1st Supervisor: Andrew Scott, Imperial College London
2nd Supervisor: David Firmin, Imperial College London
Additional Supervisors: Guang Yang, Sonia Nielles-Vallespin, Pedro Ferreira, Imperial College London
Clinical Champion: Ranil de Silva, Imperial College London
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 / Background:
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 2.8×2.8mm2 to 1.8×1.8mm2.
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 and denoising DT-CMR datasets. In simulations of spiral DT-CMR, 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 (figure 1). 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.
This project would suit an enthusiastic, motivated student with a background in physics, applied mathematics or engineering with an interest in AI.
Figure 1: The effects of undersampling diffusion tensor cardiovascular magnetic resonance data along spiral trajectories and examples of reconstruction using convolutional neural networks.
- Nielles-Vallespin JACC 2017.https://dx.doi.org/10.1016/j.jacc.2016.11.051
- Nielles-Vallespin JMRI 2019.https://10.1002/jmri.26912
- Khalique JACC: Cardiovascular Imaging 2020. https://doi.org/10.1016/j.jcmg.2019.07.016
- Scott JCMR 2018. https://doi.org/10.1186/s12968-017-0425-8
- Gorodezky MRM 2019. https://doi.org/10.1002/mrm.27504
- Yang IEEE TMI 2018. https://doi.org/10.1109/TMI.2017.2785879
- Fernandez SCMR 2020 Poster WP-004.
- Luk SCMR 2019. Abstract 5.