Automated 4D function of the heart

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 industry platforms. This project will develop new methods for the visualization and analysis of 3D+time cardiac MRI exams, using the Siemens Frontier prototyping platform. This platform is available at King's College London through existing collaborations with Siemens Healthineers, and is built on the MeVisLab suite of visualization and analysis tools. This mechanism provides a pathway to translation of the resulting methods into clinical practice. The resulting app will become available to Siemens researchers and customers worldwide through the Frontier app store distribution mechanism, with the goal to become part of the Siemens product suite.

The current clinical standard MRI method for evaluating cardiac function consists of multiple 2D cine breath-hold scans [1]. Since each acquisition is performed in a separate breath hold, the slices are often mis-registered due to different breath-hold positions. Also, these acquisitions require complex planning and many patients cannot perform the multiple breath-holds required. Recently, advances in sparse sampling and parallel imaging have led to the development of respiratory gated 3D cine acquisitions [2, 3]. These do not require complex planning or patient compliance with breath-hold instructions. There is improved isotropic resolution and all regions of the heart are evenly sampled. However, current analysis tools have been optimized for the current standard 2D acquisition protocol and there is a need for robust automated analysis of the large amount of data resulting from these new 3D cine acquisitions.

This project will leverage recent advances in machine learning and artificial intelligence to automatically analyse 3D+t data, providing information on shape and motion of all four chambers of the heart. Transfer learning techniques will be investigated to enable existing neural networks trained on standard acquisitions to be adapted to the 3D+t analysis problem.

[1] Schulz-Menger, J., et al. (2013). "Standardized image interpretation and post processing in cardiovascular magnetic resonance: Society for Cardiovascular Magnetic Resonance (SCMR) board of trustees task force on standardized post processing." J Cardiovasc Magn Reson 15: 35.
[2] Wetzl, J., et al. (2018). "Single-breath-hold 3-D CINE imaging of the left ventricle using Cartesian sampling." MAGMA 31(1): 19-31.
[3] Feng, L., et al. (2018). "5D whole-heart sparse MRI." Magn Reson Med 79(2): 826-838.

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.