Abstract

Purpose

The assessment of vulnerable plaque characteristics and distribution is important to stratify cardiovascular risk in a patient. Computed tomography angiography (CTA) offers a promising alternative to invasive imaging but is limited by the fact that the range of Hounsfield units (HU) in lipid-rich areas overlaps with the HU range in fibrotic tissue and that the HU range of calcified plaques overlaps with the contrast within the contrast-filled lumen. This paper is to investigate whether lipid-rich and calcified plaques can be detected more accurately on cross-sectional CTA images using deep learning methodology.

Methods

Two deep learning (DL) approaches are proposed, a 2.5D Dense U-Net and 2.5D Mask-RCNN, which separately perform the cross-sectional plaque detection in the Cartesian and polar domain. The spread-out view is used to evaluate and show the prediction result of the plaque regions. The accuracy and F1-score are calculated on a lesion level for the DL and conventional plaque detection methods.

Results

For the lipid-rich plaques, the median and mean values of the F1-score calculated by the two proposed DL methods on 91 lesions were approximately 6 and 3 times higher than those of the conventional method. For the calcified plaques, the F1-score of the proposed methods was comparable to those of the conventional method. The median F1-score of the Dense U-Net-based method was 3% higher than that of the conventional method.

Conclusion

The two methods proposed in this paper contribute to finer cross-sectional predictions of lipid-rich and calcified plaques compared to studies focusing only on longitudinal prediction. The angular prediction performance of the proposed methods outperforms the convincing conventional method for lipid-rich plaque and is comparable for calcified plaque.

中文翻译：

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计算机断层扫描血管造影图像上富含脂质和钙化组织的横截面角度预测

摘要

目的

评估易损斑块的特征和分布对于分层患者的心血管风险非常重要。计算机断层扫描血管造影 (CTA) 为侵入性成像提供了一种有前途的替代方案，但受到以下事实的限制：富含脂质的区域中的亨斯菲尔德单位 (HU) 范围与纤维化组织中的 HU 范围重叠，并且钙化斑块的 HU 范围重叠与充满对比剂的管腔内的对比。本文旨在研究是否可以使用深度学习方法在横截面 CTA 图像上更准确地检测富含脂质和钙化的斑块。

方法

提出了两种深度学习（DL）方法，2.5D Dense U-Net 和 2.5D Mask-RCNN，分别在笛卡尔域和极域中执行横截面斑块检测。展开视图用于评估和显示斑块区域的预测结果。DL 和传统斑块检测方法的准确性和 F1 分数是根据病变水平计算的。

结果

对于富含脂质的斑块，两种提出的 DL 方法计算出的 91 个病变的 F1 分数的中值和平均值分别比传统方法高出约 6 倍和 3 倍。对于钙化斑块，所提出方法的 F1 分数与传统方法相当。基于 Dense U-Net 的方法的中位 F1 分数比传统方法高 3%。

结论

与仅关注纵向预测的研究相比，本文提出的两种方法有助于对富含脂质和钙化斑块进行更精细的横截面预测。所提出的方法的角度预测性能优于令人信服的富含脂质斑块的传统方法，并且与钙化斑块相当。