Concurrent multi-task learning-based deep convolutional neural networks for high resolution assessment of in-vivo cardiac microstructure

Aim of the PhD Project:

Develop a concurrent multi-task learning-based deep convolutional neural network (CNN) for data acquisition and image reconstruction of highly-undersampled spiral MRI data with minimal artefact.
Deploy these methods for efficient, high-resolution in-vivo diffusion tensor cardiovascular magnetic resonance of cardiac microstructure.
Validate these methods in controls and patients with myocardial infarction (MI).

Project Description:

The complex arrangement and dynamics of heart muscle cells (cardiomyocytes) and groups of cardiomyocytes known as sheetlets is vital to normal cardiac function. Diffusion tensor cardiovascular magnetic resonance (DT-CMR) is a unique MRI method providing information on microscopic tissue structures, based on measuring the self-diffusion of water. DT-CMR can infer the orientation of cardiomyocytes and sheetlets, which reorientate during contraction and provide measures sensitive to changes in extracellular space, membrane integrity and coherence of cardiomyocyte orientation. This novel method is increasingly used to investigate the microscopic changes underlying disease.

As part of our ongoing investigations into cardiac microstructure at the Cardiovascular Magnetic Resonance Unit, The Royal Brompton Hospital, we have shown that acquisition of DT-CMR at multiple timepoints in the cardiac cycle is possible using the STEAM technique, which is a low signal to noise ratio (SNR) technique and therefore requires many signal averages at low resolution. As a result, pathologies affecting the right ventricle and atria, the thinned myocardium of chronically infarcted tissue and small focal changes cannot be adequately investigated.

We recently developed a technique which samples data along two interleaved spiral paths to split the acquisition of higher resolution DT-CMR data across two heartbeats. However, interleaved DT-CMR techniques are sensitive to artefacts caused by localised changes in the magnetic field and motion between the two spirals. Despite these artefacts, we were able to increase the in-plane resolution of in-vivo DT-CMR acquisitions from 2.8x2.8mm2 to 1.8x1.8mm2[5].

Unfortunately, acquiring two interleaves requires doubling the already long scan times, with typically twenty 18s breath holds required per slice and cardiac phase. In contrast, reconstructing data from a single interleave saves time, improves patient comfort and avoids some of the associated artefacts. Parallel imaging is frequently used to reduce the data required in MRI, but the associated loss of SNR, long computation times and the small field of view used in spiral STEAM DT-CMR are incompatible.

In recent work we have established the effectiveness of deep convolutional neural networks (CNNs) in MRI reconstruction[6] and denoising DT-CMR datasets[7]. In simulations of spiral DT-CMR[8], we retrospectively undersampled DT-CMR images along spiral trajectories and trained a CNN using the fully sampled data as the ground truth. We demonstrated effective removal of aliasing artefacts with undersampling factors of up to 4 in this promising, but early stage pilot data. Here, we aim to build on these initial computational simulations and develop a clinically applicable in-vivo tool for CNN enabled efficient and robust high-resolution spiral DT-CMR in challenging patient cohorts. The student will develop a novel concurrent multi-task learning-based deep CNN for optimising data acquisition (i.e. seeking optimal spiral trajectories) and achieving high-fidelity image reconstruction (i.e. removal of undersampling and other artefacts) simultaneously. These methods will be tested in simulations and in-vivo using our state-of-the-art 3T Siemens Vida scanner at The Royal Brompton Hospital.

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.