebral artery (MCA) stenosis and differences in MCA blood flow velocities between plaques with different characteristics using contrast-enhanced high-resolution magnetic resonance imaging (CE-HR-MRI).

Methods

A total of 122 patients with MCA stenosis detected using TCD underwent CE-HR-MRI. Peak systolic velocity (PSV), mean flow velocity (MFV), and end-diastolic velocity (EDV) of the stenotic and distal segments were recorded. The stenotic/distal MFV ratio (SDR) was then calculated. Plaque characteristics were analyzed using CE-HR-MRI to compare differences in blood flow velocity between the plaques with different characteristics.

Results

The optimal cutoff values for detecting mild, moderate, and severe stenosis were PSV = 140 cm/s, EDV = 60 cm/s, and MFV = 90 cm/s; PSV = 200 cm/s, EDV = 90 cm/s, MFV = 120 cm/s, and SDR = 1.7; and PSV = 270 cm/s, EDV = 150 cm/s, MFV = 180 cm/s, and SDR = 3.0, respectively. The agreement between TCD and CE-HR-MRI was highest when using PSV (weighted kappa = 0.839). PSV, MFV, EDV, and SDR were significantly elevated in concentric plaques compared with those in eccentric plaques. Plaque enhancement grade 2 was significantly higher in PSV, MFV, and EDV than plaque enhancement grades 0 and 1. However, after stratification by stenosis severity, these differences were no longer significant.

Conclusions

TCD, particularly PSV, provides reliable grading of MCA stenosis when referenced against CE-HR-MRI. Although higher flow velocities are observed in concentric plaques and those with grade 2 enhancement, these differences are not independent of stenosis severity. Nevertheless, the established velocity cutoffs for stenosis grading may facilitate non-invasive risk stratification in patients with MCA stenosis.

Introduction

Stroke is the third leading cause of mortality worldwide, accounting for 7.3 million deaths (10.7% of all deaths) in 2021, and the fourth leading contributor to global disability-adjusted life-years (DALYs), responsible for 160.5 million DALYs (5.6% of all DALYs) [1]. In China, the burden of stroke is particularly severe. According to the Global Burden of Disease Study 2019, there were 3.94 million new stroke cases, 28.76 million prevalent cases, and 2.19 million stroke-related deaths in China that year. Stroke was also the leading cause of disability-adjusted life-years (DALYs) in China, with the number of DALYs reaching 45.9 million [2]. Given this substantial disease burden, effective preventive interventions are crucial. The Asymptomatic Carotid Atherosclerosis Study (ACAS) demonstrated that timely revascularization in high-risk patients with asymptomatic carotid artery stenosis reduced the 5-year cumulative stroke risk from approximately 11% to 5.1% [3].

For patients with intracranial arterial stenosis, routine imaging evaluation methods include transcranial Doppler ultrasonography (TCD), magnetic resonance angiography (MRA), computed tomography angiography (CTA), and digital subtraction angiography (DSA). Contrast-enhanced high-resolution magnetic resonance imaging (CE-HR-MRI) is an emerging vascular imaging technology that utilizes high-resolution three-dimensional imaging to reconstruct any blood vessel in multiple planes, observe intracranial artery wall structures, clarify plaque distribution relative to the lumen, determine plaque characteristics, and ascertain the remodeling index of blood vessels, among other uses [4, 5]. Plaque formation and arterial stenosis can disrupt hemodynamics, increasing the risk of stroke. Nonetheless, the correlation between plaque attributes and cerebral blood flow velocities remains underexplored. TCD has emerged as the favored non-invasive, portable, and cost-effective tool for testing intracranial arterial hemodynamics [6]. It has good-to-excellent accuracy in identifying stenoses and occlusions, particularly in the middle cerebral artery (MCA) [6, 7], and is suitable for screening high-risk individuals among a large Chinese population.

This study employed CE-HR-MRI as a reference to investigate variations in blood flow velocity associated with different plaque characteristics. Additionally, this study aimed to determine the optimal cutoff values for TCD and grade MCA stenosis to enhance the reliability of TCD diagnosis for MCA stenosis when screening cerebrovascular disease.

Materials and methods

Ethical approval

This retrospective study was approved by the Research Ethics Committee of the Second Hospital of Hebei Medical University, and the requirement for informed patient consent was waived.

Study design and patients

A retrospective review was conducted for all patients who received TCD in the Department of Neurology at the Second Hospital of Hebei Medical University from October 2019 to December 2022 (N = 10,121). Among these, 623 patients underwent CE-HR-MRI within 2 weeks after TCD. From these 623 patients, those with MCA M1 stenosis confirmed on CE-HR-MRI were selected. The exclusion criteria included: severe carotid stenosis or occlusion (n = 317); vertebrobasilar stenosis or occlusion (n = 181); Moyamoya disease (n = 1); and temporal acoustic window failure (n = 2) (Fig. 1). The presence of these conditions was determined by a comprehensive review of available cerebrovascular imaging modalities, including but not limited to TCD, carotid/vertebral ultrasound, CTA, or MRA. A total of 122 patients (78 men and 44 women) with middle cerebral artery (MCA) stenosis were enrolled, with a mean age of 46.8 ± 13.1 years (range, 23–74). Baseline data, including sex, age, hypertension, diabetes, hyperlipidemia, stroke, coronary heart disease, and history of smoking and alcohol consumption, were recorded. Hemodynamic analysis was performed on a per-artery basis. Of the 244 MCAs assessed (bilateral arteries from each patient), 19 occluded MCA were excluded, resulting in 225 arteries being included in the final analysis.

Fig. 1.

Flow chart of excluded and included patients

TCD

TCD was performed using 1.6-MHz pulsed wave Doppler probes, and the transcranial spectral signals were analyzed using a 8-channel Doppler unit (Delica EMS-9UA; Shenzhen, China). TCD examinations were performed by an experienced neurologist (J.H., experience of more than 3,000 cases). The TCD setup involved a single channel with eight depths, with a sweep speed of 4–8 s per screen. The range of the gate was 8–15 mm, gain was adjusted until the spectral waveform was clearly displayed, and the scale and baseline were adjusted until the spectral waveform was completely displayed. The participants were placed in the supine position, and the probe was placed in the temporal window, in the area corresponding to the line between the arch of the eyebrow and upper edge of the ear, vertically or slightly tilted toward the anterior and superior. The detection depth of the MCA M1 segment was 40–65 mm, and the blood flow direction was toward the probe. Depth was adjusted to the midpoint of the M1 segment to approximately 50 mm, and the angle and direction of the probe were gently adjusted to show a clear spectral waveform and display the highest flow velocity. The depth was gradually reduced to approximately 40 mm to continuously observe the blood flow signal in the distal section of M1 segment. Subsequently, the depth was gradually increased along the M1 segment to 60–65 mm to probe the beginning of the MCA. The peak systolic velocity (PSV), mean flow velocity (MFV), and end-diastolic velocity (EDV) were recorded at the highest velocity of the M1 segment and the distal M1 segment, and the stenotic/distal MFV ratio (SDR) was calculated [8].

HR-MRI

A 3.0-T scanner (Philips Healthcare, Best, Netherlands) with an 8-channel phased-array head coil was used for HR-MRI. All patients underwent a series of imaging sequences, including three-dimensional time-of-flight (3D-TOF) MRA, high-resolution T1-weighted imaging (HR-T1WI) perpendicular to the long axis of stenotic vessels, and HR-T1WI enhancement scanning sequences based on 3D-TOF MRA maximal intensity projection images. The CE-HR-MRI scanning parameters for MCA were as follows: (1) 3D-TOF MRA: repetition time (TR)/echo time (TE) = 15/3.5 ms, field of view (FOV) = 320 × 191 mm², slice thickness = 0.6 mm, number of excitations (NEX) = 1, matrix size = 384 × 301, sequence duration = 1.38 min; (2) T1WI: TR/TE = 233/4.6 ms, FOV = 230 × 183 mm², NEX = 1, matrix = 400 × 255, slice thickness = 5.5 mm, sequence duration = 1.97 min; (3) HR-T1WI VISTA: TR/TE = 800/20 ms, FOV = 200 × 181 mm², NEX = 1, matrix size = 332 × 300, slice thickness = 0.3 mm, and sequence duration = 3.33 min; and (4) contrast enhancement T1WI: contrast agent gadopentetate glucosamine at a dose of 0. l mmol/kg was injected, and an enhancement scan was performed 5.1 min after the injection. The scanning parameters were the same as those used for T1WI.

Stenosis degree was calculated using the following formula: stenosis ratio = (1- narrow lumen diameter/reference lumen diameter) × 100% [9]. The MCA was classified as normal (0–14%), mild stenosis (15–50%), moderate stenosis (50–69%), severe stenosis (70–99%), and occlusion [9, 10].

The plaque distribution was categorized as concentric or eccentric. An eccentric plaque was diagnosed if the thinnest part of the wall was < 50% of the thickest part on the T1WI slice. Concentric plaque was diagnosed if the thinnest part of the wall was ≥ 50% of the thickest point [11]. Plaque enhancement was classified into three grades: grade 0, enhancement similar to or smaller than the intracranial artery walls without plaque; grade 1, enhancement greater than grade 0 but smaller than the pituitary infundibulum; and grade 2, enhancement similar to or greater than the infundibulum [12]. All datasets were analyzed using commercially available viewing software (Syngo, Plaza, Siemens Healthcare, Erlangen, Germany) through consensus reading by an experienced neurologist and neuroradiologist.

Statistical analyses

Statistical analyses were performed using SPSS 24.0 (IBM Corporation, Armonk, NY, USA). Continuous variables with normal distribution are presented as mean±standard deviation, and non-normally distributed variables are presented as median (interquartile range). Continuous variables were compared between the two groups using the independent samples t-test (for normally distributed data) or Mann–Whitney U-test (for non-normally distributed data). The optimal diagnostic cutoff values of the TCD flow parameters for diagnosing different degrees of MCA stenosis were assessed using the area under the receiver operating characteristic (ROC) curve with CE-HR-MRI as the reference standard. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and Youden index versus CE-HR-MRI were calculated. Weighted kappa statistics were used to assess the degree of agreement between TCD and CE-HR-MRI results. Scatter plots were generated using GraphPad Prism (version 10.0, GraphPad Software, San Diego, CA, USA).

Results

According to TCD and CE-HR-MRI results, 122 patients (244 arteries in total, 19 occluded MCA arteries excluded) were enrolled. Based on CE-HR-MRI assessment, among 225 arteries, 117 (52.0%), 31 (13.8%), 37 (16.4%), and 40 (17.8%) exhibited normal MCA, and mild, moderate, and severe stenosis, respectively. The distribution of flow velocities across different stenosis severity groups is shown in Fig. 2.

Fig. 2.

Cumulative distribution function (CDF) plots for peak systolic velocity (PSV), end-diastolic velocity (EDV), mean flow velocity (MFV) and stenotic/distal MFV ratio(SDR)across different stenosis severity groups. In the PSV panel, a vertical line is drawn at 140 cm/s

ROC curve analysis

The diagnostic performance of various TCD parameters for different degrees of MCA stenosis was assessed using ROC curve analysis. For the detection of mild stenosis (15–50%), PSV exhibited the largest area under the curve (AUC) at 0.918 (95% CI: 0.862–0.974, P < 0.001), followed by MFV (AUC: 0.906) and EDV (AUC: 0.896), indicating that PSV provides the highest diagnostic accuracy for identifying early-stage lesions. In the evaluation of moderate stenosis (50–69%), SDR demonstrated the largest AUC at 0.875 (95% CI: 0.792–0.959, P < 0.001), which was slightly higher than that of PSV (AUC: 0.855) and MFV (AUC: 0.852), suggesting that SDR offers optimal diagnostic value within this degree of stenosis. For severe stenosis (70–99%), PSV maintained the highest diagnostic efficacy (AUC: 0.820, 95% CI: 0.719–0.921, P < 0.001). Notably, SDR yielded the smallest AUC in the severe stenosis group at just 0.698 (95% CI: 0.579–0.816), indicating its relatively limited accuracy in detecting extremely severe lesions. All parameters demonstrated statistically significant diagnostic performance (P < 0.05).

Optimal cutoff values for detecting MCA stenosis

To determine the optimal cutoff values for each parameter in diagnosing different degrees of MCA stenosis, we analyzed the diagnostic performance indicators at various thresholds and selected the optimal cutoff value for each parameter based on the maximum Youden index (Table 1).

Table 1.

Optimal cutoff values for MCA stenosis

Stenosis Degree	Parameter	Optimal Cut-off	Sensitivity (%)	Specificity (%)	PPV (%)	NPV (%)	Accuracy (%)	Youden Index
Mild	PSV	≥ 140 cm/s	90.3	87.2	65.1	97.1	87.8	77.5
	EDV	≥ 60 cm/s	87.1	86.3	62.8	96.2	86.5	73.4
	MFV	≥ 90 cm/s	83.9	87.2	63.4	95.3	86.5	71.1
Moderate	PSV	≥ 200 cm/s	91.9	74.2	81.0	88.5	83.8	66.1
	EDV	≥ 90 cm/s	86.5	67.7	76.2	80.8	77.9	54.2
	MFV	≥ 120 cm/s	91.9	67.7	77.3	87.5	80.9	59.6
	SDR	≥ 1.7	91.9	74.2	81.0	88.5	83.8	66.1
Severe	PSV	≥ 270 cm/s	87.5	73.0	77.8	84.4	80.5	60.5
	EDV	≥ 150 cm/s	70.0	75.7	75.7	70.0	72.7	45.7
	MFV	≥ 180 cm/s	85.0	67.6	73.9	80.6	76.6	52.6
	SDR	≥ 3.0	82.5	54.1	66.0	74.1	68.8	36.6

MCA Middle cerebral artery, PSV Peak systolic velocity, EDV End-diastolic velocity, MFV Mean flow velocity, SDR0 Stenotic/distal MFV ratio, PPV Positive predictive value, NPV Negative predictive value

For mild stenosis, the optimal cutoff value for PSV was ≥ 140 cm/s, with a sensitivity of 90.3%, specificity of 87.2%, and accuracy of 87.8%. The optimal cutoff values for EDV and MFV were ≥ 60 cm/s and ≥ 90 cm/s, respectively, with accuracies of 86.5% and 86.5%.

For moderate stenosis, the optimal cutoff value for PSV was ≥ 200 cm/s, with a sensitivity of 91.9%, specificity of 74.2%, and accuracy of 83.8%; the optimal cutoff value for SDR was ≥ 1.7, and its diagnostic performance was comparable to that of PSV (accuracy 83.8%). The optimal cutoff values for EDV and MFV were ≥ 90 cm/s and ≥ 120 cm/s, respectively.

For severe stenosis, the optimal cutoff value for PSV was ≥ 270 cm/s, with a sensitivity of 87.5%, specificity of 73.0%, and accuracy of 80.5%; MFV was optimal at ≥ 180 cm/s, with an accuracy of 76.6%. In contrast, the diagnostic accuracies of EDV and SDR were relatively low, with optimal cutoff values of ≥ 150 cm/s and ≥ 3.0, and accuracies of 72.7% and 68.8%, respectively.

Agreement between the TCD and CE-HR-MRI results

For 225 MCA arteries (normal, mild, moderate, and severe stenosis) using PSV, MFV, and EDV as the diagnostic criteria, the weighted kappa coefficients for the agreement between the TCD and CE-HR-MRI results were 0.839 (95% confidence interval [CI], 0.795–0.882; P < 0.001), 0.809 (95% CI, 0.760–0.858; P < 0.001), and 0.784 (95% CI, 0.735–0.833; P < 0.001), respectively. In the case of mild, moderate, and severe stenosis using SDR as the diagnostic criterion, the weighted kappa coefficient for the agreement between the TCD and CE-HR-MRI results was 0.595 (95% CI, 0.474–0.717; P < 0.001) (Table 2).

Table 2.

Agreement between the TCD and CE-HR-MRI results

	Kappa	Asymptotic standard error	Z	P	Lower 95% asymptotic CI bound	Upper 95% asymptotic Cl bound
CE-HR-MRI & PSV	0.839	0.022	16.058	< 0.001	0.795	0.882
CE-HR-MRI & MFV	0.809	0.025	15.390	< 0.001	0.760	0.858
CE-HR-MRI & EDV	0.784	0.025	15.147	< 0.001	0.735	0.833
CE-HR-MRI & SDR	0.595	0.062	7.842	< 0.001	0.474	0.717

CI Confidence interval, HR-MRI High-resolution magnetic resonance imaging, PSV Peak systolic velocity, MFV Mean flow velocity, EDV End-diastolic velocity, SDR Stenotic/distal MFV ratio, TCD Transcranial Doppler ultrasonography

Differences in blood flow velocity in plaques with different characteristics

Baseline characteristics, including sex, age, hypertension, diabetes, hyperlipidemia, history of stroke, coronary heart disease, smoking, and alcohol consumption, showed no significant differences between the concentric and eccentric plaque groups (all P > 0.05), nor between the enhancement grade 0–1 and grade 2 groups (all P > 0.05).

Eccentric plaques and concentric plaques showed differences in PSV, MFV, EDV, and SDR (P < 0.001, P < 0.001, P < 0.001, and P = 0.007, respectively). However, when stratified by stenosis severity (mild, moderate, and severe), these differences between eccentric and concentric plaques became non-significant (all P > 0.05) (Table 3; Fig. 3).

Table 3.

Morphology of plaques in patients with stenosis in the middle cerebral artery

Plaque morphology	N (%)	PSV	EDV	MFV	SDR
Eccentric	74	228.0 (180.0, 279.5)	109.0 (84.0, 142.0)	150.0 (115.2, 185.6)	2.6 (1.5, 5.0)
Concentric	34	294.0 (258.3, 363.5)	170.5 (133.5, 219.0)	210.9 (182.7, 267.7)	3.9 (2.7, 9.5)
Z		-4.118	-4.439	-4.366	-2.712
P		< 0.001	< 0.001	< 0.001	0.007

EDV End-diastolic velocity, MFV Mean flow velocity, PSV Peak systolic velocity, SDR Stenotic/distal mean flow value ratio

Fig. 3.

Scatter distribution of PSV, EDV, and MFV in plaques with different morphological types and enhancement grades. Abbreviations: PSV, peak systolic velocity; EDV, end-diastolic velocity; MFV, mean flow velocity; C, concentric; E, eccentric; G0&1, enhancement grades 0 and 1; G2, grade 2. Y-axis: velocity (cm/s); -, median

Furthermore, there were significant differences in PSV, MFV, and EDV between plaques with enhancement grades 0, 1, and 2 (P < 0.001, P = 0.004, and P < 0.001, respectively). Nevertheless, when graded by stenosis, the difference between the two groups was not significant (all P > 0.05) (Table 4; Fig. 4).

Table 4.

Plaque enhancement in patients with stenosis in the middle cerebral artery

Plaque enhancement	N (%)	PSV	EDV	MFV	SDR
Grades 0 & 1	30	223.9 ± 53.1	101.0 (79.5, 129.8)	148.0 ± 41.7	2.5 (1.9, 3.9)
Grade 2	78	272.3 ± 86.2	138.5 (93.8, 200.5)	190.4 ± 72.1	3.5 (1.7, 7.4)
t’/Z		-3.520	-2.867	-3.794	-1.797
P		0.001	0.004	< 0.001	0.072

EDV End-diastolic velocity, MFV Mean flow velocity, PSV Peak systolic velocity, SDR Stenotic/distal mean flow value ratio

Fig. 4.

Scatter distribution of SDR in plaques with different morphological types and enhancement grades. Abbreviations: SDR, stenotic/distal MFV ratio; C, concentric; E, eccentric; G0&1, enhancement grades 0 and 1; G2, grade 2. Y-axis: stenotic/distal MFV ratio; -, median

Discussion

In this study, CE-HR-MRI was used as a reference to evaluate the optimal blood flow velocity parameters of MCA stenosis detected using TCD, and differences in blood flow velocity parameters were found between plaques with different characteristics.

Numerous laboratories have established their own diagnostic criteria, and the optimal cutoff values exhibit variability among these studies. It is advisable to formulate criteria that align with the specific context of the laboratory [8, 13–16]. The variation in criteria for interpreting the degree of MCA stenosis leads to differences in test results, consequently influencing the selection of appropriate clinical treatment options, including both endovascular interventions and pharmacological therapies. This study showed that PSV, MFV, EDV, and SDR can be used as criteria for grading MCA stenosis. Additionally, PSV was superior to MFV, EDV, and SDR in terms of sensitivity, specificity, PPV, NPV, accuracy, Youden index, and weighted kappa coefficient. The North American Symptomatic Carotid Endarterectomy Trial, which was conducted across nearly 50 centers with over 1,100 patients, compared nonstandard PSV values and the percentage reduction in the internal carotid artery diameter as determined by catheter angiography. This comparison revealed that PSV measurements consistently adhered to the hypothetical Spencer curve and closely aligned with the underlying hemodynamic model [17–19]. The Stroke Outcomes and Neuroimaging of Intracranial Atherosclerosis trial, using DSA as the reference, investigated the TCD cutoff values of MFV ≥ 100 cm/s for ≥ 50% MCA stenosis [16]. Notably, this study did not account for PSV. Several other studies have utilized PSV ≥ 140 as the criterion for MCA stenosis ≥ 50%, with high accuracy in detecting lumen diameter reductions exceeding 50% [20–23]. This finding is consistent with our study results. The optimal cutoff velocities in our study were generally consistent with those reported by Chen et al. [13].

Although some studies suggested that culprit plaques exhibited a higher degree of enhancement, compared with non-culprit plaques [24], our investigation revealed that plaque enhancement grade 2 was associated with significantly higher blood flow velocity than those of grades 0 and 1 (P < 0.01). However, it should be noted that these differences were no longer significant after stratification by stenosis severity, suggesting that the observed association may be largely driven by the degree of luminal narrowing rather than representing an independent effect of plaque enhancement characteristics. Intense contrast enhancement is believed to be associated with neovascularization and increased endothelial permeability, facilitating the delivery and accumulation of contrast agents within the plaque [12, 25, 26]. Elevated blood flow velocity alters the mechanical environment within the vasculature, increasing the pressure gradient in the vascular network, thereby modifying local arterial wall tension and shear stress. This alteration affects endothelial cell function and intercellular tight junctions, contributing to the up-regulation of the expression of endothelial cell proliferating factors and, subsequently, to the proliferation and migration of endothelial cells [27]. This study recorded extremely high blood flow velocities (e.g., a peak systolic velocity as high as 423 cm/s). Applying the modified Bernoulli equation (ΔP = 4v²), this corresponds to a significant trans-stenotic pressure gradient exceeding 70 mmHg during systole. Such pronounced periodic pressure fluctuations may alter the biomechanical environment of the plaque, potentially promoting intraplaque neovascularization originating from the vasa vasorum. Increased blood flow velocity leads to vascular proliferation, contributing to plaque enhancement; this vascular proliferation, in turn, elevates the risk of plaque rupture [28]. Previous studies have reported that contrast enhancement in intracranial atherosclerotic plaques is associated with recent ischemic events and may serve as an indicator of plaque stability, providing valuable insights into stroke [12].

Our study showed that concentric stenoses were associated with higher flow velocities compared to eccentric stenoses. This finding appears to contradict idealized hemodynamic models which, for a fixed stenosis degree, predict higher velocities in eccentric configurations [29]. A principal explanation for this discrepancy lies in the dynamic pathological progression of atherosclerotic plaque. An atheroma typically originates focally on one vessel wall, presenting initially as an eccentric stenosis. As the plaque progresses, it expands circumferentially while the unaffected vessel wall undergoes compensatory outward remodeling. This evolutionary process can result in a more concentric stenosis morphology, which is often concomitant with a greater overall degree of luminal narrowing [30, 31]. Therefore, the concentric plaques in our study likely represent a more advanced disease stage with more severe stenosis, which hemodynamically accounts for the observed higher flow velocities. This progression from eccentric to concentric may also contextualize the spectrum of plaque characteristics observed across different age groups. For instance, concentric enhancement patterns in some younger patients may be linked to inflammatory etiologies such as arteritis [32, 33], whereas in older populations with advanced atherosclerosis, concentric morphology may signify late-stage disease. In contrast, eccentric plaques are often associated with lumen preservation and milder hemodynamic effects [34]. In our cohort, they were observed predominantly in cases with mild to moderate stenosis, consistent with an early or intermediate pathological stage.

This study has several limitations. First, as a single-center retrospective study with a limited sample size, selection bias may exist. Most patients with TCD findings did not undergo CE-HR-MRI due to mild symptoms, contraindications to MRI, or logistical reasons. Therefore, our findings may primarily reflect the characteristics of patients with symptomatic or moderate-to-severe MCA stenosis. Second, blood flow velocity and plaque characteristics were not directly associated with cerebrovascular events; thus, the relationship between these factors requires further investigation. Third, our per-artery analysis did not account for the potential within-patient correlation between bilateral MCA measurements, which may lead to a slight underestimation of standard errors. Large-scale, prospective, multicenter studies are warranted to validate our findings.

Conclusions

Our study underscores the reliability of TCD as an effective imaging method for evaluating MCA stenosis, particularly in PSV. Additionally, our findings demonstrate that plaques with enhancement grade 2 have higher blood flow velocities than those with enhancement grades 0 and 1, and concentric plaques have higher flow velocities than eccentric plaques, although these differences were not independent of stenosis severity. This observation offers preliminary, hypothesis-generating insights into the potential relationship between plaque characteristics and flow velocity. This study identifies optimal TCD velocity cutoffs associated with plaque characteristics on HR-MRI, providing an imaging–hemodynamic reference for future investigations into risk stratification in patients with MCA stenosis.

Authors’ contributions

Conceptualization, X.S. and J.H.; formal analysis, X.S. and J.H.; data curation, F.L. and L.Q.; writing—original draft, X.S.; writing—review & editing, X.S.; supervision, G.Y.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This retrospective study was approved by the Research Ethics Committee of the Second Hospital of Hebei Medical University. In accordance with the hospital's guidelines for retrospective research and the national regulations on ethical review of biomedical research involving human subjects, the requirement for written informed consent was waived due to the retrospective design and the use of anonymized patient data. The study was conducted in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.