The impact of post-processing images of abdominal CT small vessels using contrast enhancement boost technique: a retrospective study

Introduction

Medical imaging plays a pivotal role in the diagnosis and management of abdominal disorders. Among the various imaging modalities available, computed tomography (CT) has become a cornerstone because of its high spatial resolution and rapid acquisition capabilities (1,2). However, the accurate visualization of small abdominal vessels remains a significant challenge, often necessitating advanced imaging techniques to enhance diagnostic precision. Conventional contrast-enhanced CT, while widely used, has inherent limitations in detecting small vessels due to factors such as restricted spatial resolution and suboptimal contrast agent distribution (3). These challenges can hinder the early diagnosis and management of vascular pathologies, which are critical for optimizing patient outcomes.

Recent advancements in CT post-processing techniques, particularly the contrast enhancement boost (CE-Boost) method, have shown promise in addressing these limitations. This technique employs sophisticated algorithms and specialized filters to selectively enhance the contrast of small vascular structures, improving their visibility and characterization (4-6). By amplifying vascular signals, it has the potential to facilitate more precise assessments. While previous studies have demonstrated its effectiveness in large-vessel imaging and animal models, its clinical utility in routine practice, particularly for small abdominal vessels, remains underexplored. Furthermore, the retrospective nature of some existing studies raises concerns about the generalizability of their findings to broader patient populations.

This retrospective study aims to evaluate the impact of post-processing abdominal CT images using the CE-Boost technique on the visualization of small vessels. By analyzing a substantial dataset of patient CT scans, this study seeks to determine whether this technique significantly improves the detection and characterization of small abdominal vessels compared to conventional imaging methods. Additionally, we assess the potential implications of enhanced imaging for clinical decision-making and patient management, thereby contributing to the optimization of vascular diagnostics in abdominal CT imaging. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-406/rc).

Methods

Patient population

This retrospective study analyzed a cohort of 100 consecutive patients who underwent triple-phase contrast-enhanced abdominal CT scans between July 2, 2024, and November 11, 2024. The study population comprised 49 males and 51 females, with a median age of 56 years (interquartile range: 44 to 67 years). Inclusion criteria were strictly defined as: (I) availability of diagnostic-quality images suitable for accurate radiological assessment, and (II) absence of significant upper abdominal lesions. Patients were excluded based on the following criteria: (I) suboptimal image quality due to technical factors or inadequate patient cooperation during breath-holding, which could compromise accurate image evaluation and quantitative measurements, and (II) incomplete imaging datasets. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of the First Affiliated Hospital of Kunming Medical University [No. (2024) Ethics Review L-180] and informed consent was taken from all the patients.

Scan protocol

All abdominal triple-phase enhanced CT scans were acquired using a 320-detector row volumetric CT scanner (Aquilion One Genesis Edition; Canon Medical Systems, Japan). Patients were positioned supine in a head-first orientation with both arms elevated above the head. The scan range extended from the diaphragm to the inferior renal margins, acquired during a single breath-hold to minimize motion artifacts. Standardized scanning parameters were implemented: tube voltage of 100 kV, automatic tube current modulation (100–700 mA) to maintain consistent image noise (standard deviation =10.0), collimation width of 0.5 mm × 100, gantry rotation time of 0.5 seconds, and a pitch factor of 1.39.

For contrast enhancement, a standardized protocol was followed: after the initial non-contrast scan, 50 mL of iodinated contrast agent (iopromide, 350 mgI/mL) was administered intravenously through the median cubital vein of the right elbow using a dual-syringe power injector at a rate of 4.5 mL/s, followed by a 40 mL saline flush at the same rate. Automated bolus tracking was implemented with a region of interest (ROI) positioned at the descending aorta at the level of the aortic arch, initiating the arterial phase scan when the enhancement threshold reached 150 Hounsfield Units (HU). This protocol ensured consistent and reproducible contrast enhancement across all examinations.

Image reconstruction and post-processing

All acquired images were reconstructed using the Adaptive Iterative Dose Reduction three-dimensional (AIDR 3D) algorithm, generating a standard image dataset (Group A) with a slice thickness of 1 mm and an inter-slice interval of 0.8 mm. These standard images were subsequently processed using SURE Subtraction Iodine Mapping software, which employed advanced flexible subtraction techniques to create optimized CE-Boost images (Group B). Both image datasets were systematically transferred to a dedicated Vital post-processing workstation for comprehensive analysis. Advanced post-processing techniques were uniformly applied to both groups, including volume rendering (VR), maximum intensity projection (MIP), and multiplanar reconstruction (MPR), ensuring consistent and comparable visualization of vascular structures across all datasets.

Subjective image analysis

The quality of the CT images was independently assessed by two radiologists, each with over 5 years of experience in abdominal imaging, who were blinded to the image reconstruction methods and group assignments. All image datasets—including those before and after application of the CE-Boost technique—were anonymized and randomly ordered using a computer-generated sequence prior to evaluation. This randomization ensured that scans from Group A and Group B were not presented sequentially. Radiologists performed a blinded assessment of image quality using a five-point Likert scale (7,8). Subjective image analysis was based on the following criteria: a score of 1 (poor) indicates severely compromised image quality, characterized by substantial motion artifacts, poor vessel delineation insufficient for diagnosis, and unacceptable sharpness. A score of 2 (weak) reflects obvious motion artifacts, poor but diagnostically adequate vessel definition, and marginally acceptable sharpness. A score of 3 (satisfactory) denotes moderate image quality with some artifacts, along with moderate vessel definition and sharpness. A score of 4 (good) corresponds to good image quality, with few artifacts, well-defined vessels, and satisfactory sharpness. The highest score of 5 (excellent) represents excellent image quality, free of artifacts, with clearly defined vessels and outstanding sharpness (9).

Objective image analysis

Placing circular or oval ROI within the main branches of the hepatic artery, left gastric artery, splenic artery, and superior mesenteric artery, CT values and standard deviations (SD) were measured within the ROIs. Each ROI was measured three times and the average value was taken, with attention paid to avoiding vascular lumens and calcifications. Simultaneously, CT values and SD values of the bilateral erector spinae muscles at the same level were measured multiple times. All ROIs were placed and measured independently by two radiologists (Q.L. and Y.L.), each with 5 years of experience in abdominal imaging. Using the vessel SD value as image noise and muscle SD value as background noise, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated as follows (10):

SNR=CTvesselSDvessel[1]

CNR=CTvessel−CTmuscleSDmuscle[2]

Statistical analysis

Data analysis was performed using SPSS 26.0 (IBM Corporation, Armonk, NY, USA). The Shapiro-Wilk test was used to assess the normality of quantitative data. Normally distributed variables were expressed as mean ± standard deviation (mean ± SD) and compared using paired t-tests, while non-normally distributed data were presented as median (first quartile/third quartile) and analyzed using the Wilcoxon signed-rank test. Interobserver agreement was evaluated using Cohen’s Kappa test, with Kappa values interpreted as follows: >0.8, excellent agreement; 0.6–0.8, good agreement; 0.4–0.6, moderate agreement; and <0.4, poor agreement (11). A P value <0.05 was considered statistically significant.

Results

Subjective image analysis

The subjective image quality scores in Group B [5 (4, 5)] were significantly higher than those in Group A [4 (4, 5)], with P<0.001 (Figures 1,2). Interobserver agreement between the two physicians was good, with Kappa values of 0.776 and 0.723, both statistically significant (P<0.001). Detailed results are presented in Table 1. Detailed category-specific scores for image quality, motion artifacts, vascular delineation, and vessel sharpness—along with inter-observer variability analyses for each category—are provided in Table S1.

Figure 1 Comparison of abdominal CT small vessel images before and after CE-Boost post-processing in the same patient. Conventional group (A,C) and CE-Boost (B,D) show the common hepatic artery. (A,C) The artery is blurred with low contrast; (B,D) it’s clearer with enhanced details. Panel (E) (conventional) displays the left gastric artery, while panel (F) (CE-Boost) demonstrates improved vessel clarity, facilitating more detailed morphological analysis. Arrows indicate the common hepatic artery (A-D) and left gastric artery (E,F). CE-Boost, contrast enhancement boost; CT, computed tomography.

Figure 2 Comparison of VR images in the same patient: conventional vs. CE-Boost. Rows show same patient images. (A,C) Conventional VR and (B,D) CE-Boost VR, offering improvements for better anatomical viewing. Arrows highlight key abdominal arterial branches in VR images. They demonstrate CE-Boost’s superiority in 3D anatomical depiction, with enhanced vessel continuity and depth compared to fragmented conventional VR. CE-Boost, contrast enhancement boost; VR, volume-rendered.

Objective image analysis

Objective image quality analysis demonstrated significant improvements in Group B compared to Group A across all evaluated parameters (Tables 2,3). CT attenuation values were significantly higher in Group B at all anatomical locations: common hepatic artery [362.50 (310.67, 421.25) vs. 256.00 (220.50, 296.17), P<0.001], left gastric artery (333.87±77.27 vs. 230.38±53.90, P<0.001), splenic artery [374.50 (323.33, 427.58) vs. 257.17 (225.08, 296.83), P<0.001], and superior mesenteric artery [380.67 (331.33, 444.17) vs. 269.00 (236.00, 311.34), P<0.001]. Noise levels remained comparable between groups for all vessels except the superior mesenteric artery. Both SNR and CNR were significantly enhanced in Group B across all target vessels (P<0.001 for all comparisons). Figure 3 is a visualized chart of the objective image quality assessment results.

Figure 3 Visualization chart of objective evaluation results of image quality. Group A: conventional reconstruction; Group B: CE-Boost processed images. (A) Image noise (standard deviation, HU) measured in adjacent erector spinae musculature. Noise levels were comparable between groups (P=0.152). (B) CT attenuation values (HU) in four arterial branches: CHA, LGA, SA, and SMA. Group B shows significantly higher attenuation (all P<0.001). (C) SNR and (D) CNR for target vessels. Group B demonstrates 39–54% improvement in SNR/CNR (all P<0.001). CE-Boost, contrast enhancement boost; CHA, common hepatic artery; CNR, contrast-to-noise ratio; HU, Hounsfield units; IQR, interquartile range; LGA, left gastric artery; SA, splenic artery; SMA, superior mesenteric artery; SNR, signal-to-noise ratio.

Discussion

The CE-Boost technique significantly improved abdominal computed tomography angiography (CTA) image quality, with Group B demonstrating elevated CT attenuation values (mean increase:109.75 HU, P<0.05) and enhanced SNR/CNR (37.38% and 117.01% increases, respectively, P<0.01) across all vascular tiers compared to conventional processing. These quantitative improvements, consistent with prior neurovascular study (12), likely stem from the algorithm’s ability to amplify intravascular contrast signals while preserving anatomical fidelity—possibly through deep learning-based noise reduction or iterative reconstruction. The enhanced spatial resolution and contrast differentiation improved small vessel (<2 mm) delineation, potentially aiding detection of subtle pathologies (e.g., microaneurysms, early stenoses) and preoperative planning. However, technical limitations including motion artifact susceptibility and lack of longitudinal clinical outcome data warrant further validation in larger cohorts to establish definitive diagnostic impact.

The CE-Boost technology employs a multi-modal approach combining the SUB-Helical acquisition protocol with a 3D anatomy-based deformable registration algorithm (13), specifically designed to compensate for multiphase misalignments caused by cardiorespiratory motion (mean displacement correction: 4.2±1.3 mm in craniocaudal axis). Through AI-powered automatic error correction, this system achieves submillimeter registration accuracy [<0.5 mm root-mean-square (RMS) error] between non-contrast and arterial phase datasets, enabling precise extraction of iodine enhancement maps via digital subtraction. Subsequent fusion of these maps with arterial phase images generates diagnostically optimized CE-Boost reconstructions, a process validated in portal venous (CNR improvement: 132% vs. baseline) and pulmonary arterial imaging without protocol modification (14).

Compared to other techniques aimed at improving CTA image quality, such as dual-energy CT and iterative reconstruction algorithms (15-17), the CE-Boost technique offers distinct advantages. Unlike dual-energy CT, which requires specialized hardware, CE-Boost is a post-processing method that can be applied to existing imaging datasets, making it more accessible and cost-effective. Additionally, unlike standard iterative reconstruction methods that primarily focus on noise reduction, CE-Boost specifically enhances contrast resolution for small vascular structures, addressing a critical need in abdominal imaging (18). Previous research has demonstrated that CE-Boost technology significantly improves CTA image quality for the portal vein and pulmonary artery without requiring modifications to scanning protocols or clinical workflows. In our study, the CE-Boost technique also significantly increased the CNR of the superior mesenteric artery, further confirming its effectiveness in enhancing the visibility of small abdominal vessels.

Enhancing the visualization of small abdominal vessels has significant clinical implications. High-quality imaging is essential for accurate diagnosis, treatment planning, and postoperative follow-up in conditions such as vascular diseases, tumors requiring detailed vascular mapping, and preoperative evaluation for liver transplantation or pancreatic surgery. By improving both objective and subjective image quality, CE-Boost has the potential to enhance diagnostic accuracy and workflow efficiency while reducing the need for repeat imaging, thereby minimizing radiation exposure and contrast agent usage. These advantages underscore the potential of CE-Boost as a valuable tool for optimizing vascular imaging and improving clinical outcomes (19).

Despite its promising results, there are several limitations in this study. First, the retrospective design may introduce selection bias, as patients were not randomized to the imaging groups. Second, the study was conducted at a single institution, which may limit the generalizability of the findings. Third, we focused on a limited number of vessels and further studies could evaluate the impact of CE-Boost on other vascular territories. Finally, while the subjective evaluation scores showed strong inter-observer agreement, a larger pool of radiologists with varying levels of expertise would provide more robust validation of the technique’s clinical utility.

Future research should focus on prospective, multicenter studies to validate the benefits of CE-Boost in a broader patient population and across different imaging platforms. Additionally, exploring the application of CE-Boost in other clinical scenarios, such as oncological imaging or non-abdominal vascular imaging, could further expand its utility. Investigating the potential integration of CE-Boost with artificial intelligence algorithms for automated vascular segmentation and analysis could also enhance its clinical applicability.

Conclusions

In conclusion, the CE-Boost technique significantly improves both objective and subjective image quality metrics for abdominal small vessel CT imaging. By enhancing vessel visualization and providing clearer imaging, CE-Boost has the potential to improve diagnostic accuracy and efficiency in clinical practice. While further studies are needed to confirm these findings and explore additional applications, the results of this study highlight the value of CE-Boost as an effective post-processing tool for advanced CT imaging.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-406/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-406/dss

Funding: This work was financially supported by the Joint Program of Yunnan Provincial Science and Technology Department and Kunming Medical University (No. 202301AY070001-149).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-406/coif). The 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. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of the First Affiliated Hospital of Kunming Medical University [No. (2024) Ethics Review L-180] and informed consent was taken from all the patients.

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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