Project ID BE-MI2024_15


Co Supervisor 1A Faculty of Life Sciences & Medicine, School of Biomedical Engineering & Imaging Sciences, Department of Biomedical EngineeringWebsite

Co Supervisor 1B Faculty of Life Sciences & Medicine, School of Biomedical Engineering & Imaging Sciences, Department of Cardiovascular ImagingWebsite

Enabling low field interventional MRI using novel AI-powered image reconstruction

Cardiac catheterisation is a common procedure in patients with congenital heart disease (CHD). During these procedures, a catheter and a guidewire are navigated into the cardiovascular system under fluoroscopic guidance, which is associated with significant radiation exposure and lack of soft tissue visualisation. Magnetic resonance imaging (MRI) is a promising alternative to fluoroscopy as it avoids ionising radiation, has excellent soft tissue contrast, and provide superior hemodynamic data. However, such interventional MRI system requires specialised MRI-compatible devices (catheters and guidewires) which currently have degraded mechanical properties. Modern low-field MRI scanners have recently emerged and represent an exciting avenue for interventional MRI where off-the-shelve standard devices can now be used safely. However, such scanners are associated with lower imaging framerate, signal-to-noise ratio (SNR), and image resolution.

The aim of this PhD is to enable MRI-guided cardiac catheterisation at low field by 1) developing an advanced online image reconstruction pipeline providing images with a high framerate, high SNR, and high spatial resolution required for real-time device tracking and 2) evaluating its potential during MRI-guided cardiac catheterisation in patients.
Aim 1/Year 1: Development of a deep learning-based image artifact suppression using a physics informed neural network to enable highly under-sampling acquisition and high frame rate imaging.
Aim 2/Year 2: Development of a deep-learning based image denoising approach for the reconstruction of high SNR images. Models including 2D, 2D+time, 2D+time+real-time motion correction will be evaluated.
Aim 3/Year 3: Development of deep learning based super resolution reconstruction to enhance image spatial resolution. Integration of temporal information and real-time motion correction will be explored.
Aim 4/Year 4: Clinical evaluation of the proposed technology in patients undergoing MRI-guided cardiac catheterisation.

Learning experience:
The student will develop a range of inter-disciplinary skills including in AI/deep learning, MRI physics, Medical imaging acquisition/reconstruction, computer programming, and cardiology.

Representative Publications

1. ‘M.N. Velasco Forte, K. Pushparajah, T. Schaeffter, I. Valverde Perez, K. Rhode, M. Ruijsink, B. Alhrishy, N. Byrne, A. Chiribiri, T. Ismail, T. Hussain, R. Razavi, and S. Roujol. Improved passive catheter tracking with positive contrast for MRI-guided cardiac catheterization using partial saturation (pSAT). Journal of Cardiovascular Magnetic Resonance, 19:60, 2017.

2. R. Mooiweer, R. Schneider, A.J. Krafft, K. Empanger, J. Stroup, A.P. Neofytou, R. Mukherjee, S. Williams, T. Lloyd, M. O’Neill, R. Razavi, T. Schaeffter, R. Neji, and S. Roujol. Active tracking based cardiac triggering for MR-thermometry during radiofrequency ablation therapy in the left ventricle. Frontiers in Cardiovascular Medicine, 9:971869, 2022.

3. S. Roujol, M. Ries, B. Quesson, C. T. W. Moonen, and B. Denis de Senneville. Real-time MR-thermometry and dosimetry for interventional guidance on abdominal organs. Magnetic Resonance in Medicine, 63(4):1080–1087, 2010.

1. M.N. Velasco Forte, S. Roujol, B. Ruijsink, I. Valverde, P. Duong, N. Byrne, S. Krueger, S. Weiss, Y. Arar, S.R. Veeram Reddy, T. Schaeffter, T. Hussain, R. Razavi, and K. Pushparajah. Magnetic resonance imaging for guided right and left heart cardiac catheterization: a prospective study in congenital heart disease. Journal of Magnetic Resonance Imaging, 53(5):1446–1457, 2021.

2. K. Pushparajah, H. Chubb, R. Razavi, MR-guided Cardiac Interventions, Top Magn Reson Imaging, 27(3):115-128, 2018. doi: 10.1097/RMR.0000000000000156

3. K. Pushparajah, A. Tzifa, R. Razavi, Cardiac MRI catheterization: a 10-year single institution experience and review, Interventional Cardiology, 6(3), 2014.