Yamamoto Megumi, Okura Yasuhiko
Department of Clinical Radiology, Faculty of Health Science, Hiroshima International University.
Nihon Hoshasen Gijutsu Gakkai Zasshi. 2022;78(2):129-139. doi: 10.6009/jjrt.780203.
DSA processing is not applied to the coronary artery because heavy artifacts will be generated in DSA images by heart beats and breathing of the patient. However, DSA images of coronary artery contribute to the accuracy of diagnosis and elucidating new pathogenesis of coronary artery diseases. On the other hand, deep learning techniques have been improved and shown to have a high performance. The purpose of this study was to develop a DSA method available for coronary arteries using deep learning techniques.
We developed a convolutional neural network (CNN) -based model for this study. A total of 29 cases of coronary angiograms were used. We carried out training of the model using 21025 image patches consisting of pairs after contrast-enhanced images and before contrast-enhanced images. To obtain DSA images, we derived mask images by inputting live images to the trained model. Finally, we obtained DSA images subtracting mask images from live images. We evaluated the proposed method using subjective evaluation, which is based on human observation, and objective evaluation, which is based on standard deviation of the pixel value.
The results of both the subjective evaluation and the objective evaluation confirmed the effectiveness of the proposed method in clinical cases.
We proposed new DSA techniques for coronary arteries using deep learning. Motion artifacts caused by heart beats and breathing were reduced, and vessel visibility was improved by the proposed DSA. By applying the technique to the area of other organs, DSA study without stopping breathe during X-ray exposure can be realized in clinical situations.
由于患者的心跳和呼吸会在数字减影血管造影（DSA）图像中产生严重伪影，因此DSA处理不适用于冠状动脉。然而，冠状动脉的DSA图像有助于提高诊断准确性并阐明冠状动脉疾病的新发病机制。另一方面，深度学习技术已经得到改进并显示出高性能。本研究的目的是利用深度学习技术开发一种适用于冠状动脉的DSA方法。
我们为这项研究开发了一种基于卷积神经网络（CNN）的模型。总共使用了29例冠状动脉造影病例。我们使用由增强对比图像和未增强对比图像组成的21025个图像块对模型进行训练。为了获得DSA图像，我们将实时图像输入到经过训练的模型中以导出掩码图像。最后，我们通过从实时图像中减去掩码图像来获得DSA图像。我们使用基于人工观察的主观评估和基于像素值标准差的客观评估来评估所提出的方法。
主观评估和客观评估的结果均证实了所提出的方法在临床病例中的有效性。
我们提出了使用深度学习的冠状动脉新DSA技术。所提出的DSA减少了由心跳和呼吸引起的运动伪影，并提高了血管的可视性。通过将该技术应用于其他器官区域，可以在临床情况下实现X射线曝光期间不停止呼吸的DSA研究。
