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
- 6 Mar 2019: James Clough's work on incorporating toplogical knowledge into deep learning based image segmentation has been accepted for oral presentation at IPMI 2019. Preprint available here.
- 22 Feb 2019: Data download now available for MRI images used in Clough et al, IEEE T-PAMI 2019
- 17 Jan 2019: New PAMI paper by James Clough: "Weighted Manifold Alignment using Wave Kernel Signatures for Aligning Medical image Datasets".
- 03 Oct 2018: MICCAI MLMIR workshop paper by Ilkay Oksuz on correcting for MR motion artefacts during deep learning based reconstruction: "Cardiac MR Motion Artefact Correction from K-space Using Deep Learning-Based Reconstruction"
- 27 Sept 2018: Congratulations to Esther Puyol-Antón on passing her PhD viva! And thanks to the examiners Prof Wiro Niessen and Dr Maxime Sermesant.
- 24 Sept 2018: New IEEE Transactions on Biomedical Engineering paper on multiview learning by Esther Puyol-Antón: "Regional Multi-view Learning for Cardiac Motion Analysis: Application to Identification of Dilated Cardiomyopathy Patients"
- 22 Aug 2018: MICCAI paper by Ilkay Oksuz - now available online: "Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection"
- 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"
- 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)