Synergistic Image Analysis of longitudinal cardiac MRI

Aim of the PhD Project
Develop and evaluate frameworks for improving the estimation of cardiac biomarkers and their changes from longitudinal MR imaging
Investigate techniques ranging from separate independent analysis to 'synergistic' and simultaneous analysis of scans from multiple time points
Establish the repeatability of cardiac biomarker estimation using repeat scan data
Compare longitudinal analysis results to baseline repeatability performance
Project description:
Recent years have seen the emergence of state-of-the-art deep learning segmentations tools, reaching human-level performance on a range of tasks [1]. In cardiac MR, such techniques have been developed to derive imaging biomarkers for fully automated quality-controlled functional quantification [2]. In both clinical practice and research studies, it is common for subjects to be scanned multiple times. The resulting longitudinal data will contain significant similarities, as well as differences due to changes in anatomy and pathology over time as well as variations due to differences in acquisition. However, the image analysis tools that are employed to segment structures and derive biomarkers from imaging data do not typically exploit these similarities and treat the data as if they were independent. There is a need for methods that can sensitively identify biomarker changes, especially when trialling a new drug or treatment, as this would enable the trial size/duration to be reduced, which can have a significant impact on cost and the ability to determine the efficacy of the technology.
Some other strands of research, such as image reconstruction, have developed 'synergistic' approaches for processing longitudinal data [3]. Some related work has also been performed in image analysis, but to date this has mostly focused on exploiting longitudinal data for classification problems such as predicting treatment response [4,5], tumour detection [6,7] or assessing disease severity [8].

This project will seek to develop a 'synergistic' analysis framework for longitudinal imaging data in a novel application to biomarker estimation. Our focus will be on cardiac MR imaging, but we believe that the methods developed will be applicable to a wider range of modalities and problems, for example the methods developed may be extended to further organs to assess health in multi-organ conditions such as Covid-19. We will aim to exploit the similarities between scans in a synergistic way to enable more accurate and robust estimates to be made of morphological and functional biomarkers from all scans. We will also use repeat-scan data from the same subjects to establish a level of repeatability/reproducibility and evaluate our synergistic approaches against this. To begin with, we will develop methods for synergistically analysing two scans from the same scanner at different time points, but then seek to generalise the framework to multiple scans from multiple scanners at multiple time points. We will predominantly focus on deriving biomarkers via segmentations but will also investigate the possibility of synergistic direct estimation of biomarkers from the imaging data.

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.