Using Machine Learning to Identify Noninvasive Motion-Based Biomarkers of Cardiac Function
This project aims to apply state-of-the-art imaging, motion analysis and machine learning techniques to characterise the motion of the heart as it beats. A population-based, spatio-temporal atlas of cardiac motion has been developed that enables subtle variations in cardiac motion to be identified and quantified. This analysis forms the basis for the development of novel noninvasive biomarkers for the stratification of patients with cardiovascular disease. A notable success to date has been the development of biomarkers that can predict response to cardiac resynchronisation therapy with a success rate of 91% (Peressutti et al, Medical Image Analysis), whilst the current clinical selection criteria result in around one third of patients not responding positively to the treatment. More recent work has focused on the incorporation of multi-modal data into the atlas, in the form of MR and US imaging (Puyol-Anton et al, Medical Image Analysis) and the use of the spatio-temporal atlas to identify the characteristic spatial scales of motion signatures (Sinclair et al, Medical Image Analysis).
- M. Sinclair, D. Peressutti, E. Puyol-Antón, W. Bai, S. Rivolo, J. Webb, S. Claridge, T. Jackson, D. Nordsletten, M. Hadjicharalambous, E. Kerfoot, C. A. Rinaldi, D. Rueckert, A. P. King, "Myocardial Strain Computed at Multiple Spatial Scales from Tagged Magnetic Resonance Imaging: Estimating Cardiac Biomarkers for CRT Patients", Medical Image Analysis 43:169-185, 2017. (open access paper)
- E. Puyol-Antón, M. Sinclair, B. Gerber, M. S. Amzulescu, H. Langet, M. De Craene, P. Aljabar, P. Piro, A. P. King, "A Multimodal Spatiotemporal Cardiac Motion Atlas from MR and Ultrasound Data", Medical Image Analysis 40:96-110, 2017. (open access paper)
- D. Peressutti, M. Sinclair, W. Bai, T. Jackson, J. Ruijsink, D. Nordsletten, L. Asner, M. Hadjicharalambous, C. A. Rinaldi, D. Rueckert, A. P. King, "A Framework for Combining a Motion Atlas with Non-Motion Information to Learn Clinically Useful Biomarkers: Application to Cardiac Resynchronisation Therapy Response Prediction", Medical Image Analysis 35:669-684, 2017. (open access paper)
- E. Puyol-Antón, D. Peressutti, P. Aljabar, M. De Craene, P. Piro and A. P. King, "Towards a Multimodal Cardiac Motion Atlas for Biomarker Extraction", Proceedings ISBI, 2016. (paper)
- M. Sinclair, D. Peressutti, E. Puyol-Antón, W. Bai, D. Nordsletten, M. Hadjicharalambous, E. Kerfoot, T. Jackson, S. Claridge, C. A. Rinaldi, D. Rueckert, and A. P. King, "Learning Optimal Spatial Scales for Cardiac Strain Analysis Using a Motion Atlas", Proceedings MICCAI STACOM, 2016.
- D. Peressutti, W. Bai, T. Jackson, M. Sohal, C. A. Rinaldi, D. Rueckert, and A. P. King, "Prospective Identification of CRT Super Responders Using a Motion Atlas and Random Projection Ensemble Learning", Proceedings MICCAI, 2015. (paper, presentation)
- D. Peressutti, W. Bai, W. Shi, C. Tobon-Gomez, T. Jackson, M. Sohal, A. Rinaldi, D. Rueckert, and A. King, "Towards Left Ventricular Scar Localisation Using Local Motion Descriptors", Proceedings MICCAI STACOM, 2015. (paper)