AI enabled 3-fold accelerated in vivo Whole-Heart Diffusion Tensor Cardiac MR

Aim of the PhD Project:

3-times accelerated whole-heart in vivo diffusion tensor cardiac magnetic resonance (DT-CMR).
Development of 3D and simultaneous multi-slice (SMS) DT-CMR acquisitions
Development and optimisation of AI algorithms to minimise artefacts in 3D and SMS DT-CMR images.
Integration of these algorithms in the CMR image reconstruction pipeline for clinical translation.
Project Description / Background:

In vivo diffusion tensor cardiac magnetic resonance (DT-CMR) provides a means for non-invasive interrogation of the 3D microarchitecture of the beating heart, which no other clinical test allows1,2. This unique and innovative technology offers tremendous potential to improve clinical diagnosis through novel microstructural and functional assessment. DT-CMR data acquisition is currently very inefficient; ~10 minutes are needed to acquire a single 2D slice through the heart at one timepoint in the cardiac cycle. Whole-heart DT-CMR coverage is essential for accurate diagnosis in many cardiac diseases such as myocardial Infarction (MI) which affects the heart in a heterogeneous fashion. Improved efficiency is therefore key to DT-CMR clinical translation.

3D acquisitions allow increased coverage and SNR. Currently, 2D DT-CMR methods use single-shot readouts that acquire all the data needed for one image in one cardiac cycle. 3D DT-CMR would require segmented readouts that acquire all the data needed for one volume over several cardiac cycles. Segmentation combined with diffusion encoding leads to phase error artefacts. Furthermore, longer 3D readouts lead to distortion artefacts2.

Simultaneous multi-slice (SMS) techniques acquire multiple 2D slices simultaneously but reconstruct them separately, improving imaging efficiency tremendously. While SMS techniques have been successfully used in neuroimaging3, so far only proof-of-concept SMS DT-CMR studies have been published4,5. Since the heart is a smaller organ than the brain, the distance between multiple slices is smaller. The SMS algorithm often fails in these circumstances and information from one slice erroneously ends up on a different slice ("interslice-leakage" artefact).

Artificial Intelligence (AI) algorithms can learn complex relationships or patterns from training images and make accurate predictions on newly acquired images. AI algorithms can be trained to reconstruct highly undersampled 3D acquisitions, correct distortion artefacts and minimise motion-induced phase errors. SMS is also well poised to benefit from AI algorithms because multi-slice training data sets can easily be acquired. Ex vivo DT-CMR data can provide an excellent test case devoid of cardiac and respiratory motion, while in vivo DT-CMR data can be acquired in healthy volunteers and as well as patient cohorts.

In this project, Generative Adversarial Networks and other novel AI algorithms will be developed to minimise/eliminate artefacts in 3D DT-CMR and SMS DT-CMR ex vivo and in vivo data. These novel algorithms will be optimised to allow for the maximum possible speed-up factors, aiming for 3-fold accelerated whole-heart coverage (i.e. a whole-heart 9-slice protocol which currently takes ~90 minutes would require ~30 minutes with the targeted SMS acceleration factor of 3). These AI enabled in vivo whole-heart DT-CMR techniques will then be integrated in the clinical CMR image reconstruction pipeline for clinical translation. Reproducibility of these AI algorithms will be tested in healthy volunteers and patients with MI.

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.