Abstract:  Digital subtraction angiography （DSA） is commonly used in the diagnosis of vascular diseases. The temporal subtraction-based method requires subtraction of two frames before and after injection of contrast agent to obtain vascular images， but factors such as involuntary patient movement or unaligned equipment make artifacts exist in the subtraction results. In this paper， a DSA artifact removal algorithm based on deformation field registration is proposed to improve the imaging quality of blood vessels after subtraction and to remove the artifacts in the nonvascular region. Firstly， a mask is extracted from the subtraction image with artifacts before registration to separate the vascular region from the non-vascular region， then the background and contrast frames are unimodally aligned using a deformation field registration network， and finally the aligned deformation image and the background frames are digitally subtracted and re-imaged to obtain a vascular subtraction image with the elimination of artifacts. In the experimental results of the test set， the MSE， PSNR， SSIM， and Dice coefficients are 30.619， 33.396， 0.901， and 0.687， respectively， all of which are significantly higher than other traditional or deep learning registration methods. The results show that the method proposed in this paper is more effective in removing artifacts， and the quality of subtractive imaging has been improved.

Key words: deep learning, deformation field registration, digital subtraction angiography, de-artifacting