Machine Learning for Automated Heart Strain and Motion from DENSE

Aim of the PhD Project:

DENSE MRI provides accurate and reproducible myocardial strain, but processing currently relies on extensive manual input.
This project will utilize existing data and state-of-the-art machine-learning algorithms to automatically process and segment DENSE data, outputting cardiac geometry as well as pixelwise displacement and strain.

Project Description / Background:

Recent advances in machine learning and artificial intelligence methods have enabled the development of new tools for the quantitative analysis of cardiac performance in medical imaging examinations. However, applications to patients require implementation and evaluation on clinical scans in specific disease cases. This project will develop new methods for the analysis of DENSE cardiac MRI exams. The resulting software tools will be used in clinical studies performed at the Royal Brompton Hospital.

DENSE is the most accurate and best resolution method for non-invasive quantification of strain and motion of heart muscle [1,2]. However, the images currently require complex and time-consuming off-line post-processing to estimate the clinically important strain parameters. In particular, the borders of the heart muscle must be determined (segmentation) and the phase signal unwrapped (due to aliasing). In the presence of noise and artefacts this is difficult and errors need to be corrected manually. The post-acquisition nature of this processing also means that MRI technologists are operating "blind" to a large extent, with little indication of the quality of the strain data and results, which could be used to guide subsequent data acquisition. Recently, advances in machine learning and AI methods have shown promise in automatic evaluation of cardiac MRI data [3, 4]. These do not require manual interaction and can considerably speed up the evaluation process.

This project will leverage recent advances in machine learning and artificial intelligence to automatically analyse DENSE data, including segmentation and phase unwrapping, to provide accurate pixel-wise strain information in all regions of the heart. Training will make use of the high performance computing clusters at Imperial and/or KCL. The trained models will be incorporated into online image reconstruction tools, providing immediate measures of myocardial displacement and strain at the scanner.

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.