Super-resolution of cardiac magnetic resonance images using Laplacian Pyramid based on Generative Adversarial Networks.
Journal:
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Published Date:
Mar 1, 2020
Abstract
BACKGROUND AND OBJECTIVE: Cardiac magnetic resonance imaging (MRI) can assist in both functional and structural analysis of the heart, but due to hardware and physical limitations, high-resolution MRI scans is time consuming and peak signal-to-noise ratio (PSNR) is low. The existing super-resolution methods attempt to resolve this issue, but there are still shortcomings, such as hallucinate details after super-resolution, low precision after reconstruction, etc. To dispose these problems, we propose the Laplacian Pyramid Generation Adversarial Network (LSRGAN) in order to generate visually better cardiovascular ultrasound images so as to aid physician diagnosis and treatment.