Robust quality-controlled quantitative stress perfusion cardiac MRI

Aim of the PhD Project:

Fully automatic assessment of perfusion cardiac MRI can suffer from reliability issues due to low image quality, failed contrast injections, or a lack of response to stress. This project will develop a range of AI-enabled quality control processes to make perfusion cardiac MRI more robust and reliable in the clinic.

Project Description:

The main cause of the underutilisation of stress perfusion cardiac MRI is that visual assessment of the images is highly dependent on the level of training of the operators [1]. As yet, quantitative analysis of stress perfusion cardiac MRI remains primarily a research tool but its clinical translation would be advantageous as it can be automated, enabling accurate and user-independent assessment of myocardial perfusion. Our group has also recently demonstrated the independent prognostic value of quantitative stress perfusion CMR [2]. However, this work still involved several steps of manual interaction, including the segmentation of the myocardium. The automated analysis of quantitative stress perfusion CMR has the potential to revolutionise the management of patients with suspect coronary artery disease.

Our group has pioneered the use of deep learning to achieve the automatic quantitative analysis of stress perfusion CMR [3]. The problem of deploying this solution at large scale is the visual inspection and quality control of the analysis is not feasible. If it is not obvious for which cases the analysis has failed, then clinicians may be presented with failed or inaccurate measurements and, therefore, draw the wrong conclusions. We aim to overcome this limitation by developing a fully automated image quality control (QC) tool using deep neural networks. Deep learning has been widely adopted in the field of medical imaging and has become the de facto standard for many processing tasks. Trends in cardiac MRI image analysis have followed a similar route where deep learning is now used for everything from reconstruction to detection and segmentation tasks to automating diagnostics and prognostics.

To this end, we envisage the development of three QC processes to be developed, validated, and integrated into the clinical workflow.

Analysis of the arterial input function (AIF): Machine learning methods will be used to classify whether the injection of the contrast agent into the patient was done correctly.
Analysis of stress response: Some patients do not respond as expected to the injection of the stressor drug. Algorithms will be trained and validated to identify these patients.
Detection of failed cases. Our previously developed automated processing pipeline can fail, in that the heart is predicted in the wrong location or the predicted shape of the heart is not as expected. A further neural network will be developed to flag these cases.

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.