Evaluation of Pulmonary Vein Variations Using Multidetector Computed Tomography


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DOI:

https://doi.org/10.5152/EurJTher.2017.251

Keywords:

Pulmonary vein, variation, Multidetector Computed Tomography

Abstract

Objective: In this study, we aimed to identify variations in pulmonary veins (PVs) that are important for preparation before a radiofrequency ablation (RFA) using 64 multidetector computed tomography (MDCT) and to classify their incidence and drainage types.
Methods: In total, 503 patients, including 312 males and 191 females who were examined by abdominal computed tomography angiography for various reasons between January 2011 and December 2016 were included in this study. A 64-section CT device was used for scanning. Two-dimensional multiplanar reformats were created from axial images, and 3-dimensional images were created using maximum intensity projection and volume rendering methods. PV anatomic variations were identified.
Results: A pulmonary venous drainage pattern with the classical pattern, i.e., four ostia with two ostia on the right and two on the left, was observed in 44.8% of the patients. The remaining patients had varying anatomies on the right or on the left. In addition, 3.4% had right top PV, 72.4% had the classical type with two ostia on the right, whereas 27.6% had the varying type with one ostium or more than two ostia on the right. In addition, 61.5% had the classical pattern with two atrial ostia on the left, whereas 38.5% had the varying drainage patterns with one atrial ostium or three atrial ostia on the left. Our study is important in terms of being the largest series to date with 503 patients. The type that involves three separate atrial ostia on the left classified as L3 (Left) is not included in Marom’s classification.
Conclusion: MDCT accurately identifies pulmonary venous anatomy in detail, which is important in RFA preparation.

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References

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Published

2023-04-19

How to Cite

Demir, M., & Gelebek Yılmaz, F. (2023). Evaluation of Pulmonary Vein Variations Using Multidetector Computed Tomography. European Journal of Therapeutics, 24(2), 86–93. https://doi.org/10.5152/EurJTher.2017.251

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