Application of deblur technology for improving the clarity of digital subtractive angiography

Digital subtraction angiography (DSA) is most commonly used in vessel disease examinations and treatments. We aimed to develop a novel deep learning-based method to deblur the large focal spot DSA images, so as to obtain a clearer and sharper cerebrovascular DSA image.

The proposed network cascaded several residual dense blocks (RDBs), which contain dense connected layers and local residual learning. Several loss functions for image restoration were investigated. Our training set consisted of 52 paired images of angiography with more than 350,000 cropped patches. The testing set included 10 body phantoms and 80 clinical images of different types of diseases for subjective evaluation. All test images were acquired using a large focal spot, and phantom images were simultaneously acquired using a micro focal spot as ground-truth. Peak-to-noise ratio (PSNR) and structural similarity (SSIM) were determined for quantitative analysis. The deblurring results were compared with the original data, and the image quality was subjectively evaluated and graded by two clinicians.

For quantitative analysis of phantom images, the average PSNR/SSIM based on the deep-learning approach (35.34/0.9566) was better than that of large focal spot images (30.64/0.9163). For subjective evaluation of 80 clinical patient images, image quality in all types of cerebrovascular diseases was also improved based on a deep-learning approach (p < 0.001).

Deep learning-based focal spot deblur algorithm can efficiently improve DSA image quality for better visualization of blood vessels and lesions in the image.