Enhancing vascular wall assessment in computed tomography: image quality optimization via small-field-of-view vascular wall spectral images

Introduction

The anatomical remodeling of the arterial wall, including dimensional alterations in luminal diameter, intimal hyperplasia, and medial thickness, underlies the pathophysiological processes of various cardiovascular diseases (CVDs) (1). Specifically, acute aortic syndrome pathogenesis arises from medial degeneration and intimal disruption, whereas atherogenesis develops through progressive intimal thickening and plaque formation (2). These subclinical vascular alterations often occur before luminal stenosis or plaque formation, and are asymptomatic. Therefore, early detection is crucial to prevent adverse cardiovascular outcomes.

Non-invasive vascular wall imaging techniques can be used for the vascular wall assessment, including ultrasound (US) (3,4), magnetic resonance imaging (MRI) (5-7), and computed tomography (CT) (8,9). US is primarily used to assess the carotid arteries, but its effectiveness is highly operator-dependent. Transesophageal echocardiography can be used to visualize aortic walls but is invasive (10). Thus, MRI is the preferred method for evaluating vessel walls (11,12).

Recent research has shown that high-resolution black-blood MRI can be used to assess the thoracic aortic wall area (WA) and plaque characteristics, which are independent predictive factors of CVD (13). However, the clinical utility of MRI is limited by contraindications and prolonged acquisition times (14). Conventional CT angiography enables rapid image acquisition and luminal stenosis evaluation, but its ability to visualize the vascular wall is limited by high intraluminal contrast attenuation.

Recent developments in dual-energy CT (DECT) black-blood techniques have shown potential in visualizing the vessel wall (15-18) and predicting vascular involvement in pancreatic cancer (19,20). Our previous study established the capacity of DECT for quantitative vascular wall analysis using vascular wall spectral images and water-calcium decomposition maps, revealing significant correlations between wall thickness (WT)/descending aortic WA (DAWA), and cardiovascular risk factors (21). However, these studies employed the normal scanning field-of-view (FOV; 500 mm × 500 mm), which is inherently limited by partial volume effects and suboptimal spatial resolution. The small-FOV reconstruction technique addresses these limitations through matrix size preservation with reduced geometric sampling (200 mm × 200 mm FOV), achieving improvements in spatial resolution while minimizing partial volume averaging (22).

This study aimed to compare the visualization of the aortic vessel wall in normal-FOV and small-FOV vascular wall spectral images, and to examine the potential of the small-FOV technique in vessel wall spectral imaging.

Methods

Study population

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Guangdong Provincial Hospital of Chinese Medicine (No. ZE2023-422-01), and the requirement for informed consent was waived due to the retrospective nature of this study. A total of 52 patients who underwent spectral CT chest examinations at Guangdong Provincial Hospital of Chinese Medicine from September 2023 to October 2023 were retrospectively included in the study. Patients were excluded from the study if they had a history of aortic diseases, such as aortic dissection, aneurysm, intramural hematoma, or vasculitis. The clinical data of each patient were collected, including gender, age, and atherosclerotic risk factors (smoking, hypertension, diabetes, dyslipidemia, and coronary disease). The enrolled patients were allocated to two groups based on the presence or absence of atherosclerotic risk factors.

CT image acquisition and post-processing

All the patients underwent spectral CT chest enhancement scanning on the GE Revolution Apex CT scanner (GE HealthCare, Chicago, IL, USA). Both the normal-FOV and the small-FOV vessel wall spectral images were reconstructed from the same scan using different parameters. The scanning parameters were as follows: instantaneous tube voltage switching between 80 and 140 kVp, a tube current of 405 mA, a pitch of 0.992:1, and a rotation time of 0.8 s. For reconstruction, a 500 mm × 500 mm display FOV (DFOV) with 50% intensity adaptive statistical iterative reconstruction was used for the normal-FOV group, and a 200 mm × 200 mm DFOV with 70% intensity adaptive statistical iterative reconstruction was used for the small-FOV group. For contrast enhancement, contrast agent tracking trigger technology (Smart Prep, GE HealthCare) was used. CT acquisitions of the arterial phase were initiated with a delay of 6 seconds after the threshold of 150 Hounsfield units (HU) was reached at the tracheal bifurcation level of the descending aorta. The venous phase was performed 20–25 seconds after the arterial phase. The contrast agent (Optiray@370 mgI/mL, Bayer, Leverkusen, Germany) was injected via a high-pressure dual-barrel injector through the antecubital vein at a flow rate of 4 mL/s, with a dosage of 1 mL/kg. This was followed by a flush of 20 mL of saline at the same rate.

The spectral data of the normal-FOV and the small-FOV scans were transferred to Advantage Workstation 4.7 (AW 4.7, GE HealthCare), and virtual non-contrast (VNC) images and 70 keV virtual monochromatic images (VMIs) were obtained. The vascular wall spectral images were obtained by subtracting the 70 keV VMI from the VNC image with a weight p (Figure 1). In accordance with empirical observations, p fell within the range of 0.7 to 0.8 regardless of whether normal-FOV or small-FOV imaging was employed. Vascular wall spectral images were generated using the following formula:

Vascularwallspectralimages=p×VNCimage−(1−p)×VMI70KeV[1]

Figure 1 Study workflow. CNR, contrast-to-noise ratio; CVD, cardiovascular disease; DECT, dual-energy computed tomography; FOV, field-of-view; VMI, virtual monochromatic image; VNC, virtual non-contrast; w, the weight of subtraction; WA, wall area; WT, wall thickness.

Data measurement and analysis

For the quantitative analysis, the vascular wall spectral images from both the normal-FOV and small-FOV groups were simultaneously loaded. All the axial reconstructions were optimized at the diaphragmatic aortic level with fixed display parameters (magnification DFOV 10.6 cm × 7.6 cm), and the wall of the descending aorta was divided into four quadrants visually. Dedicated regions of interest (ROIs) were manually delineated in each aortic quadrant, with paired measurements performed in the lumen and periadventitial adipose tissue at corresponding anatomical levels (Figure 2). The average HU value and standard deviation of the HU value in each ROI were recorded. The contrast-to-noise ratio (CNR) between the wall and the lumen or the surrounding fat of the descending aorta was calculated using the following formula:

CNRwall-lumen/fat=|meanHUwall−meanHUlumen/periaorticfat|12(SDHUwall2+SDHUlumen/periaorticfat2)[2]

Figure 2 Measurement of the CNR in vascular wall spectral images. An axial image of the descending aorta at the diaphragm level was selected for the CNR measurements. Four small ROIs were manually placed at the 12, 3, 6, and 9 o’clock positions of the aortic wall (red ellipses). On the same image slice, the lumen and periaortic ROIs were manually placed at the corresponding region (red cycles). CNR, contrast-to-noise ratio; ROIs, regions of interest.

For the qualitative analysis, two radiologists (Observer 1 and Observer 2, with 13 and 7 years of radiological experience, respectively) independently evaluated the image quality. Based on vessel wall clarity and edge smoothness, a five-point Likert scale was employed for image quality scoring, on which 4 represented excellent visualization of the vascular wall with smooth endoluminal and adventitial boundaries, fully satisfying the diagnostic criteria, 3 represented good visualization of the vascular wall with slightly rough endoluminal and adventitial boundaries, meeting the diagnostic criteria, 2 represented fair visualization of the vascular wall with segmental roughness of endoluminal and adventitial boundaries, slightly affecting the diagnosis, 1 represented poor visualization of the vascular wall with endoluminal and adventitial boundaries barely visible, affecting the diagnosis, and 0 represented no clear visualization of the vascular wall with invisible endoluminal and adventitial boundaries, making assessment impossible.

In terms of the WT and DAWA measurements, both observers independently performed analyses on axial reconstructions aligned with the CNR measurement planes in the normal-FOV and small-FOV vascular wall spectral images. WT was measured perpendicular to the lumen centerline at four standardized positions (12, 3, 6, and 9 o’clock anatomical orientations), with the mean value recorded as the final WT. Lumen diameters were quantified in anteroposterior and left-right dimensions (Figure 3). The DAWA was calculated using the following formula:

DAWA=π×(WT+Lumendiameter2)2−π×(Lumendiameter2)2[3]

Figure 3 Measurement methods for wall thickness and lumen diameter. (A) Wall thickness measurement at the 12, 3, 6, and 9 o’clock positions; (B) lumen diameter measurement. Red lines indicate vessel wall and lumen measurement.

Statistical analysis

All the statistical analyses were performed using SPSS (version 22, IBM Corporation, Armonk, New York, USA). The normally distributed continuous variables are expressed as the mean ± standard deviation, and analysis of variance tests or t-tests were performed to compare the CNR, WT, and DAWA values between the two reconstruction groups. The non-normally distributed continuous variables, including the subjective image scores, WT, and DAWA, are presented as the median (first quartile, third quartile) [M (Q1, Q3)]. The Mann-Whitney U test was used to compare: (I) the two reconstruction groups (small-FOV vs. normal-FOV), and (II) the patient groups with and without atherosclerotic risk factors (WT and DAWA only). Intraclass correlation coefficients (ICCs) and Cohen’s kappa (κ) values were used to assess inter-reader agreement. The κ values were interpreted as follows: poor agreement: κ<0.4; moderate agreement: κ=0.41–0.60; good agreement: κ=0.61–0.80; and excellent agreement: κ=0.81–1.00. A P value <0.05 was considered statistically significant.

Results

Study population

Ultimately, 52 consecutively enrolled patients (28 males, 24 females) were included in the final analysis. The patients had a mean age of 58±15 years and an average body mass index of 22.30±2.86 kg/m². Of the 32 patients with atherosclerotic risk factors, 9 had dyslipidemia, 13 had hypertension, 7 had diabetes, 8 had coronary disease and 12 had a history of smoking (Table 1).

Image quality evaluation

The quantitative analysis revealed that the contrast resolution was significantly enhanced in the small-FOV imaging compared to the normal-FOV imaging (Figure 4A and Table 2). Specifically, the wall-lumen CNR increased by 94% (9.45±3.28 vs. 4.86±2.16, P<0.001), and the wall-fat CNR increased by 45% (5.63±2.89 vs. 3.88±2.09, P<0.001). The qualitative analysis also indicated that the small-FOV images were superior to the normal-FOV images (Figures 4B,5, and Table 2), with Observer 1 scoring 4.00 (4.00, 4.00) and 4.00 (3.00, 4.00), and Observer 2 scoring 4.00 (4.00, 4.00) and 4.00 (3.00, 4.00) (both P<0.005) for the small-FOV and normal-FOV images, respectively. Inter-observer agreement improved from good (κ=0.799) in the normal-FOV protocol to excellent (κ=0.898) in the small-FOV protocol.

Figure 4 Comparisons of imaging quality metrics between normal-FOV and small-FOV spectral CT vessel wall imaging. (A) CNRs between the vascular wall and the lumen or fat in normal-FOV and small-FOV vascular wall spectral images. (B) Stacked bar chart of qualitative scores for the normal-FOV and the small-FOV vascular wall spectral images (left: Observer 1, right: Observer 2), 0-4 indicate image quality score. A higher score indicates better image quality. A score of 4 denotes excellent image quality, while 0 means the image is unassessable. CNR, contrast-to-noise ratio; CT, computed tomography; FOV, field-of-view.

Figure 5 Normal-FOV and small-FOV vascular wall spectral images and corresponding pseudo-color image. (A) A normal-FOV vascular wall spectral image. (B) A pseudo-color image of (A). (C) A small-FOV vascular wall spectral image. (D) A pseudo-color image of (C). FOV, field-of-view.

WT and DAWA measurement results

The WT values of the descending aorta were 2.11±0.28 and 2.14±0.30 mm (Observer 1), and 2.15±0.30 and 2.13±0.28 mm (Observer 2) for the normal-FOV and small-FOV vascular wall spectral images, respectively (Table 3). The DAWA values were 148.57±37.45 and 148.04±35.57 mm² (Observer 1), and 149.53±36.49 and 147.98±33.44 mm² (Observer 2) for the normal-FOV and the small-FOV groups, respectively (Table 3). There were no significant statistical differences between the normal-FOV and small-FOV groups across all the measurements. The inter-observer reliability analysis showed excellent agreement for both parameters, with ICCs of 0.93 and 0.97 for the WT measurements, and 0.97 and 0.98 for DAWA measurements in the normal-FOV and the small-FOV groups, respectively. Figure 6A-6D shows the results of the Bland-Altman analysis for WT and DAWA. The Bland-Altman plots demonstrated good inter-observer agreement for both the small-FOV and normal-FOV imaging. Notably, the small-FOV group exhibited a mean bias closer to zero, along with tighter 95% limits of agreement for both the WT and DAWA measurements, confirming its superior inter-observer reproducibility.

Figure 6 Bland-Altman analysis results. (A,B) The WT for the normal-FOV and small-FOV groups, respectively. (C,D) The DAWA for the normal-FOV and small-FOV groups, respectively. DAWA, descending aortic wall area; FOV, field-of-view; SD, standard deviation; WT, wall thickness.

In the normal-FOV group, the median WT and DAWA values for the patients with and without atherosclerotic risk factors were 2.10 and 1.90 mm, 151.03 and 125.61 mm² (Observer 1), and 2.15 and 2.02 mm, 159.31 and 127.45 mm² (Observer 2). In the small-FOV group, the median WT and DAWA values for the patients with and without atherosclerotic risk factors were 2.15 and 2.03 mm, 158.64 and 132.42 mm² (Observer 1), and 2.15 and 2.03 mm, 155.23 and 132.42 mm² (Observer 2). With the exception of the WT measured in the normal-FOV group, all the other measurements differed significantly between the patients with and without atherosclerotic risk factors (all P<0.05) (Table 4 and Figure 7).

Figure 7 Vessel wall images of a patient with atherosclerotic risk factors. (A) Spectral CT vessel wall image. (B) Corresponding pseudo-color image. CT, computed tomography.

Discussion

This comparative study systematically evaluated the objective and subjective image quality between normal-FOV and small-FOV vascular wall spectral images. It also measured the WT and DAWA, and performed an inter-observer consistency analysis. The results revealed that the small-FOV vascular wall spectral images showed higher CNRs, better subjective image quality scores, and greater inter-observer agreement in the WT and DAWA measurements compared to the normal-FOV images. These advantages collectively support its clinical utility for precise vascular wall characterization in spectral CT imaging.

Vascular wall spectral images based on contrast-enhanced DECT were generated by subtracting the 70 keV VMIs from VNC images with appropriate weighting factors. This algorithmic approach effectively suppressed intraluminal contrast, creating a “dark lumen, bright wall” visualization. Under this protocol, the hypodense vascular lumen is clearly delineated against the hyperattenuating vessel wall. Critically, this was accomplished without protocol modifications or additional radiation exposure (21). The small-FOV technique, which enhances spatial resolution, demonstrated potential for improving both the repeatability and accuracy of the vascular wall measurements. The quantitative analysis revealed a significantly higher CNR in the small-FOV group compared to the normal-FOV group (P<0.001). This improvement may be attributed to the enhanced spatial resolution of small-FOV acquisitions, which increased the pixel density in the ROI. The resulting reduction in the partial volume averaging effect led to the lower standard deviation of measurements. Further, the same technical refinement mitigated the jagged appearance of inner wall boundaries commonly observed in subtraction images, without the application of any smoothing post-processing. Consequently, the small-FOV group achieved superior subjective evaluation scores (P<0.001).

To further demonstrate the advantages of the small-FOV technique in vascular wall imaging, this study measured the WT and DAWA, and assessed inter-observer agreement. A comparative analysis of the WT measurements revealed no significant differences between the groups. However, inter-observer agreement was improved in the small-FOV group (ICC =0.97 vs. 0.93). Further analysis of the association between the atherosclerotic risk factors and WT revealed that in both the normal-FOV and small-FOV groups, the patients with atherosclerotic risk factors had significantly higher WT values than those without atherosclerotic risk factors. In the normal-FOV, the WT median measurements of the patients with and without atherosclerotic risk factors were 2.10 and 1.90 mm (Observer 1), and 2.15 and 2.02 mm (Observer 2) (P>0.05), and in the small-FOV the WT median measurements were 2.15 and 2.03 mm (Observer 1), and 2.15 and 2.03 mm (Observer 2) (P<0.05).

A previous study that assessed the thoracic aorta WT in 196 CVD-free patients using fast spin-echo double inversion recovery MRI reported an average WT for the descending aorta of 2.11±0.06 mm for females and 2.32±0.06 mm for males (23). Malayeri et al. reported a mean WT of 2.35±0.5 mm in the thoracic descending aorta of 1,053 CVD-free participants using MRI (24). The T1-three-dimensional volume isotropic turbo spin-echo (TSE) sequence (T1-mVISTA) has also been used to compare an abdominal large vessel vasculitis group and a conventional control group. The results revealed that the abdominal aortic WT of the control group was 2.24±0.45 mm (24,25). Our study results were slightly smaller than these findings. In the present study, the DAWA values of the normal-FOV group were 148.57±37.45 and 149.53±36.49 mm² (Observers 1 and 2), while those of the small-FOV group were 148.04±35.57 and 147.98±33.44 mm² (Observers 1 and 2), and the measurements did not differ significantly between the groups. The inter-observer ICCs of both the normal-FOV and the small-FOV group were high at 0.97 and 0.98, respectively. The patients with atherosclerotic risk factors had significantly higher DAWA values than those without atherosclerotic risk factors (P<0.05). In the normal-FOV images, the DAWA median measurements of patients with and without atherosclerotic risk factors were 151.03 and 125.61 mm² (Observer 1), and 159.31 and 127.45 mm² (Observer 2), and in the small-FOV images, the DAWA median measurements were 158.64 and 132.42 mm² (Observer 1), and 155.23 and 132.42 mm² (Observer 2). The current findings corroborate existing evidence, demonstrating that established atherosclerotic risk factors (smoking, hypertension, diabetes, and dyslipidemia) contribute significantly to changes in WT and DAWA (24,25).

This study showed that vascular wall spectral CT imaging is applicable to vascular wall imaging, offering rapid acquisition without protocol modification or increased radiation exposure. Due to its technical advantages, including its enhanced spatial resolution and measurement reproducibility, small-FOV imaging is a promising non-invasive option for vascular assessment. In clinical practice, the small-FOV technique in vascular wall imaging enables the precise quantitative measurement of vascular WT and the luminal area. These capabilities make it a better choice for the early risk stratification of atherosclerosis in asymptomatic high-risk populations, enabling the early detection and treatment of small vulnerable plaques. Additionally, the technology allows for accurate detection of aortic diseases (e.g., intramural hematoma). Further, by dynamically tracking changes in these quantitative indicators, it offers reliable support for the precise adjustment of treatment plans and the objective evaluation of therapeutic efficacy. It should be noted that the small-FOV technique in vascular wall imaging places higher technical demands on the operator, requiring targeted reconstruction during scanning. Additionally, this technique must be performed in dual-energy scanning mode, as conventional CT is unable to accomplish such vascular wall imaging.

This study had several limitations. First, the absence of direct MRI-based vascular wall imaging as a reference standard limits the ability to validate WT and DAWA measurements against an established gold standard. However, the close agreement between our CT-derived measurements and published MRI reference values supports the biological plausibility and technical consistency of our findings. Second, the generalizability of our findings is limited by the single-center design, modest sample size (n=52), and reliance on specific spectral CT equipment. Future research should refine multi-center study designs that incorporate diverse imaging platforms. Further, subsequent studies need to be conducted to correlate and compare measurements from both techniques with clinical outcomes, thereby strengthening the clinical evidence supporting our findings.

Conclusions

Compared to normal-FOV imaging, small-FOV vascular wall spectral CT imaging enhances both the image quality of the vascular wall and inter-observer consistency. This approach is a promising non-invasive technique for vascular wall imaging.

Acknowledgments

We appreciate the CT scanning technology support provided by GE Corporation.

Footnote

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1378/dss

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1378/coif). M.G. is employed by GE HealthCare. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Guangdong Provincial Hospital of Chinese Medicine (No. ZE2023-422-01). Informed consent was waived in this retrospective study.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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