Clinical relevance of gemstone spectral CT in the diagnosis of carotid atherosclerosis.

A variety of imaging methods can be used in the diagnosis of atherosclerotic plaques. In the present study, we investigated the morphology and composition of atherosclerotic plaque associated with ischemic cerebral infarction by comparing gemstone spectral computed tomography (GSCT) and traditional multi-slice CT (MSCT). In total, 200 patients were enrolled and divided into the experimental group (n=100), which underwent GSCT, and the control group (n=100), which underwent MSCT. All the cases were followed up to observe disease outcomes in patients with different atherosclerotic plaque types, and adverse events in carotid artery stenosis or cerebral infarction were recorded. Compared with traditional MSCT, sensitivity (93.2%), specificity (84.5%), and accuracy (91.0%) of GSCT were significantly higher. We found a correlation between vulnerable plaque of carotid atherosclerotic plaque and the occurrence of cerebral infarction. These results suggest the advantages of GSCT in analyzing atherosclerotic plaque and predicting the risk of ischemic cerebral infarction.

Introduction

Atherosclerosis is a common disease affecting the head and neck vessels that increase the risk of ischemic cerebral infarction. In recent years, the prevalence of atherosclerosis has gradually increased in China. This condition is characterized by degenerative changes and the main pathological change is the deposition of plasma lipids in the artery wall, which leads to the formation of plaques. The most important cause of acute coronary syndrome and sudden cardiac death, and ischemic stroke is the loss of the vulnerable plaque components. Therefore, the analysis of plaque morphology and plaque composition has more clinical value than the degree of luminal stenosis (1–5). A variety of imaging methods can be used in the diagnosis of atherosclerotic plaques, including carotid ultrasound color Doppler scan, carotid computed tomography (CT), carotid DSA and carotid MRA. Each method has advantages and disadvantages, and the choice of method is based on clinical presentation and patient's choice (6).

Gemstone spectral CT (GSCT) is a promising imaging technology (7). Compared with traditional CT, the main advantage of GSCT is the collection of a virtual single image, which makes the imaging clearer after iodine deposition (8). The traditional multi-slice CT (MSCT) results in a CT value by color X-ray that produces uncertainty because the beam has deepened the color rendering effect (9).

GSCT uses X-rays and expresses the absorption of the energy spectrum based on tissue composition and lesions, providing quantitative analysis. Transforming CT from single parameter imaging to multi-parameter imaging enables the quantitative measurement of material properties and composition analysis (10). GSCT can be applied as a new non-invasive arterial angiography. In this study, we compared the performance of GSCT and MSCT in the identification of atherosclerotic plaques. We further focused on the morphological characteristics of carotid artery plaque and distribution by GSCT. We analyzed the correlation between different types of atherosclerotic plaques and ischemic cerebral infarction, explored plaque morphology associated with ischemic cerebral infarction, and determined the formation of vulnerable plaque from morphology.

Materials and methods

Clinical data

We selected 200 patients from the Ninth Hospital of Xi'an (Shaanxi, China) who were enrolled from January 2014 to December 2015 and assigned a random number. One hundred cases were assigned to the experimental group for GSCT for image subtraction and vascular remodeling in patients with carotid plaques. The other 100 cases were enrolled in the control group, which were imaged by MSCT.

This study was approved by the ethics committee of the Ninth Hospital of Xi'an. Signed written informed consent was obtained from all the participants prior to the study

Inclusion criteria for the study were

i) Doppler color ultrasound, clinical symptoms, and laboratory examination leading to carotid plaque diagnosis. ii) Patients and their family members were aware of the purpose and risks of the study, and signed the informed consent form. iii) Patients and relatives actively cooperated in the clinical diagnosis and inspection process.

Exclusion criteria for the study were

i) Patients with other musculoskeletal disorders: Coronary heart disease (CHD), basal ganglia hemorrhage, increased intracranial pressure, heart failure, myocardial infarction, congenital heart disease, valvular disease, and other diseases. ii) Patients with cognitive dysfunction or mental illness. iii) Other reasons leading to failure to achieve satisfactory clinical results. iv) Patients or their relatives did not cooperate. v) Patients or relatives refused to sign the informed consent. vi) Sudden death, migration, loss of access, or patients with acute lesions, resulting in the termination of treatment and diagnosis. vii) Patients unilaterally refusing further diagnosis and treatment. viii) Iohexol or Ultravist solution contrast agent allergy.

GSCT scanning method

Sixty-four slice spiral CT was used to scan the skull base from the upper edge of the aortic arch. Specific detection steps were as follows: i) Before scanning, high pressure syringe was used, intravenous injection of mixed 60 ml iodine contrast agent and 30 ml normal saline was made, and the flow rate was 3–3.5 ml/sec. ii) The root of the common carotid artery was selected as the region of interest. The area of interest was enlarged to 3–5 times. iii) Scanning up to the top with a delay of 5–10 sec was performed, followed by gemstone spectral imaging (GSI) for rapid scanning, observation of the plaque of the various vascular segments, and marking the plaque area clearly.

Qualitative analysis of components in the plaque (11)

After the scan was completed, we used the GSI energy spectrum software (version 2; GE Healthcare, Little Chalfont, Buckinghamshire, UK) to process images for the resulting tomographic images in the later stage. A minimum of ROI was placed in the plaque lesions and the system automatically generated the characteristic spectral curves of the plaques in fat, muscle, and bone. The ROI, which can be automatically obtained by the corresponding three spectrum curves with three representative organizations, were selected. The curve of the atherosclerotic plaque and the fat, muscle, and bone tissue were analyzed, and the HU value of atherosclerotic plaque was determined. Vulnerable plaque comprised lipid plaque and fiber blend, while stable plaque comprised calcified plaque.

Quantitative analysis of the components in the plaque

After the scan was completed, the system automatically entered the MD analysis mode in the area of interest of the plaque and placed a minimum of ROI to measure the specific tissue content of the site. The doctrine of separation of reference calcium grease showed only calcium density in calcium in calcium base map, while the fat base map showed only fat density. The ROI region, and calcium concentration (the ratio of calcium fat relative content, the unit is g/l) were tested, and the corresponding results were analyzed and compared.

Questionnaire design and survey

Literature research: We searched the terms ‘AS’, ‘atherosclerosis’, ‘carotid plaque’, ‘MSCT’, ‘gemstone spectral imaging’, and ‘GSI’ in the NCBI and CNKI databases. We found 4,985 results in NCBI and 2,956 in CNKI. We downloaded, sorted, and analyzed these documents, and used the method of teaching content, including diagnostic means, diagnostic carotid plaque CT diagnostic criteria, vulnerable and stable plaque diagnostic methods, the methods to distinguish them, and the pathological classification of carotid plaque. We took induction and analysis into the format of the questionnaire to make the clinical manifestations and a variety of clinical indicators clearer.

For the references, we successfully downloaded 672 articles, of which 426 were international (63.4%) and 246 Chinese (36.6%).

To generate the questionnaire, we listed the literature and reference books consulted for carotid artery plaque type and clinical diagnostic criteria, and the occurrence of carotid artery plaque and related risk factors. At least three radiologists of intermediate title working independently diagnosed the formation of carotid plaque from the clinical data and questionnaire (Table I).

Table I.

Clinical questionnaire for patients with carotid plaque.

Age	___Years old
Gender	□ Male	□ Female
Marital status	□ Married	□ Unmarried
Nationality	□ Han	□ Others (please specify)
Occupation	(01) Students, (02) teachers, (03) conservation nanny, (04) food (05), business services, (06) medical personnel, (07) workers, (08) migrant workers, (09) farmers, (10) herders, (11) fishermen, (12) cadres, (13) retired, (14) unemployed, (15) other ___________ (please specify), (16) unknown
Degree of education	(1) Illiteracy, (2) primary school, (3) junior high school, (4) high school, (5) University and above, (6) unknown
Important past history	□ No	□ Yes (Detailed description_________________)
Allergic history	□ No	□ Yes (Detailed description_________________)
Smoking history	□ No	□ Yes (Detailed description_________________)
Drinking history	□ No	□ Yes (Detailed description_________________)
Height	|_|_|.|_| cm
Weight	|_|_|_|.|_| kg
BMI	|_|_|_|.|_| kg/m2
Subcutaneous fat thickness	|_|_|.|_| cm
Main clinical manifestations	□ No	□ Yes (Detailed description_________________)
Ultrasonic examination	□ No	□ Yes (Detailed description_________________)
Pathological examination	□ Stable plaque	□ Vulnerable plaque (Detailed description_________________)
Site: 1. Initial part of common carotid artery, 2. Trunk of common carotid artery, 3. Bifurcation of common carotid artery (including the initial part of the internal carotid artery), 4. Trunk of internal carotid artery, 5. Internal carotid siphon, 6. Bilateral vertebral artery (intracranial and extra cranial segment	□ Single	□ Multiple (Detailed description_________________)
Hyperlipidemia	□ Yes	□ No (Detailed description_________________)
Coronary heart disease	□ Yes	□ No (Detailed description_________________)
Radiation exposure history	□ Yes	□ No (Detailed description_________________)
Diabetes	□ Yes	□ No (Detailed description_________________)
Cerebral infarction	□ Yes	□ No (Detailed description_________________)
Transient ischemic attack	□ Yes	□ No (Detailed description_________________)
Chronic cerebral circulatory insufficiency	□ Yes	□ No (Detailed description_________________)
Posterior circulation ischemia	□ Yes	□ No (Detailed description_________________)

Statistical analysis

We used SPSS 20.0 software (IBM, Armonk, NY, USA) for statistical analysis, and the continuous variables were determined as mean ± standard deviation. The median and four-point range were used for the distribution of continuous variables. Categorical variables were determined as absolute percentage. Continuous variables were tested using the Student's t-test or Mann-Whitney U test non-parametric distribution. Comparison of the categorical variables was tested using the Pearson's χ2 test.

Results

Clinical test results of the cohort

We collected clinical data for the 200 patients enrolled in this study with suspected carotid plaque, including age, gender, triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), aspartate aminotransferase (AST), alanine aminotransferase (ALT), glycosylated hemoglobin (HbA1c), γ-glutamyltransferase (GGT), fasting plasma glucose (FPG), systolic blood pressure (SBP), and diastolic blood pressure (DBP). The comprehensive clinical data for the 200 cases are summarized in Table II.

Table II.

Clinical data of patients in the experimental and control groups.

Group	No. of cases (male/female)	Age (years)	SBP (mmHg)	DBP (mmHg)	BMI (kg/m2)	AST (U/l)	ALT (U/l)
Control	132 (57/75)	3.65±0.87	118.5±30.5	70.4±22.8	19.7±2.4	17.2±2.1	12.7±3.6
Experimental	132 (72/60)	3.27±1.24	102.4±23.7	67.4±34.7	23.6±1.8	16.5±2.4	3.2±4.9
T-value	–	0.68	0.17	0.45	9.32	0.78	0.44
P-value	–	0.37	0.21	0.34	0.002	0.27	0.38
Group	GGT (U/l)	FPG (mmol/l)	HbA1C (%)	TG (mmol/l)	HDL-C (mmol/l)	LDL-C (mmol/l)
Control	18.6±2.2	4.37±0.85	4.70±0.43	185.4±20.6	67.5±13.5	98.5±10.2
Experimental	17.3±3.6	8.24±3.36	7.82±1.03	177.3±21.2	68.4±11.8	102.3±21.6
T-value	0.67	9.79	0.56	0.75	0.52	0.63
P-value	0.43	0.002	0.28	0.92	0.48	0.38

SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; AST, aspartate aminotransferase; ALT, alanine aminotransferase; GGT, γ-glutamyltransferase; FPG, fasting plasma glucose; HbA1C, glycosylated hemoglobin; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.

Analysis of clinical history of the cohort

We analyzed the clinical history for the 200 patients, 112 males and 88 females (Tables II and III). We found a history of diabetes in 87 cases, hypertension in 69, CHD in 46, smoking in 45, cerebral infarction in 7, transient cerebral ischemia attack in 15, chronic cerebral circulation insufficiency in 21, posterior circulation ischemia in 6, and hyperlipidemia in 48 patients.

Table III.

Analysis of general clinical history of the patients in the group (%).

Parameters	Experimental	Control	T-value	P-value
No. of cases, n	100	100	–	–
Age (years)	57.3±10.2	61.3±12.4	1.24	0.43
Male, n (%)	48 (0.48)	64 (0.64)	0.28	0.75
Hypertension, n (%)	38 (0.38)	31 (0.31)	0.24	0.81
Diabetes, n (%)	42 (0.42)	45 (0.45)	0.43	0.33
Coronary heart disease, n (%)	20 (0.20)	26 (0.26)	0.83	0.78
Hyperlipidemia, n (%)	27 (0.27)	21 (0.21)	0.28	0.24
Smoking, n (%)	22 (0.22)	23 (0.23)	1.09	0.43
Transient ischemic attack, n (%)	7 (0.07)	8 (0.08)	1.23	0.56
Chronic cerebral circulatory insufficiency, n (%)	12 (0.12)	9 (0.09)	2.19	0.71
Cerebral infarction, n (%)	4 (0.04)	3 (0.03)	2.24	0.83
Posterior circulation ischemia, n (%)	4 (0.04)	2 (0.02)	2.13	0.98
Intima layer thickness (mm)	0.98±1.02	0.89±1.17	0.32	0.24
Three or more carotid plaques, n (%)	8 (0.08)	9 (0.09)	0.04	0.45

Different carotid artery angiography analysis results by GSCT

We analyzed 100 patients by GSCT and acquired image data (Fig. 1A-D). We found that compared with the traditional MSCT, gemstone spectrum CT had a good clinical diagnostic value in judging the composition, type, and vulnerable plaque of carotid plaque, and the identification of vulnerable and stable plaques (Fig. 2) (Table IV).

Figure 1.

Patient examined using GSCT. (A) Cervical arteries and stenosis in supine position. (B) Detection of carotid artery plaque formation. (C) Left common carotid artery on the left side. (D) Right common carotid artery. GSCT, gemstone spectral computed tomography.

Figure 2.

Vulnerable plaques were better detected by spectral CT, whereas more stable and non-atherosclerotic plaques were more frequently detected by MSCT. CT, computed tomography; MSCT, multi-slice computed tomography.

Table IV.

Detection rate of vulnerable and stable plaques in the two groups.

Group	No. of cases	Vulnerable plaque	Stable plaque	Non-atherosclerotic plaque
Control	100	74	10	16
Experimental	100	48	22	30
χ2 value	–		21.7
P-value	–		0.007

Spectrum curves of GSCT in different atherosclerotic plaques

We analyzed the spectrum curve in carotid plaque by spectral CT and found unique spectrum curves for different tissue types (Figs. 3 and 4). This result suggests that gemstone spectrum CT had obvious advantages in identification of the composition of atherosclerotic plaques.

Figure 3.

(A) Arrow indicates the carotid plaque detected and the ROI. (B) The system automatically produced the energy spectrum curve, enabling determination of the characteristics of the carotid atherosclerotic plaque.

Figure 4.

(A) Arrow indicates the carotid plaque detected and the ROI. (B) The system automatically produced the energy spectrum curve, enabling identification of the carotid atherosclerotic plaque as lipid plaque.

Correlation between risk factors and cerebral infarction

We followed the prognosis of the 200 patients and found a correlation between carotid plaque and late cerebral infarction in several types of patients. Furthermore, we performed a multi-factorial analysis between cerebral infarction and multiple risk factors (Table V). There was a strong correlation between cerebral infarction and the type of plaque, blood lipid, blood glucose, and body mass index (BMI). We found no correlation between age and ALT, among other parameters. Carotid atherosclerosis plaques were the most prone to cerebral infarction (r=1.03; P=0.03). Therefore, there is a correlation between carotid atherosclerotic vulnerable plaque and cerebral infarction (Tables V and VI).

Table V.

Correlation analysis of multiple risk factors of cerebral infarction and cerebral infarction (r).

Index	Gender	Age	Stable plaque	Vulnerable plaque	SBP (mmHg)	DBP (mmHg)	AST (U/l)	ALT (U/l)	GGT (U/l)	FPG (mmol/l)	BMI (kg/m2)
Cerebral infarction
r	0.02	0.35	0.17	1.03	0.16	0.28	0.24	0.15	0.06	0.38	0.14
P-value	0.88	0.42	0.03	0.01	0.13	0.24	0.93	0.47	0.01	0.28	0.02

SBP, systolic blood pressure; DBP, diastolic blood pressure; AST, aspartate aminotransferase; ALT, alanine aminotransferase; GGT, γ-glutamyltransferase; FPG, fasting plasma glucose; BMI, body mass index.

Table VI.

Correlation analysis.

						95% CI
Variables	β	SE	β	T-value	P-value	Upper limit	Lower limit
Vulnerable plaque	0.681	0.10	0.642	0.652	0.02	0.39	0.78
Stable plaque	0.372	0.02	0.483	0.328	0.25	0.27	0.58
BMI	0.768	0.08	0.871	0.981	0.32	0.61	0.92

BMI, body mass index; CI, confidence interval.

Diagnostic efficacy evaluation of GSCT and MSCT

Finally, we evaluated the sensitivity, specificity, predictive value, and accuracy of GSCT compared to traditional MSCT (Table VII). GSCT demonstrated higher sensitivity (93.2 vs. 62.5%), specificity (84.5 vs. 61.5%), and overall accuracy (91 vs. 74%) for diagnosing vulnerable plaque of carotid atherosclerosis. The positive and negative predictive values were not significantly different between the two technologies (P<0.05; Fig. 5). Based on data in Table VII, we delineated Receiver Operating Characteristic (ROC) curves of GSCT, MSCT, and gold standard DSA for the diagnosis of carotid plaque (Fig. 5).

Table VII.

Comparison of accuracy, sensitivity, specificity, positive predictive value and negative predictive value (%).

Variables	Sensitivity (%)	Specificity (%)	Positive predictive value (%)	Negative predictive value (%)	Accuracy (%)
GSCT	93.2	84.5	94.5	81.5	91.0
MSCT	62.5	61.5	67.7	84.2	74.0
T-value	9.27	7.88	1.39	1.57	8.17
P-value	0.002	0.017	0.132	0.148	0.011

GSCT, gemstone spectral computed tomography; MSCT, traditional multi-slice computed tomography.

Figure 5.

ROC curves of GSCT, MSCT, and gold standard DSA for the diagnosis of carotid plaque. GSCT, gemstone spectral computed tomography; MSCT, multi-slice computed tomography.

Discussion

Previous studies suggest that plaque morphology and composition are better indicators for stroke than the degree of luminal stenosis (12–14). Thus, imaging techniques capable of providing this information non-invasively have extensive clinical applications. CT is currently one of the most important diagnostic imaging tools available in the clinic (15). CT provides accurate anatomic information, accurate assessment of the organ contour and volume, and can be applied to adjust the therapeutic drug concentrations in different tissues and in the design of treatment plans (16–18). Compared with traditional CT, GSCT produces clearer images after iodine deposition (10). Saito and Murase have suggested that the reduced energy obtained by GSCT can be transformed into relative electron density, and there is a simple linear relationship between the two (16). This method is simple with accurate measurement, but color imaging needs to be carried out in a different camera pressure tube, which potentially limit the application of GSCT. GSCT creatively takes the ‘gem’ as the main material of the CT detector, representing a major technological innovation by further combining MSCT with spectrum CT (18). GSCT combines high spatial resolution and rapid image acquisition with low radiation, has clear benefits for patients and radiologists (10).

In other studies, the electron density and effective atomic number were obtained by dual-energy CT (DECT) (19,20). DECT can image an object with X-rays of different energies, which can accurately determine the proportion of the object. However, a practical dual energy CT that can accurately measure the electron density has yet to be invented (21). There are studies on the accuracy of the resulting RED in GSI mode, and Saito and Murase have proposed a single energy DECT algorithm for kVp conversion (16). This is a simple conversion method that transforms ΔHU into RED. The advantage of this method is that we do not need to know in advance the X-ray spectrum of the CT scan. The error rate of this method is only 1% compared to GSI for measurement of the RED. IPEM81 is recommended in water, and the electron density consistency is 1%, in contrast to its true value, in the lungs and bones, which is 2%. For RED repeated studies, a RED repeatability study is a prerequisite. In 1980, DECT spatial resolution was low and CT value was unstable. In early CT, tube current deficiencies and low tube voltage also limited its application (22). The present study suggests that the standard deviation of the calculated error of the DECT can be kept within 0.5% regardless of the material for scanning. Therefore, the development of DECT has an important role and significance in clinical medicine diagnosis (23,24).

In 2013, a prospective study of 226 patients with intracranial stent implantation examined the occurrence of restenosis and the risk of stroke (25). Follow-up found that in patients with a history of cerebral vascular disease the incidence of intracranial stent restenosis was significantly higher (19). Due to the limitations of the current DSA examination, the vast majority of patients felt discomfort in the follow-up examination, and 0.79% of the patients suffered unpredictable complications (26). According to previous studies, the angiography follow-up rate is relatively low (28–52%) (27), which limits the detection of restenosis or other complications. To the best of our knowledge, only two studies have suggested that patients with MRA after stent implantation require follow-up (27,28). MRA can use quantitative or gadolinium-enhanced MRA to detect carotid artery stent implantation in patients with restenosis (28). Compared with MRA, conventional CTA is more economic and saves time, and is critical for patients with MRI contraindications, such as cardiac pacemakers and claustrophobia (26).

Previous studies proved that GSCT for iodine imaging reduced the artifacts of metal objects by reducing the density of the radiation markers. These changes enable the radiologist to find the lumen of the carotid artery stent more easily (21–23). In this study, we used GSCT following ultrasound screening of suspected carotid artery plaque formation as the DSA had an invasive and radiation quantity, and, some patients in the group in the late period who developed carotid artery stenosis and need stent implantation. Most patients only accepted the CTA and ultrasound examination of the carotid arteries. Therefore, we used carotid ultrasound as a standard to evaluate the diagnosis of GSCT. Compared with conventional CT, gemstone spectral CTA had fewer synthetic image artifacts, the images were clearer, had obvious advantages to examine tube wall thickness and pipe wall, and demonstrated more sensitivity and specificity. Previous diagnosis of aneurysm by GSCT had two obvious advantages. First, traditional CTA showed many artifacts in arteries, thus making the artery wall fuzzy and hard to identify. After the use of iodine imaging, artifacts were significantly reduced. Therefore, GSCT has advantages in the diagnosis of aneurysm, which is very similar to the results of our study. GSCT in the carotid plaque imaging also has many advantages.

Compared with MSCT, GSCT has a good diagnostic value in identifying the composition and type (vulnerable or stable) of carotid artery plaque. Spectral CT had unique curves for different tissues, providing advantages to characterize the atherosclerotic plaque. Generally, clinically calcified atherosclerotic plaques are known as stable plaques, which are more unlikely to rupture and the threat of cerebral infarction is relatively small. Due to the high specificity of GSCT, we showed that it can more accurately differentiate between fatty and calcified plaques. GSCT has other applications in the diagnosis of cerebral vascular atherosclerosis and coronary atherosclerosis.

We believe that the higher sensitivity and accuracy of GSCT for the detection of atherosclerotic plaque is closely related to the detection of tissue composition. General CT uses the CT value to identify the components of atherosclerotic plaque. This method has a certain diagnostic value for simple plaques, but was less useful in the diagnosis of mixed plaques (14). This is due to mutual interference between the fat content of the plaque and the CT value of the fibrous tissue. However, spectrum CT can be a good solution to this problem because of the unique spectrum curves of the plaque components. The rich lipid composition of vulnerable plaque produces a rising curve, whereas the large amount of Ca+ in calcified plaque produces a declining curve. GSCT has also been used to diagnose coronary arteries, cerebral arteries, brain lesions (vertebral atherosclerosis), and lesions of lower extremity (arteriosclerosis obliterans). Identification of vulnerable plaques in vertebral atherosclerosis was significantly higher by GSCT. This helps clinicians evaluate prognosis, effectively reduces the incidence of intracerebral hemorrhage in the basal ganglia, and improves prognosis of the patients.

The gradual development of energy spectrum technology brings the material separation theory into focus. Thus, the attenuation effect of any tissue under X-rays can be described by two types of materials, and the image density of the two materials contains the corresponding material density information (4). Radiologists in energy spectrum CT diagnosis can specifically pair two materials to improve the identification of the constituents of the lesion or plaque, the most commonly used are calcium-fat and calcium-muscle pairing. In this study, we focused on calcium-fat pairing through fault surface and software synthesis analysis. We found that the calcium density map displayed all the calcium contained in the plaque enabling to identify plaque containing calcium and calcium density, whereas the calcium density map and the lipid composition were weaker, avoiding mutual interference (4).

In conclusion, we suggest that GSCT provides significant advantages for the clinical diagnosis of carotid plaques by effectively reducing the metal artifacts and providing a better contrast. GSCT also reduces radiation exposure with benefits for patients and radiologists.