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Updated: Jun 10, 2025

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Published on: January 18, 2021

一种机器学习算法,用于从常规的心脏MRI定位器创建异型3D大动脉细分.

Yue Jiang¹, Karan Punjabi², Iain Pierce³

¹Institute of Cardiovascular Science, University College London, London WC1N 1DZ, United Kingdom.

Magnetic resonance imaging

|October 14, 2024

概括

此摘要是机器生成的。

一种新的3D U-Net方法从标准的2D心脏MRI定位器创建精确的3D大动脉细分. 这种技术可以在没有额外的成像序列的情况下有效地查大动脉动脉瘤.

关键词:

大动脉直径的大动脉直径.同源性大动脉细分的同源性大动脉细分.机器学习是机器学习.常规的异型定位器.英国生物银行.

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Last Updated: Jun 10, 2025

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

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科学领域:

心血管成像 - 心血管成像
医学图像分析医学图像分析
人工智能在医学中的应用

背景情况:

大动脉动脉瘤检测和测量是关键的临床挑战.
高分辨率的3D心脏磁共振 (CMR) 图像提供了详细的大动脉评估,但耗时,限制了其在例行查和人口研究中的使用.

研究的目的:

开发和验证一种方法,从标准的低分辨率2DCMR定位器图像生成3D同位素大动脉细分.
评估这些细分的临床适用性和准确性,以确定大动脉动脉瘤.

主要方法:

一个3D U-Net模型 (U-Net_LR) 被训练使用模拟的异型二维定位器图像与临床医生生成的3D异型三维分段面具配对.
分段质量和准确性在外部数据集 (英国生物库) 上进行了评估,并与高分辨率3D同位素图像和在高分辨率数据上训练的U-Net变体 (U-Net_HR) 相比较.

主要成果:

由U-Net_LR生成的93%的3D细分在外部数据集上被认为是临床适用的.
U-Net_LR与地面真相细分表现出很好的一致性 (平均DICE分数为0.9),与U-Net_HR相比.
来自U-Net_LR,U-Net_HR和临床观察员的直径测量显示,关键大动脉区域之间没有显著差异.

结论:

一种新方法有效地从常规的2DCMR定位器产生3D异型大动脉细分.
这种方法与高分辨率3D数据的细分有很强的一致性,并具有作为大动脉动脉瘤的非侵入性查工具的潜力,消除了对额外专业序列的需求.