Towards 10-Minute Magnetic Resonance Imaging Scans in Children with Machine Learning
Lead Research Organisation:
University College London
Department Name: Medical Physics and Biomedical Eng
Abstract
"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. "
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. "
Planned Impact
The critical mass of scientists and engineers that i4health will produce will ensure the UK's continued standing as a world-leader in medical imaging and healthcare technology research. In addition to continued academic excellence, they will further support a future culture of industry and entrepreneurship in healthcare technologies driven by highly trained engineers with deep understanding of the key factors involved in delivering effective translatable and marketable technology. They will achieve this through high quality engineering and imaging science, a broad view of other relevant technological areas, the ability to pinpoint clinical gaps and needs, consideration of clinical user requirements, and patient considerations. Our graduates will provide the drive, determination and enthusiasm to build future UK industry in this vital area via start-ups and spin-outs adding to the burgeoning community of healthcare-related SMEs in London and the rest of the UK. The training in entrepreneurship, coupled with the vibrant environment we are developing for this topic via unique linkage of Engineering and Medicine at UCL, is specifically designed to foster such outcomes. These same innovative leaders will bolster the UK's presence in medical multinationals - pharmaceutical companies, scanner manufacturers, etc. - and ensure the UK's competitiveness as a location for future R&D and medical engineering. They will also provide an invaluable source of expertise for the future NHS and other healthcare-delivery services enabling rapid translation and uptake of the latest imaging and healthcare technologies at the clinical front line. The ultimate impact will be on people and patients, both in the UK and internationally, who will benefit from the increased knowledge of health and disease, as well as better treatment and healthcare management provided by the future technologies our trainees will produce.
In addition to impact in healthcare research, development, and capability, the CDT will have major impact on the students we will attract and train. We will provide our talented cohorts of students with the skills required to lead academic research in this area, to lead industrial development and to make a significant impact as advocates of the science and engineering of their discipline. The i4health CDT's combination of the highest academic standards of research with excellent in-depth training in core skills will mean that our cohorts of students will be in great demand placing them in a powerful position to sculpt their own careers, have major impact within our discipline, while influencing the international mindset and direction. Strong evidence demonstrates this in our existing cohorts of students through high levels of conference podium talks in the most prestigious venues in our field, conference prizes, high impact publications in both engineering, clinical, and general science journals, as well as post-PhD fellowships and career progression. The content and training innovations we propose in i4health will ensure this continues and expands over the next decade.
In addition to impact in healthcare research, development, and capability, the CDT will have major impact on the students we will attract and train. We will provide our talented cohorts of students with the skills required to lead academic research in this area, to lead industrial development and to make a significant impact as advocates of the science and engineering of their discipline. The i4health CDT's combination of the highest academic standards of research with excellent in-depth training in core skills will mean that our cohorts of students will be in great demand placing them in a powerful position to sculpt their own careers, have major impact within our discipline, while influencing the international mindset and direction. Strong evidence demonstrates this in our existing cohorts of students through high levels of conference podium talks in the most prestigious venues in our field, conference prizes, high impact publications in both engineering, clinical, and general science journals, as well as post-PhD fellowships and career progression. The content and training innovations we propose in i4health will ensure this continues and expands over the next decade.
Organisations
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/S021930/1 | 30/09/2019 | 30/03/2028 | |||
2713593 | Studentship | EP/S021930/1 | 14/08/2022 | 13/08/2025 | Michele Pascale |