Dynamic layer separation for coronary DSA and enhancement in fluoroscopic sequences

[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.

Inter/intra-frame constrained vascular segmentation in X-ray angiographic image sequence

Background

Automatic vascular segmentation in X-ray angiographic image sequence is of crucial interest, for instance, for better quantifying coronary arteries in diagnostic and interventional procedures.

Methods

A novel inter/intra-frame constrained vascular segmentation method is proposed to automatically segment vessels in coronary X-ray angiographic image sequence. First, a morphological filter operator is applied to remove structures undergoing the respiratory motion from the original image sequence. Second, an inter-frame constrained robust principal component analysis (RPCA) is utilized to remove the quasi-static structures from the image sequence. Third, an intra-frame constrained RPCA is employed to smooth the final extracted vascular sequence. Fourth, a multi-feature fusion is designed to improve the vascular contrast and the final vascular segmentation is realized by thresholding-based method.

Results

Experiments are conducted on 22 clinical X-ray angiographic image sequences. The global and local contrast-to-noise ratio of the proposed method are 6.6344 and 4.2882, respectively. And the precision, sensitivity and F1 value are 0.7378, 0.7960 and 0.7658, respectively. It demonstrates that our method is effective and robust for vascular segmentation from image sequence.

Conclusions

The proposed method is effective to remove non-vascular structures, reduce motion artefacts and other non-uniform illumination caused noises. Also, the proposed method is online which can just process one image per time without re-optimizing the model.

Robust and fast contrast inflow detection for 2D X-ray fluoroscopy

2D X-ray fluoroscopy is widely used in computer assisted and image guided interventions because of the real time visual guidance it can provide to the physicians. During cardiac interventions, acquisitions of angiography are often used to assist the physician in visualizing the blood vessel structures, guide wires, or catheters, localizing bifurcations, estimating severity of a lesion, or observing the blood flow. Computational algorithms often need to process differently to frames with or without contrast medium. In order to automate this process and streamline the clinical workflow, a fully automatic contrast inflow detection algorithm is proposed. The robustness of the algorithm is validated by more than 1300 real fluoroscopic scenes. The algorithm is computationally efficient; a sequence with 100 frames can be processed within a second.