VCC-DSA: A Novel Vascular Consistency Constrained DSA Imaging Model for Motion Artifact Suppression

TL;DR

This paper introduces VCC-DSA, a new imaging model that effectively suppresses motion artifacts in DSA images, enhancing vascular visibility through innovative learning and consistency strategies, validated on animal and clinical data.

Contribution

The paper presents a novel Vascular Consistency Constrained DSA model with a learning paradigm, residual blocks, a vascular consistency strategy, and a data self-evolution method for superior motion artifact suppression.

Findings

Improves PSNR by 73.4% over existing methods.

Enhances SSIM by 8.56%, indicating better image quality.

Validated on animal and human clinical data, demonstrating practical effectiveness.

Abstract

Digital Subtraction Angiography (DSA) is a clinically significant imaging technique for diagnosing cerebrovascular disease, as gold-standard. However, the artifacts caused by motion of high-attenuation tissues such as bones, teeth, and catheters, seriously reduce the visibility of blood vessels. This paper presents a novel Vascular Consistency Constrained DSA Imaging Model (VCC-DSA) for robust motion suppression and precise vascular imaging with the following designs: 1) We specially design a Learning-based Subtraction Mapping Paradigm, so that the ill-posed problem of existing learning-based methods can be solved to enhance the stability of the algorithm. 2) Our model effectively develops Residual Dense Blocks and details-shortcut to improve the performance under complex structures, such as moving bones overlapping with blood vessels, and small features, like peripheral vessels. 3) An innovative Vascular Consistency Strategy is proposed to extract intrinsically consistency from the various relative motions in mask-live images, so that spontaneously distils the vascular structure with contrast-agent development and robustly suppress motion artifacts, and also naturally alleviates the high matching requirements of data. 4) We creatively design a Mixup-based Data Self-evolution Strategy for data-intra self-enhancement in training loop, so that the training data gains dynamically optimized to promote model better learning the vascular features, and excluding the irrelevant structures in live/mask image and even the inevitable-artifacts/fake-structure in label. Prospectively, to further evaluate practical value, an actual general anesthesia animal experiment is specially conducted, besides the assessment on human clinical data. Compared with other method, our model improves the PSNR and SSIM by 73.4% and 8.56%, respectively.