Towards 10-Minute Magnetic Resonance Imaging Scans in Children with Machine Learning

"1) Magnetic Resonance Imaging (MRI) has enabled significant advances in the diagnosis and management of many childhood diseases. However, MRI is challenging in the pediatric population as it is time consuming (~1 hour to perform) and requires patient cooperation. Hence it is often necessary to use general anesthesia (GA) in children below 8-years of age, which is both costly and carries some risk.

One way of overcoming these problems would be to speed up the MRI scans so children do not have to keep still or hold their breath. The simplest way of doing this is to acquire less data (data undersampling), however this results in artefacts that make the images unusable. Current reconstruction methods for removing these artefacts, allow limited acceleration, or use time consuming algorithms which hamper their clinical uptake.

A new approach is Machine Learning that aims to 'learn' how to remove undersampling, as well as motion artefacts.

2) By the end of this project, the student will have an excellent understanding of machine learning algorithms particularly for reconstruction of MRI data. The student will also be able to use an MRI scanner, understand MRI sequence design, as well as traditional and state-of-the-art MRI reconstruction algorithms. This is a very translational project and will include working closely with clinical partners.

All work packages will be integrated into standard clinical workflow to enable clinical validation studies, and simple translation into routine clinical practise.

3) This study aims to develop novel accelerated magnetic resonance imaging (MRI) technologies which will allow scan times to be reduced from ~1 hour to ~10 minutes in children with diseases within the abdomen. This will be achieved this through development of optimised MR acquisition strategies combined with Machine Learning (ML) reconstruction techniques. "