Motion Modelling and Analysis Group

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 MMAG has worked extensively in the past 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 worked on applying such models in a range of applications, but predominantly in the context of image-guided cardiac interventions and PET-MR imaging.

Currently, the main research theme of the group is the analysis of motion with the aim of extracting clinically useful biomarkers. Here, work focuses 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. The group works on developing novel machine learning techniques to learn motion features that are useful for a range of clinical prediction tasks.

The group is also involved in developing machine learning solutions to other problems, such as radiotherapy and vocal tract imaging.

Latest Updates

• 16 November 2020: New journal paper published by Matthieu Ruthven et al - "Deep-learning-based segmentation of the vocal tract and articulators in real-time magnetic resonance images of speech", Computer Methods and Programs in Biomedicine, 2020. (paper)
• 8 September 2020: New MICCAI workshop papers now have Arxiv papers online:
  • Nick Byrne et al - "A Persistent Homology-based Topological Loss Function for Multi-class CNN Segmentation of Cardiac MRI", Proceedings MICCAI STACOM (Arxiv paper)
  • Bram Ruijsink et al, "Quality-aware Semi-supervised Learning for CMR Segmentation", Proceedings MICCAI STACOM (Arxiv paper)
  • Zhen Yuan et al, "Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients", Proceedings MICCAI ASMUS, 2020. (Arxiv paper)
• 7 September 2020: New journal paper by James Clough et al - "A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020. (paper)
• 24 August 2020: New journal paper by Esther Puyol-Antón - "Automated Quantification of Myocardial Tissue Characteristics From Native T1 Mapping Using Neural Networks With Uncertainty-based Quality-control", Journal of Cardiovascular Magnetic Resonance, 22:60, 2020. (open access paper)
• 15 July 2020: New TMI paper by Ilkay Oksuz: Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation - see project summary
• 25 June 2020: Accepted MICCAI 2020 paper by Esther Puyol-Antón now has a preprint available online: Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction
• 10 September 2019: Accepted MICCAI 2019 workshop papers now have preprints available online:
  • Esther Puyol-Antón et al (STACOM), Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders - see project summary.
  • Nick Byrne et al (PIPPI), Topology-preserving Augmentation for CNN-based Segmentation of Congenital Heart Defects from 3D Paediatric CMR - see project summary.
• 18 July 2019: New paper by Esther Puyol-Antón and Bram Ruijsink on Fully Automated, Quality-Controlled Cardiac Analysis From CMR - see project summary.

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  11 July 2019: New textbook available - Statistics for Biomedical Engineers and Scientists:
  • Student's companion site
  • Teacher's pack (registration required)
  • Author Q & A
• 18 June 2019: Accepted MICCAI 2019 papers now have preprints available online:
  • James Clough et al, Global and Local Interpretability for Cardiac MRI Classification - see project summary.
  • Ilkay Oksuz et al, Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-space - see project summary.
• 18 June 2019: Accepted MIDL 2019 papers available on OpenReview:
  • Solene Girardeau et al, Deep Learning for Magnetic Resonance Fingerprinting.
  • Ilkay Oksuz et al, High-quality Segmentation of Low Quality Cardiac MR Images Using K-space Artefact Correction.
• 3 May 2019: New Medical Image Analysis paper by Ilkay Oksuz on Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning - see project summary.
• 6 Mar 2019: James Clough's work on incorporating topological knowledge into deep learning based image segmentation has been accepted for oral presentation at IPMI 2019. Preprint available here - see project summary.
• 22 Feb 2019: Data download now available for MRI images used in Clough et al, IEEE T-PAMI 2019.
• 17 Jan 2019: New IEEE-TPAMI paper by James Clough: "Weighted Manifold Alignment using Wave Kernel Signatures for Aligning Medical image Datasets" - see project summary.