Synthetic CTA images from deep-learning model close to real thing

A generative adversarial network (GAN)-based noncontrast CT angiography (CTA) system has promise in vascular diagnosis, suggest research findings published November 14 in Radiology. A team led by Jinhao Lyu, MD, from the Chinese PLA General Hospital in Beijing found that the synthetic images produced by their system were comparable to real CTA images, which could help assess the aorta and carotid arteries while avoiding risks related to contrast media use. “With further studies and assessment, this model may provide a fast and low-cost auxiliary CTA-like imaging method for patients, especially those with iodine allergy or limited access to health care,” Lyu and co-authors wrote. Iodinated contrast agents are widely used in CTA exams. While studies have highlighted the clinical utility of these agents, the researchers noted that they come with risks and drawbacks. These include adverse reactions from allergies, higher cost, and time consumption. Lyu and colleagues sought to develop and test its noncontrast deep learning GAN model to synthesize CTA-like images. This included evaluating image quality and the diagnostic accuracy of these synthetic images. The CTA-GAN model includes the following components: a generator designed for CTA-like image synthesis based on noncontrast CT images; a corrector to adapt the CT images to account for error loss; and a discriminator that estimated the probability of the input images being real CTA images versus synthetic images. The i...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: Advanced Visualization Source Type: news