Mask-less Two-dimensional Digital Subtraction Angiography Generation Model for Abdominal Vasculature using Deep Learning.

Researchers

Journal

Modalities

Models

Abstract

To develop a deep learning model to generate synthetic, two-dimensional subtraction angiography images free of artifacts from native abdominal angiograms.In this retrospective study, two-dimensional digital subtraction angiograms (2D-DSA) and native angiograms were consecutively collected from July 2019 to March 2020. Images were divided into motion-free (training, validation, and motion-free test datasets) and containing motion artifacts (motion-artifact test dataset) sets. A total of 3185, 393, 383, and 345 images from 87 patients (mean age, 71 ± 10 years; 64 men, 23 women) were included in the training, validation, motion-free, and motion-artifacts test datasets, respectively. Native angiograms and 2D-DSA image pairs were used to train and validate an image-to-image translation model to generate synthetic deep learning-based subtraction angiography (DLSA) images. DLSA images were quantitatively evaluated by peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) using the motion-free dataset and were qualitatively evaluated by visual assessments by radiologists with a numerical rating scale using the motion-artifacts dataset.The DLSA images showed mean PSNR (± standard deviation) of 43.05 ± 3.65 dB and mean SSIM of 0.98 ± 0.01, indicating high agreement with the original 2D-DSA images in the motion-free dataset. Qualitative visual evaluation by radiologists on the motion-artifacts dataset showed that DLSA images contained fewer motion artifacts than 2D-DSA. Additionally, DLSA images scored similarly to or higher than 2D-DSA images for vascular visualization and clinical usefulness.The developed deep learning model could generate synthetic, motion-free subtraction images from abdominal angiograms with similar imaging characteristics to 2D-DSA images.Copyright © 2022. Published by Elsevier Inc.