Evidence map›Paper›PMID 41669408›Full record

ArticleQuantitative imaging in medicine and surgery2026

Black-blood computed tomography with contrast-enhancement boost for optimized portal vein imaging and segmentation: a feasibility study.

Hairong Jiang, Xiaofang Chen, Dingxiang Xie, Zhiman Lai, Jiale Wu, Jing Zhao, Rulin Xu, Hui Ma, Huasong Cai

Abstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Hairong Jiang *Department of Radiology, Foresea Life Insurance Guangzhou General Hospital, Guangzhou, China.
Xiaofang Chen *Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Dingxiang Xie *Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Zhiman LaiDepartment of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Jiale WuDepartment of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Jing ZhaoDepartment of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Rulin XuDivision of CT & MR Business, Department of Clinical Science Research, Canon Medical Systems, Guangzhou, China.
Hui MaDepartment of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Huasong CaiDepartment of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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: Group C demonstrated significantly higher subjective image quality scores (median 5.0 Conclusions: The synergistic integration of CE-boost and BBCT markedly enhances portal vein visualization and substantially accelerates manual segmentation.

Indexed as

black-blood computed tomography (BBCT)contrast-enhancement boost (CE-boost)image qualitymanual segmentationPortal vein

Identifiers

PMID41669408
PMCPMC12883468

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.