Black-blood computed tomography with contrast-enhancement boost for optimized portal vein imaging and segmentation: a feasibility study
Year: 2026
Type: article
Abstract: Background: Clear visualization of the portal vein is essential for managing liver diseases such as those requiring transplantation and portal hypertension. However, abdominal computed tomography angiography (CTA) is often challenged by poor vascular contrast and lingering arterial signals, complicating manual segmentation. This study assesses the feasibility and effectiveness of using contrast-enhancement boost (CE-boost) and black-blood computed tomography (BBCT) techniques to improve image quality and reduce the manual segmentation time of the portal venous system in abdominal CTA. Methods: In this retrospective study, 50 patients undergoing abdominal CTA were processed by three methods: conventional portal venous phase (Group A), CE-boost images (Group B), and combined CE-boost with BBCT images (Group C). Subjective image quality was scored by two experienced radiologists, whereas objective metrics [signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR)] were calculated from standardized region of interest (ROI) measurements. Manual segmentation times were recorded. Statistical analyses employed intraclass correlation coefficient (ICC) for interobserver agreement, Kruskal-Wallis test for subjective scores, and one-way analysis of variance (ANOVA) for objective metrics, with P<0.05 denoting significance. Results: 3.7 and 4.4; P<0.001) and superior objective metrics (SNR and CNR improvements, all P<0.001) compared to Groups A and B. Moreover, median segmentation time in Group C (2.2 min) was markedly shorter than it was in Group A (5.1 min) and Group B (4.2 min) (P<0.001). Conclusions: The synergistic integration of CE-boost and BBCT markedly enhances portal vein visualization and substantially accelerates manual segmentation.
Authors: Hairong Jiang, Xiaofang Chen, Dingxiang Xie, Zhiman Lai, Jiale Wu, Jing Zhao, Rulin Xu, Hui Ma, Huasong Cai
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7125970241
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