Artifact-aware Digital Subtraction Angiogram Image Generation for Head and Neck Vessels

Digital subtraction angiography (DSA) is an essential diagnostic tool for analyzing and diagnosing cardiovascular diseases. However, patient movement during image acquisition can introduce motion artifacts in DSA images, and this degradation in image quality always hinders accurate vessel identification and surgical treatment. Recently, some deep learning-based studies have been presented to address the artifact problem in DSA images by leveraging a generative model to produce high-quality DSA images directly from contrast images. Motionless data (paired contrast and artifact-free DSA images) is always required for these methods to train a model in a supervised manner. However, we face a dilemma that motionless DSA data is hard to acquire in clinical practice, most of which contain varying degrees of artifacts. This raises issues of insufficient motionless data and imperfect motion data for training effective deep generative models. To address this problem, we propose a new Artifact-aware DSA image generation method (denoted as AaDSA), which aims to generate high-quality DSA images with decreased artifacts using only motion-induced data. Specifically, a Gradient Field Transformation-based (GFT-based) method is introduced to obtain an artifact mask that identifies the artifact regions in a DSA image with minimal manual labeling costs. We then train an AaDSA model using the artifact mask as guidance, avoiding the adverse effect of artifact regions for model training. In the inference phase, the proposed AaDSA model can automatically generate a DSA-like image with decreased artifacts from a single contrast image without any human intervention. Experimental results on a real head-and-neck DSA dataset demonstrate the superiority of our method compared to state-of-the-art methods and its potential for clinical use.