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Abstract：
The novel approach of our study consists in adapting and in evaluating a custom-made variational autoencoder (VAE) using two-dimensional (2D) convolutional neural networks (CNNs) on magnetic resonance imaging (MRI) images for differentiate soft vs. hard plaque components in peripheral arterial disease (PAD). Five amputated lower extremities were imaged at a clinical ultra-high field 7 Tesla MRI. Ultrashort echo time (UTE), T1-weighted (T1w) and T2-weighted (T2w) datasets were acquired. Multiplanar reconstruction (MPR) images were obtained from one lesion per limb. Images were aligned to each other and pseudo-color red-green-blue images were created. Four areas in latent space were defined corresponding to the sorted images reconstructed by the VAE. Images were classified from their position in latent space and scored using tissue score (TS) as following: (1) lumen patent, TS:0; (2) partially patent, TS:1; (3) mostly occluded with soft tissue, TS:3; (4) mostly occluded with hard tissue, TS:5. Average and relative percentage of TS was calculated per lesion defined as the sum of the tissue score for each image divided by the total number of images. In total, 2390 MPR reconstructed images were included in the analysis. Relative percentage of average tissue score varied from only patent (lesion #1) to presence of all four classes. Lesions #2, #3 and #5 were classified to contain tissues except mostly occluded with hard tissue while lesion #4 contained all (ranges (I): 0.2-100%, (II): 46.3-75.9%, (III): 18-33.5%, (IV): 20%). Training the VAE was successful as images with soft/hard tissues in PAD lesions were satisfactory separated in latent space. Using VAE may assist in rapid classification of MRI histology images acquired in a clinical setup for facilitating endovascular procedures.
摘要：
我们研究的新方法包括在磁共振成像（MRI）图像上使用二维（2D）卷积神经网络（CNN）来调整和评估定制的变分自动编码器（VAE），以区分软与外周动脉疾病（PAD）中的硬斑块成分。在临床超高场7特斯拉MRI上对五个截肢的下肢进行了成像。超短回波时间(UTE),获取T1加权(T1w)和T2加权(T2w)数据集。从每个肢体的一个病变获得多平面重建（MPR）图像。将图像彼此对齐并创建伪彩色红-绿-蓝图像。对应于由VAE重建的分类图像，定义了潜在空间中的四个区域。根据图像在潜在空间中的位置进行分类，并使用组织评分（TS）进行评分：（1）管腔专利，TS：0；（2）部分专利，TS:1;(3)多为软组织闭塞,TS:3;(4)多为硬组织闭塞,TS：5。每个病变计算TS的平均和相对百分比,定义为每个图像的组织评分的总和除以图像的总数。总的来说,2390个MPR重建图像被包括在分析中。平均组织评分的相对百分比从只有专利(损伤#1)到存在所有四类。病变#2、#3和#5被分类为包含组织，除了大部分被硬组织闭塞，而病变#4包含所有组织(范围(I):0.2-100%，(二):46.3-75.9%,(三):18-33.5%,（四）：20%）。训练VAE是成功的，因为PAD病变中的软/硬组织图像在潜在空间中令人满意地分离。使用VAE可以帮助在临床设置中获得的MRI组织学图像的快速分类以促进血管内手术。
