Image Quality Transfer Using Generative Models

1. Brief description of the context of the research including potential impact

High-field MRI scanners are crucial for accurate diagnostic and clinical management in medical imaging. However, low-field MRI scanners, which have a lower magnetic strength (<1T) compared to 1.5T or 3T scanners, are still widely used in applications such as portable MRI or in lower- and middle-income countries. These scanners often have a lower signal-to-noise ratio (SNR) and less contrast between brain tissues, limiting their application in various clinical stages and for further image analysis. To address these limitations, researchers have proposed using Image Quality Transfer (IQT) to improve the quality of low-field medical images by transferring the rich information from high-field images. Previous approaches to IQT have utilized deep learning to restore high-quality information and have been shown to be superior to other methods, including interpolation and classical machine learning. However, these approaches were only limited to restoring up to x4 image resolution and none of the previous work has tested their methods on clinically relevant metrics or various domain-shifted unseen data, such as lesions and MRI scans from children.

2. Aims and objectives The aims and objectives of this project are:
-To explore and use the latest advance in generative models for IQT
- Propose more clinically relevant image quality assessment method
- Increase computational speed and robustness of the model to unseen/domain-shifted data

3. Novelty of the research methodology

In this project, we will propose a new approach for IQT based on recent paradigms in image generation/translation, such as Diffusion models and GANs. This methodology will be validated on various domain-shifted data, such as lesions and MRI scans from children, using our new image quality assessment metric that is more clinically relevant. Furthermore, the PhD will also investigate the application of data-efficient techniques, such as unsupervised/self-supervised learning, to IQT as well as cross-modality.

4. Alignment to EPSRC's strategies and research areas

This project strongly fits to the following EPSRC's research themes:
- Healthcare Technologies
- Information and communication technologies
- Artificial Intelligence Technologies

5. Any companies or collaborators involved

None

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.