Research Focus
The Motion Modelling and Analysis group (MMAG) is an academic research group based within the School of Biomedical Engineering and Imaging Sciences of King's College London. The group is based at St. Thomas' Hospital in central London.
The primary focus of the MMAG's research is on the imaging, modelling and estimation of repetitive motion. Organ motion due to respiration and the beating of the heart is approximately periodic, so measurements of this motion made from imaging data can be used to construct mathematical models of the periodic motion and its variability. Such models can be applied to update guidance information in image-guided interventions or to correct acquired imaging data for the corrupting effects of motion. The group has an interest in applying such models in a range of applications, but predominantly in the context of image-guided cardiac interventions and PET-MR imaging.
As well as seeing motion as a problem that must be overcome, another research theme of the group is the analysis of motion with the aim of extracting clinically useful biomarkers. Here, work focuses mainly on cardiac cycle motion, in which changes in heart function will manifest themselves as subtle changes in the observed motion of the heart as it beats. By learning distinctive motion signatures it is possible to derive clinically useful information purely by measuring motion patterns.
LATEST UPDATES
- 30 July 2018: MICCAI STACOM paper on UK Biobank based cardiac motion atlas by Esther Puyol Anton - now available online: Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas
- 18 July 2018: Still time to apply for a fully-funded PhD studentship in explaining the predictions of deep learning in cardiology, co-supervised by Dr Andrew King and Dr Ben Glocker, available through the King's College London/Imperial College London Centre for Doctoral training (apply here, deadline 10 August)
- 14 June 2018: Four new ISBI papers now available online:
- D. R. Balfour, J. . Clough, X Chen, M. Belzunce, C. Prieto, P. K. Marsden, A. J. Reader, A. P. King, "PET-MR Respiratory Signal Estimation Using Semi-Supervised Manifold Alignment", Proceedings ISBI, pp599-603, 2018. (paper)
- J. R. Clough, D. R. Balfour, P. K. Marsden, C. Prieto, A. J. Reader, A. P. King, "MRI Slice Stacking Using Manifold Alignment and Wave Kernel Signatures", Proceedings ISBI, pp319-323, 2018. (paper)
- E. Puyol-Antón, B. Ruijsink, W. Bai, H. Langet, M. De Craene, J. A. Schnabel, P. Piro, A. P. King, M. Sinclair, "Fully Automated Myocardial Strain Estimation from Cine MRI Using Convolutional Neural Networks", Proceedings ISBI, pp1139-1143, 2018. (paper)
- I. Oksuz, B. Ruijsink, E. Puyol-Antón, M. Sinclair, D. Rueckert, J. A. Schnabel, A. P. King, "Automatic Left Ventricular Outflow Tract Classification for Accurate Cardiac MR Planning", Proceedings ISBI, pp462-465, 2018. (paper)
- 2 November 2017: New Medical Image Analysis paper on multi-scale analysis of cardiac cycle motion using machine learning: Sinclair et al, "Myocardial Strain Computed at Multiple Spatial Scales from Tagged Magnetic Resonance Imaging: Estimating Cardiac Biomarkers for CRT Patients"
- 28 Sept 2017: New MICCAI proceedings papers now available on-line:
- M. Sinclair, W. Bai, E. Puyol-Antón, O. Oktay, D. Rueckert, A. P. King, "Fully Automated Segmentation-Based Respiratory Motion Correction of Multiplanar Cardiac Magnetic Resonance Images for Large-Scale Datasets", Proceedings MICCAI, pp332-340, 2017. (paper)
- X. Chen, D. R. Balfour, P. K. Marsden, A. J. Reader, C. Prieto, A. P. King, "Efficient Deformable Motion Correction for 3-D Abdominal MRI Using Manifold Regression", Proceedings MICCAI, pp270-278, 2017. (paper)
- 30 June 2017: New textbook now published by Elsevier on MATLAB Programming for Biomedical Engineers and Scientists ...
- 19 June 2017: New Medical Image Analysis paper on multi-modal cardiac motion atlases: Puyol-Anton et al, "A Multimodal Spatiotemporal Cardiac Motion Atlas from MR and Ultrasound Data"
(Also see associated student materials and teaching pack)