Optimized contrast enhancement and homogeneity in aortic CT angiography: bolus tracking with personalized post-trigger delay

Introduction

The prevalence of aortic disease increases annually, with patients in critical condition experiencing high rates of death and disability (1). Computed tomography angiography (CTA) of the aorta is the preferred modality for diagnosing aortic diseases due to its high diagnostic sensitivity and specificity (2,3).

The clinical manifestations of aortic diseases exhibit considerable heterogeneity, with acute aortic conditions demonstrating a pronounced early mortality risk, yet some aortic diseases may be less symptomatic or asymptomatic (4). Therefore, accurate evaluation of the aorta assumes paramount importance in guiding therapeutic interventions and prognostic outcomes for patients with aortic diseases, underscoring the need for high-quality CTA images.

Attaining high-quality CTA images requires prominent arterial enhancement. In CTA imaging, contrast enhancement is influenced by several factors (5-7), including contrast injection rate, injection duration, concentration of injected iodine, technical parameters such as tube potential, and patient factors, especially cardiac output and peripheral resistance. Discrepancies in patient time–concentration curves under the same injection and acquisition protocol persist due to variations in the timing of peak aortic enhancement influenced by factors such as left ventricular ejection fraction, coronary artery disease, cardiac cycle duration, and other variables (6,8). Therefore, precise timing of image acquisition during the arterial phase is a pivotal requirement.

Bolus tracking, commonly employed for determining scan timing, offers several advantages including reduced contrast agent dosage, decreased radiation exposure, and expedited workflow efficiency (9). Monitoring computed tomography (CT) attenuation across a predetermined area until it exceeds the trigger threshold, followed by image acquisition after a fixed post-trigger delay (PTD). Although it represents an improvement over the previous fixed timing without monitoring, there is still significant potential for enhancement in personalized applications. This is due to the time difference between reaching the threshold and reaching the peak arterial enhancement among patients. Particularly in patients with coexisting cardiovascular disorders and aneurysms, the slow blood flow speed in the aorta may cause premature initiation of image acquisition due to a fixed delay, leading to completion before the peak of arterial enhancement.

In the process of exploring the personalization of delay time, previous retrospective studies have attempted to administer PTD based on specific formulas for different indications (10-12). Some researchers have used the double region of interest (ROI) timing bolus or femoral artery test injection method to calculate the scan timing (13,14), which could introduce variation from different operators or complicate the workflow. Accordingly, in addition to achieving personalization, the PTD should also be automatic because the patient’s cardiac output information is generally not available prior to image acquisition (15).

To tackle these challenges, we used an innovative bolus tracking algorithm that employs a convolution-based algorithm to automatically derive a personalized PTD from the contrast injection and monitoring measurements information. The aim of this study was to demonstrate the feasibility of achieving optimized enhancement and contrast homogeneity with personalized PTD in aortic CTA by comparing image quality between the fixed PTD group and the personalized PTD group. We present this article in accordance with the TREND reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-24-624/rc).

Methods

Ethics approval

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This prospective study was approved by the Scientific Research and Clinical Trial Ethics Committee of the First Affiliated Hospital of Zhengzhou University (No. 2021-KY-0976), and written informed consent was provided by all the patients.

Study population

This prospective, single-center study, conducted from May 2023 to February 2024, included 148 participants aged 18 years or older who required aortic CTA in our department. Participants with the following characteristics were excluded: prior allergic reactions to iodinated contrast media (n=1), chronic kidney disease (estimated glomerular filtration rate less than 30 mL/min/1.73 m²; n=2), pregnancy (n=1), occlusion of the infrarenal aorta (aortoiliac occlusive disease; n=1), less than 4 CT numbers during the monitoring scan (n=2), and bolus tracking failure (n=1). Finally, 140 consecutive study participants underwent the aortic CTA (Figure 1).

Figure 1 Study flowchart. CM, contrast media; eGFR, estimated glomerular filtration rate; CTA, computed tomography angiography; CT, computed tomography.

The first 70 participants were assigned to the control group [Group A, 13 females, mean ± standard deviation (SD) age 58±11 years] using a fixed PTD, whereas the remaining 70 participants were assigned to the experimental group (Group B, 18 females, mean ± SD age 59±12 years) with a personalized PTD. Table 1 outlines patient details and indications in both groups.

CT scanning and contrast media injection protocol

Scans were performed using the third-generation 192-section dual-source CT scanner (SOMATOM Force; Siemens Healthineers, Erlangen, Germany) in the single-source mode. Image acquisition parameters remained the same in the two groups: tube voltage of 90 kVp; pitch of 1.2; collimation of 192 mm × 0.6 mm; gantry rotation time of 500 ms; and automated attenuation-based tube current (CareDose4D; Siemens Healthineers). All images were reconstructed with a slice thickness of 1.0 mm and an increment of 1.0 mm, utilizing a universally adapted field of view (FOV) size. The reconstruction was performed using advanced modeled iterative reconstruction (ADMIRE) with a strength level of 3 and a medium-smooth soft tissue kernel (Bv40).

The contrast media injection protocol was consistent in both groups: 18 mL of 0.9% saline solution was injected through the anterior cubital vein at a flow rate of 5 mL/s to ensure smooth contrast flow. Subsequently, 60 mL of preheated non-ionic iodinated contrast media (Iohexol, 350 mgI/mL) and 50 mL of saline were sequentially injected at a flow rate of 3 mL/s, because total iodine of 21 g and an injection flow rate of 3 mL/s produces excellent angiography at 90 kVp (16).

Fixed and personalized PTD in bolus tracking

The bolus tracking technique was used to initiate the scan. A circular ROI was positioned at the level of the left atrium on the descending aorta to monitor contrast enhancement. The attenuation of the trigger threshold was 100 Hounsfield units (HU) at 100 kVp. In the control group, image acquisition began with a fixed 6-second delay after reaching the threshold. In the experimental group, personalized PTD was determined by the new bolus tracking algorithm (Custom Pack, Somaris 7.VA50 SP2, version 2; Siemens Healthineers). This algorithm predicts the local contrast over time C(t) by extrapolating measured contrast enhancement values. For prediction, contrast dynamics are modeled by convoluting the applied contrast media injection protocol IP(t) (in gI/s) and the patient-specific arterial impulse response function AIF(t) (in HU). IP depends on the iodine concentration, injection rate and injection duration, whereas arterial input function (AIF) is derived by performing least-squares online fitting using a minimum of 4 CT values obtained during the monitoring scans to match the population-averaged parameterized arterial circulation curve in the enhancement curve database. The mid-scan time was placed at the peak of enhancement. The time between the last bolus tracking enhancement and the start of scan serves as the ideal PTD (17,18) (Figure 2).

C(t)=∫−∞∞IP(t)⋅AIF(t−t)dt=IP(t)⊗AIF(t)[1]

Figure 2 Demonstration of fixed PTD (A) and personalized PTD (B) of bolus tracking for CTA. Monitoring scanning initiates 10 seconds after the start of contrast injection; after reaching the monitoring threshold, start PTD. Personalized PTD considering the scan parameters and patient-specific arterial impulse response to predict local contrast enhancement over time and place the mid-scan time at the peak enhancement (B). HU, Hounsfield units; PTD, post-trigger delay; CTA, computed tomography angiography.

Image analysis

Objective image quality

An experienced cardiovascular radiologist with 11 years of expertise (P.L.) evaluated the objective image quality using the workstation (Syngo.via, VB40; Siemens Healthineers). The radiologist, blinded to patient allocation, scan protocol, and contrast media protocol, manually positioned circular ROIs on the axis-corrected axial reformations based on automatically defined vessel centerlines. ROIs (19) were placed at various anatomical levels to measure the CT attenuation of the vessel (HU_vessel). These levels included the pulmonary artery, ascending and descending aorta at the level of the pulmonary trunk, abdominal aorta, right common iliac artery, and the branches of the abdominal aorta (the celiac trunk, superior and inferior mesenteric arteries, left and right renal arteries). Additionally, the attenuation of the psoas muscle at the lower pole of the right kidney was measured as the background signal (HU_muscle), and the SD of the attenuation was defined as the image noise (N).

ROIs were maximized based on the internal diameter of the vessels, excluding the vessel wall, aortic stents, calcified plaque, thrombus, and streak artifacts (20). ROIs in the psoas muscle were carefully drawn to avoid fatty streaks and vessels within the muscle. Contrast-to-noise ratio (CNR) was calculated as follows:

CNR=(HUvessel−HUmuscle)/N[2]

Enhancement level and contrast homogeneity

Contrast enhancement level was defined as low (at least 1 CT attenuation lower than 350 HU in the descending aorta, abdominal aorta, and right common iliac artery), optimal (all three aortic CT attenuations between 350 and 600 HU), and high (at least one CT attenuation greater than 600 HU in the above three ROIs) (16,21).

Contrast enhancement homogeneity is classified as poor when the difference in CT attenuation between the ascending aorta and the right common iliac artery is >80 HU, and optimal when the difference is ≤80 HU (17,21).

Evaluation of the abdominal aortic branches: to further evaluate the new algorithm’s potential in patients with untreated aortic dissection or aneurysm, the enhancement level of the abdominal aortic branches was assessed (22). In Group A, there were five patients with untreated aortic dissection, 12 patients with untreated aneurysms, and seven patients with residual disease after endovascular repair, for a total of 120 branch vessels measured. In Group B, there were four patients with untreated aortic dissection, 11 patients with untreated aneurysms, and seven patients with residual disease after endovascular repair, with a total of 110 branch vessels measured.

Subjective image quality

The subjective image quality for each patient was evaluated by two cardiovascular CT imaging observers (P.L. and J.L.), with 11 and 23 years of experience, respectively. They were blinded to the clinical indication, experimental allocation, and imaging results. The maximum intensity projection (MIP) images of multiplanar reformation (MPR) and curved planar reformation (CPR) were evaluated. The study utilized a 5-point Likert scale to assess subjective image quality scores (17,23): 5, excellent (aorta and iliac arteries with excellent enhancement and homogeneous contrast media distribution, clear delineation of the vessel walls and no artifact); 4, good; 3, moderate; 2, poor; 1, non-diagnostic (aorta and iliac arteries with unacceptable enhancement, and no proximal or distal contrast enhancement caused significant inhomogeneity, poor delineation of vessel walls, and prominent artifact). Data from five patients outside of this study were randomly selected for practice testing based on the above criteria.

Radiation dose estimates

The volumetric CT dose index (CTDI_vol) and the dose length product (DLP) were recorded. The effective dose (ED) was estimated by multiplying the DLP with a conversion factor of 0.0145 mSv/mGy·cm, which is the average of the thoracic (0.014 mSv/mGy·cm) and abdominal (0.015 mSv/mGy·cm) coefficients (16).

ED=DLP×0.0145[3]

Statistical analysis

The normality of continuous data was tested using the Shapiro-Wilk test. Quantitative data that were normally distributed were presented as mean ± SD, whereas data with non-normal distributions were presented as medians and interquartile ranges (IQRs). Categorical variables were presented as percentages and frequencies. The Student’s t-test or Mann-Whitney U test was used to determine quantitative data, whereas the Chi-square test was used to compare categorical variables between the two groups. For image quality assessment, the Student’s t-test was employed to analyze the mean attenuation, the Mann-Whitney U test was used to assess CNR, and the Chi-square test was utilized to evaluate enhancement level, contrast homogeneity, and subjective scores in both groups. The agreement level between the two assessments of subjective image quality was evaluated using Cohen’s kappa.

All analyses were performed with the software SPSS 26.0 (IBM Corp., Armonk, NY, USA). A P value <0.05 indicated a statistically significant difference.

Results

Study participants

Characteristics and radiological parameters of the study participants in both groups are shown in Table 1. No significant differences were observed between the two groups in terms of age (P=0.645), sex (P=0.309), height (P=0.241), weight (P=0.383), body mass index (BMI; P=0.679), CTDI_vol (P=0.698), DLP (P=0.680), or ED (P=0.680).

Determination of PTD using bolus tracking

In Group A, the PTD was fixed at 6 seconds, whereas for Group B, the algorithm automatically calculated personalized PTD (mean ± SD, 11.2±1.5 seconds) ranging from 7.8 to 14.1 seconds.

Objective image quality

The mean attenuation and CNR of the aortoiliac artery were higher in Group B compared with Group A (417.55±71.55 vs. 345.71±60.41 HU, 16 vs. 13, both P<0.001, respectively), and the largest difference between groups was observed in the common iliac artery [110.97 HU; 95% confidence interval (CI): 87.60–134.34 HU] (Table 2). The mean attenuation of the artery was significantly higher in group B than in group A, regardless of the branch and location of the measurements. Also, Group B demonstrated improved CNRs in most arteries (Figure 3).

Figure 3 Box-plot comparison of CT attenuation and CNR for PA, AAO, DAO, AA, right CIA, CA, SMA, LRA, RRA, and IMA between the two groups. Group A: the control group using the fixed PTD; Group B: the experimental group using the personalized PTD. HU, Hounsfield units; PA, pulmonary artery; AAO, ascending aorta; DAO, descending aorta; AA, abdominal aorta; CIA, common iliac artery; CA, celiac trunk; SMA, superior mesenteric artery; LRA, left renal artery; RRA, right renal artery; IMA, inferior mesenteric artery; CNR, contrast-to-noise ratio; CT, computed tomography; PTD, post-trigger delay.

Enhancement levels and contrast homogeneity

Group B exhibited a significantly higher percentage of optimal enhancement levels and contrast homogeneity compared to Group A (78.6% vs. 37.1%, 94.3% vs. 64.3%, both P<0.001, respectively; see Table 3).

With personalized PTD, 75.7% (53/70) of participants showed optimal enhancement levels and contrast homogeneity, whereas using fixed delay, only 20.0% (14/70) achieved both. Specifically, 17.1% (12/70) in Group A had optimal enhancement levels but poor contrast homogeneity, whereas 44.3% (31/70) had optimal contrast homogeneity but low enhancement levels (Tables S1,S2).

Evaluation of abdominal aortic branches: a total of 230 vessel branches were evaluated (Table 3). Group B exhibited a significantly higher percentage of holding an attenuation of over 350 HU in branch vessels of the abdominal aorta compared to Group A (74.5% vs. 23.3%, P<0.001).

Subjective image quality

Figure 4 displays the scores of subjective image quality in the two groups. Both observers concurred that Group B had superior overall image quality (both P<0.001), which they both rated excellent or good in 50 of 70 (71.4%) and 8 of 70 participants (11.4%) (Table S3). Good coherence was found between the two observers, with subjective quality scores reaching a Cohen’s kappa of 0.868 (95 % CI: 0.806–0.930). Hence, the rating of observer 1 was used for the final outcomes (20). The percentage of scores greater than or equal to 4 was 91.4% for Group B and 68.6% for Group A. Image examples of the two groups are shown in Figure 5.

Figure 4 Subjective image quality evaluation from two observers. Group A: the control group using the fixed PTD; Group B: the experimental group using the personalized PTD. The numbers “1–5” mean the scores of subjective image quality. PTD, post-trigger delay.

Figure 5 CPR and cinematic rendering CTA images. (A,B) A 76-year-old male patient with untreated abdominal aorta aneurysm using a fixed PTD of 6 seconds. Contrast redundancy in the right atrium reflects that the early scanning timing, leading to scattering artifacts and reducing the image quality. In addition, redundancy of the contrast media above resulted in insufficient contrast opacification in the abdominal aortic aneurysm, and the iliac arteries were not visualized, thereby affecting the diagnosis. The significant decrease of the attenuation from the aorta to the iliac arteries and the nondiagnostic enhancement of the abdominal aorta resulted in an adjudication of score 1 by both observers. Attenuation in AAO, DAO, AA, and CIA were 468, 391, 320, and 190 HU. (C,D) A 77-year-old male patient with untreated abdominal aorta aneurysm using a personalized PTD of 12 s. Stable and high level of enhancement from the aorta to the iliac arteries resulted in an adjudication of score 5 by both observers. Attenuation in AAO, DAO, AA, and CIA were 400, 434, 455, and 427 HU. CPR, curved planar reformation; CTA, computed tomography angiography; PTD, post-trigger delay; AAO, ascending aorta; DAO, descending aorta; AA, abdominal aorta; CIA, right common iliac artery; HU, Hounsfield units.

Discussion

In the present study, we compared aortic CTA images obtained using a personalized PTD with those using a fixed delay. Our results showed that the personalized PTD can achieve significantly higher vessel attenuation and improved contrast homogeneity under the same injection and acquisition protocol, compared with the fixed delay.

The development of CT technology has led to a gradual reduction in scanning time, driving the trend towards using less contrast media. With the reduction in the amount of contrast media, the precise determination of scanning timing becomes more critical for achieving sufficient contrast opacification. For the precise determination of scan timing, three key factors need to be considered: (I) injection duration; (II) contrast media arrival time; and (III) scan duration (5). Under the same contrast media injection protocol and other scan parameters, attention should be paid to the contrast media arrival time, which is closely related to the individual patient’s cycle time and is influenced by individual factors such as cardiac output and cardiovascular circulation. In our study, the new bolus tracking algorithm was used to achieve this by precisely synchronizing the scanning duration with the contrast administration. The personalized PTD ranged from 7.8 to 14.1 seconds under the same injection and collection protocol, indicating that the varied time-concentration curves were caused by patient-specific cardiovascular parameters such as blood circulation time and cardiac output; therefore, using the same fixed PTD for all patients was not ideal.

Previous studies have proposed the anticipation of contrast enhancement before diagnostic CTA scans to provide patient-specific scan timing for each patient. For instance, the optical test bolus technique by Eisa et al. (24) provided adequate estimates of the bolus arrival time, but was limited in specialized optical sensors, dye contrast media, and lacked clinical validation. In contrast, this new bolus tracking technique with personalized PTD can be seamlessly integrated into scanning protocols to enhance the intelligence of the CTA imaging process. It adapts real-time patient data to an online database of the population-averaged parameterized arterial circulation curve, generates the patient-specific arterial impulse response, incorporates contrast media injection information, uses the convolution algorithm to build a predictive model, and automatically provides a personalized PTD, minimizing the impact of individual differences. According to Bae et al. (5), a higher enhancement can be achieved with faster multidetector CT for a given iodine dose due to improved contrast medium utilization from optimized scan timing. Several studies (15,17,25,26) investigated the feasibility of improving image quality with the personalized PTD bolus tracking technology in contrast-enhanced CT of abdominal parenchymal organs and CTA. Our study applied the same technique to CTA of the aorta, and validated the benefits of providing homogeneous and enhanced images.

Related to the precise scan timing, homogeneous and high-contrast enhancement are also essential for ensuring proper diagnosis in long z-axis coverage of CTA scans, such as aortic CTA. In addition to determining aneurysm size, angulation, and tortuosity, as well as the extension of aortic dissection and involvement of branch arteries, the presence of distal artery stenoses also needs to be assessed in patients with bifurcation endovascular stents or symptoms such as lower limb pain and absent femoral pulse. Discrepancies in enhancement level may affect the ability to address all clinical questions, leading to artificial defects or perceived narrowing (27). Previously, researchers have typically prolonged the state plateau phase, such as using an exponentially decelerated or multibolus contrast injection protocol (22,28), rather than precisely capturing the scan timing. The personalized PTD optimizes the scan timing by allowing sufficient delay before image acquisition and ensuring that the CT scan is focused on the plateau phase (a broad peak) of the time-enhancement curve. For instance, the turbulent blood flow in the aneurysm changes the hemodynamic situation and reduces distal blood flow and the bolus transit time is expected to increase (29). Thus, the use of a personalized PTD seems reasonable in these patients to provide sufficient contrast enhancement of the aneurysms and distal aortic branches, as a fixed PTD may lead to suboptimal visualization (30). The results of this study show that, using a personalized PTD, Group B showed significantly higher CT values and contrast homogeneity compared to Group A. Furthermore, patients with aortic dissection or aneurysm exhibited enhanced enhancement in abdominal aortic branches. This information was valuable for diagnosing and adequately opacifying the lesion, which is important for predicting late aortic events (31-33).

Additionally, reducing the contrast media volume has become an increasingly important concern for avoiding additional risks, such as post-contrast acute kidney injury. The implementation of personalized PTD in Group B maximized the use of contrast media while minimizing redundancy or waste, resulting in higher CT attenuation than the fixed PTD under the same injection protocol. Given that the peak enhancement of the aorta is directly proportional to the contrast media volume, we believe that utilizing the personalized PTD algorithm has the potential to further reduce the volume of contrast media while maintaining image quality, and further work will be conducted in the future.

Conclusions

Some limitations existed in this study. First of all, the sample size was limited. Second, the tube voltage and contrast protocol were fixed and not adjusted for body weight. However, these steps were taken to minimize other potential confounding factors affecting vessel attenuation and contrast homogeneity, to evaluate the effect of PTD more clearly. Finally, this study focused on the feasibility of using the personalized PTD algorithm to optimize image quality in the broad area of the thoracic and abdominal aorta rather than determining the optimal PTD required for a specific disease type. Further research is needed on whether there is a relationship between different aneurysm sizes or extension of aortic dissection and delay time, and whether different scan timing parameters are required for different diseases, such as hepatic hemangioma. In conclusion, the personalized PTD in bolus tracking can provide accurate scan timing, produce consistent, homogeneous vascular enhancement, and achieve enhanced image quality in aortic CTA scans. Comparison with the conventional fixed PTD highlights the superiority of the personalized PTD.

Acknowledgments

Funding: This work was supported by the Science and Technology Research Project of Henan Provincial Health Commission (No. 212102310142).

Footnote

Reporting Checklist: The authors have completed the TREND reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-624/rc

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-624/coif). L.L. is employed by Siemens Healthineers and did not have control over the data at any point during the study. 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 (as revised in 2013). The study was approved by Scientific Research and Clinical Trial Ethics Committee of the First Affiliated Hospital of Zhengzhou University (No. 2021-KY-0976) and informed consent was provided by all participants.

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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