AI enabled motion corrected quantitative MRI of the fetal brain

Aim of the PhD Project:

During the second half of pregnancy the human brain undergoes exuberant growth, with both microscopic and macroscopic changes happening rapidly. In consequence the MRI relaxation times (T1 and T2), which are key tissue properties, change substantially. These relaxation times can be used to characterise development, particularly in white matter. Relaxometry is a well established tool for assessing the brain in health and disease, however, there are currently no established methods for doing this in the fetus in utero. The aim of the project is to develop reliable motion tolerant methods for measuring T1 and T2 in the moving fetus, and to deploy these to conduct systematic quantitative studies over gestational age and so provide normative benchmark data.

Project Description / Background:

Our group has substantial track record in developing and deploying fully motion corrected methods for fetal 3D brain imaging in utero. A highly effective strategy is snapshot imaging of individual slices or small groups of slices, acquired fast enough to freeze fetal motion, which are then realigned to correct for changes in head position using slice to volume reconstruction (SVR)1. Past research has included developing comprehensive methods for anatomical, diffusion and functional imaging. We have also been able to measure the relaxation time T2* by fitting a standard physics signal model directly to single shot slices acquired using a dedicated multi-echo methodology. This approach allows the model fiting to be separated from motion correction. Methods for measuring T1 and T2 in moving tissue have been developed for the heart (ex utero) but these generally rely the constrained repetitive nature of cardiac motion. Measuring these parameters in fetal brain constitutes a special challenge because of the unpredictable nature of fetal motion. These parameters are also harder to measure than T2* as fitting the appropriate relaxometry models is likely to require combination of images with different contrasts acquired over multiple shots and controlling the spin physics is more complex. It is also critical that any methods developed have low RF power deposition, control risk of peripheral nerve stimulation in the mother and are time efficient as prolonged fetal examinations can be challenging for pregnant mothers, and increasingly the scope of these examinations is expanding as more comprehensive MRI methods are developed. The project will thus involve both research into novel sequences to achieve optimised acquisition strategies and also development of reconstruction methods that allow joint estimation of motion parameters and the desired relaxation parameters. Past reconstruction methods have been extremely computationally demanding and hence slow, so we propose to explore machine learning methods to shift the computational burden from examination time to a training phase allowing a much more clinically acceptable rapid image generation. This will build on our prior work on Deep Learning based reconstruction of cine cardiac images, which delivered state of the art performance, and has recently started to include motion correction as part of the reconstruction. We have also explored the application of Deep Leaning methods to SVR for anatomical imaging, so have a strong basis from which to build the current project. If robust quantitative T1 and T2 mapping can be achieved at high enough resolution, then it will become feasible to map oxygen extraction by exploiting the specific relaxation properties of haemaglobin. Recent results on fetal angiography provide encouraging evidence that it is feasible to resolve fetal vessels, although achieving this with quantitative methods will be a stretch target.

Strains on the healthcare system in the UK create an acute need for finding more effective, efficient, safe, and accurate non-invasive imaging solutions for clinical decision-making, both in terms of diagnosis and prognosis, and to reduce unnecessary treatment procedures and associated costs. Medical imaging is currently undergoing a step-change facilitated through the advent of artificial intelligence (AI) techniques, in particular deep learning and statistical machine learning, the development of targeted molecular imaging probes and novel "push-button" imaging techniques. There is also the availability of low-cost imaging solutions, creating unique opportunities to improve sensitivity and specificity of treatment options leading to better patient outcome, improved clinical workflow and healthcare economics. However, a skills gap exists between these disciplines which this CDT is aiming to fill.

Consistent with our vision for the CDT in Smart Medical Imaging to train the next generation of medical imaging scientists, we will engage with the key beneficiaries of the CDT: (1) PhD students & their supervisors; (2) patient groups & their carers; (3) clinicians & healthcare providers; (4) healthcare industries; and (5) the general public. We have identified the following areas of impact resulting from the operation of the CDT.

- Academic Impact: The proposed multidisciplinary training and skills development are designed to lead to an appreciation of clinical translation of technology and generating pathways to impact in the healthcare system. Impact will be measured in terms of our students' generation of knowledge, such as their research outputs, conference presentations, awards, software, patents, as well as successful career destinations to a wide range of sectors; as well as newly stimulated academic collaborations, and the positive effect these will have on their supervisors, their career progression and added value to their research group, and the universities as a whole in attracting new academic talent at all career levels.

- Economic Impact: Our students will have high employability in a wide range of sectors thanks to their broad interdisciplinary training, transferable skills sets and exposure to industry, international labs, and the hospital environment. Healthcare providers (e.g. the NHS) will gain access to new technologies that are more precise and cost-efficient, reducing patient treatment and monitoring costs. Relevant healthcare industries (from major companies to SMEs) will benefit and ultimately profit from collaborative research with high emphasis on clinical translation and validation, and from a unique cohort of newly skilled and multidisciplinary researchers who value and understand the role of industry in developing and applying novel imaging technologies to the entire patient pathway.

- Societal Impact: Patients and their professional carers will be the ultimate beneficiaries of the new imaging technologies created by our students, and by the emerging cohort of graduated medical imaging scientists and engineers who will have a strong emphasis on patient healthcare. This will have significant societal impact in terms of health and quality of life. Clinicians will benefit from new technologies aimed at enabling more robust, accurate, and precise diagnoses, treatment and follow-up monitoring. The general public will benefit from learning about new, cutting-edge medical imaging technology, and new talent will be drawn into STEM(M) professions as a consequence, further filling the current skills gap between healthcare provision and engineering.

We have developed detailed pathways to impact activities, coordinated by a dedicated Impact & Engagement Manager, that include impact training provision, translational activities with clinicians and patient groups, industry cooperation and entrepreneurship training, international collaboration and networks, and engagement with the General Public.