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Updated: Jun 29, 2026

Anatomical Reconstructions of the Human Cardiac Venous System using Contrast-computed Tomography of Perfusion-fixed Specimens
06:02

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Published on: April 18, 2013

用深度学习优化冠状动脉CT图像重建以提高质量:一项回顾性研究

Agata Zdanowicz-Ratajczyk1, Michał Puła2, Adrian Korbecki2

  • 1Department of General Radiology, Interventional Radiology, and Neuroradiology, Wroclaw Medical University, Wroclaw, Poland.

Journal of computer assisted tomography
|April 17, 2025
PubMed
概括

深度学习图像重建 (DLIR-H) 显著提高冠状动脉CT血管造影 (CCTA) 的图像质量,而不是自适应统计代重建 (ASIR-V). 这一改进有助于对疑似冠状动脉疾病 (CAD) 的患者进行更准确的诊断.

关键词:
ASIR = 自适应的统计代重建.ASIRV = 自适应的统计代重建-非常增强的优化.CAD = 冠状动脉疾病 = 冠状动脉疾病CCTA = 冠状动脉计算机断层扫描血管造影CNR =对比度与噪声比率.DLIR = 深度学习图像重建DLIR-H = 在高层次的深度学习图像重建.FBP = 过的后投影.HU = 霍恩斯菲尔德单位投资回报率 = 感兴趣的地区.SNR = 信号与噪声的比.适应的统计代的重建适应的统计.冠状动脉疾病是一种冠状动脉疾病.冠状动脉计算机断层扫描血管图谱深度学习 图像重建 重建图像质量图像质量 图像质量

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