Deep learning for diagnosis of congenital heart disease in fetus using MRI and ultrasound

Aim of the PhD Project:

The aim of this project is to design a deep learning approach for diagnosis of congenital heart disease using fetal MRI and ultrasound, which includes visualisation of cardiac anatomy from motion corrected MRI, alignment of ultrasound of moving heart and blood flow, and interpretable deep learning for prediction post-natal outcomes.

Project Description / Background:

Congenital Heart Disease (CHD) is the most common congenital malformation, affecting 8 out of a thousand births. Up to 25% of babies with CHD have a major abnormality [1] and delayed diagnosis is associated with increased mortality [2], [3]. The standard clinical modality for prenatal diagnosis is ultrasound, however in many cases the diagnosis may be incomplete, due to difficulties in visualising the structure and topology of the major vessels [4] caused by artefacts and variable ultrasound image quality. Recently we have shown that motion corrected fetal MRI [5] improved diagnostic quality in 90% of the cases [6]. However, the motion corrected fetal cardiac MRI has a number of disadvantages. Firstly, the MRI has to be reconstructed from stack of slices corrupted by interslice motion and major motion, common at around 20 weeks pregnancy when the exam is required, results in motion correction algorithm failures. For this reason, the exam is currently postponed until 30 weeks of pregnancy. Secondly, the current pipeline requires time-consuming manual input, such as manual reorientation and segmentation, that prevents the translation to routine clinical practice. Finally, even if motion-corrected 3D MRI is good quality, it has low spatial resolution that prevents visualisation of anatomical details such as valves and does not capture movement of the heart. Ultrasound, on the other hand, though poor for visualisation of 3D topology, offers higher spatial and temporal resolution as well as measurement of the blood flow in fetal heart and vessels. We hypothesise that combining anatomical and functional multimodal markers into the common reference frame will open possibilities enhanced and more accurate diagnosis and prediction of outcomes for fetuses with CHD.

The aim of this project is to design a deep learning approach for diagnosis of CHD at mid-pregnancy that combines advantages of ultrasound and MRI. The project will progress in four stages:

Development of deep learning-based motion correction algorithm that allows for fully automatic and reliable reconstruction of fetal MRI at around 20 weeks of pregnancy
Development of automatic segmentation and 3D visualisation of fetal heart in motion corrected MRI
Registration of fetal US data with the motion corrected MRI to facilitate qualitative diagnosis by joint assessment of fetal heart anatomy, motion and blood flow
Interpretable machine learning to discover new complex biomarkers of abnormalities that are currently difficult to diagnose prior to birth, such as coarctation of the aorta, and prediction of post-natal outcomes in these babies.

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.