Automated quality control of MRI scans with machine learning

Magnetic Resonance Imaging (MRI) is a widely used 3-D medical imaging technique in modern healthcare systems. However, MRI scans are more prone to corruption due to patient motion than other types of imaging, such as CT. As low-quality scans may reduce the accuracy of subsequent analysis, quality control (QC) process that can flag these scans is often required. Currently, this process is typically accomplished by human experts via visual inspection. With the volume of MRI scans rising rapidly, expert-based QC is becoming increasingly impractical. Hence, there is considerable interest in developing automated QC tools. The existing automated QC frameworks are usually machine learning (ML) based. However, due to a lack of training data, their performance is currently limited. This project aims to develop methods to simulate realistic MRI scans to address this challenge. These methods will provide a vast source of simulated yet realistic MRI scans to develop new and improved ML-based automated QC tools. The main potential impact is that the developed automated QC tools can allow researchers and clinicians alike to focus their valuable time on analysing high-quality MRI data.

The aim of the project is to develop ML-based tools for automated MRI quality control.
To address this aim, this project has the following two objectives:
1. To develop an MRI scan simulator that can produce synthetic but realistic MRI scans to increase data availability for training ML-based techiques.
2. To incorporate synthetic scans into training to improve the performance of ML-based MRI quality control frameworks.