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自动分类的磁共振组织学外围动脉慢性总闭塞使用变量自编码器:一个可行性研究

Judit Csore^1,2, Christof Karmonik³, Kayla Wilhoit³

¹DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin Street, Houston, TX 77030, USA.

Diagnostics (Basel, Switzerland)

|June 10, 2023

概括

这项研究使用自定义变异自编码器 (VAE) 与卷积神经网络 (CNN) 来分析外周动脉疾病 (PAD) 斑块的磁共振成像 (MRI). VAE成功地区分了软质和硬质斑块组成部分,有助于对潜在的内血管手术指导进行分类.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
心血管研究研究心血管研究

背景情况:

周围动脉疾病 (PAD) 的诊断和治疗规划需要准确地描述斑块的组成.
在PAD中区分软与硬斑块成分对于有效的内血管干预至关重要.
当前的成像技术在精确量化斑块特征方面可能存在局限性.

研究的目的:

适应和评估一个自定义的变化自编码器 (VAE),使用2D卷积神经网络 (CNN) 来分类PAD中的软硬斑块.
评估VAE使用高场7特斯拉MRI数据区分斑块组件的能力.
探索VAE在MRI图像的快速组织学分类方面的潜力,以协助内血管程序.

主要方法:

获取超高场7特斯拉MRI数据集 (UTE,T1w,T2w) 来自五个截肢的下肢与PAD.
从对齐的多平面重建 (MPR) 图像创建伪色RGB图像.
训练VAE根据它们在潜伏空间中的位置来分类图像,为光膜,软和硬组织组成部分分配组织分数 (TS).
从2390张重建图像中计算每次TS的平均值和相对百分比.

主要成果:

成功训练VAE,证明在潜伏空间内的PAD病变中,软和硬组织的分离是令人满意的.
病变的分类揭示了各种比例的专用光线,软组织和硬组织组成部分.
损伤#4包含所有分类的组织类型,而损伤#2,#3和#5主要包括软组织,硬组织的存在有限.

结论:

开发的VAE方法有效地使用7特斯拉MRI数据在PAD中区分软硬斑块组件.
这种由人工智能驱动的方法对MRI图像的快速组织学分类充满希望.
VAE可能有助于临床医生规划和执行PAD的内血管程序.

关键词:

血管造影 - 血管造影血管新生整形术血管新生整形术慢性四肢威胁性缺血症是什么慢性完全封闭症慢性完全封闭症血管内治疗血管内治疗非侵入性诊断是一种非侵入性诊断.周围动脉疾病周围动脉疾病超高场磁共振成像超高场磁共振成像

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