A deep learning based self super-resolution method for Cardiac MRI
Principal Investigator: Mrs Muzi Guo
Approved Research ID: 57400
Approval date: June 9th 2020
High resolution (HR) Cardiac magnetic resonance images (CMRI) provide more anatomical details and enable more precise analyses, and are therefore highly desired in clinical and research applications. However, acquiring such data with an adequate resolution is time consuming. A common way to partly achieve this goal is to acquire MR images with good in-plane resolution and poor through-plane resolution to save scanning time.The aims of the research project is to improve through-plane resolution when saving MR scanning time.Using super-resolution method based on deep learning could use the mapping between the high in-plane resolution images and simulated lower resolution images, to estimate high resolution through-plane images.This project duration will last 24 months.The expected value of the research would produce high through-plane resolution as well as saving Cardiac MRI scanning time.