[Development of the DSA Method for Coronary Angiography Using Deep Learning Techniques].

PURPOSE

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.

METHODS

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.

RESULT

The results of both the subjective evaluation and the objective evaluation confirmed the effectiveness of the proposed method in clinical cases.

CONCLUSION

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.