Determining the scanning range of coronary computed tomography angiography based on deep learning

doi:10.4329/wjr.v17.i7.110394

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 17, Issue 7

This Article

Academic Content and Language Evaluation of This Article

CrossCheck and Google Search of This Article

Academic Rules and Norms of This Article

Citation of this article

Corresponding Author of This Article

Research Domain of This Article

Article-Type of This Article

Open-Access Policy of This Article

Times Cited Counts in Google of This Article

Number of Hits and Downloads for This Article

Total Article Views (416)

All Articles published online

The chart showing PDF series, HTML series, Figures (1-6) series, Tables (1-1) series.

Item

Count

PDF

HTML

223

Figures (1-6)

Tables (1-1)

Sum=338

Publishing Process of This Article

The chart showing Browse series, Download series.

Item

Count

Browse

Download

Sum=54

Jul 28, 2025 (publication date) through Aug 9, 2025

Times Cited of This Article

Times Cited (0)

Journal Information of This Article

Publication Name

World Journal of Radiology

ISSN

1949-8470

Publisher of This Article

Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Retrospective Study

World J Radiol. Jul 28, 2025; 17(7): 110394
Published online Jul 28, 2025. doi: 10.4329/wjr.v17.i7.110394

Full Size Figure Download Figure

Figure 6 Scan range for coronary computed tomography angiography. A: Original image; B: Scan range predicted by the model itself; C: Scan range recommended by the Chinese Medical Association Radiology Society Cardiac-Chest Group; D: Society of Cardiovascular Computed Tomography recommendation. TCIM: Tracheal carina inferior margin; CDLP: Cardiac diaphragmatic lowest point; RHB: Right heart border; LHB: Left heart border.

Citation: Zhao YH, Fan YH, Wu XY, Qin T, Sun QT, Liang BH. Determining the scanning range of coronary computed tomography angiography based on deep learning. World J Radiol 2025; 17(7): 110394
URL: https://www.wjgnet.com/1949-8470/full/v17/i7/110394.htm
DOI: https://dx.doi.org/10.4329/wjr.v17.i7.110394