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Motion Modelling and Analysis Group

School of Biomedical Engineering and Imaging Sciences,

King's College London




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

  • 8 September 2022: New MICCAI workshop papers now have Arxiv papers online:
    • G. Morilhat, N. Kifle, S. FinesilverSmith, B. Ruijsink, V. Vergani, Habtamu T. D., Zerubabel T. D., E. Puyol-Antón, A. Carass, A. P. King, "Deep Learning-based Segmentation of Pleural Effusion From Ultrasound Using Coordinate Convolutions", Proceedings MICCAI FAIR, 2022. (Arxiv paper)
    • S. Ioannou, H. Chockler, A. Hammers, A. P. King, "A Study of Demographic Bias in CNN-based Brain MR Segmentation", Proceedings MICCAI MLCN, 2022. (Arxiv paper)
    • L. Humbert-Vidan, V. Patel, R. Andlauer, A. P King, T. G. Urbano, "Prediction of Mandibular ORN Incidence From 3D Radiation Dose Distribution Maps Using Deep Learning", Proceedings MICCAI AMAI, 2022. (Arxiv paper)
    • T. Lee, E. Puyol-Antón, B. Ruijsink, M. Shi, A. P. King, "A Systematic Study of Race and Sex Bias in CNN-based Cardiac MR Segmentation", Proceedings MICCAI STACOM, 2022. (Arxiv paper)
    • E. Puyol-Antón, B. Ruijsink, B. S. Sidhu, J. Gould, B. Porter, M. K. Elliott, V. Mehta, H. Gu, C. A. Rinaldi, M. Cowie, P. Chowienczyk, R. Razavi, A. P. King, "AI-enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography", Proceedings MICCAI ASMUS, 2022. (Arxiv paper)
  • 2 September 2022: New journal paper by N. Byrne, et al, "A Persistent Homology-based Topological Loss for CNN-based Multi-class Segmentation of CMR", IEEE Transactions on Medical Imaging, 2022. (paper)
  • 27 April 2022: New journal paper by E. Puyol-Antón, et al, "A Multimodal Deep Learning Model for Cardiac Resynchronisation Therapy Response Prediction", Medical Image Analysis, 2022. (open access paper)
  • 25 April 2022: New journal paper by Z. Yuan, et al - "Deep Learning-based Quality-controlled Spleen Assessment From Ultrasound Images", Biomedical Signal Processing and Control, 2022. (paper)
  • 13 April 2022: New journal paper by E. Puyol-Antón, et al - "Fairness in Cardiac Magnetic Resonance Imaging: Assessing Sex and Racial Bias in Deep Learning-Based Segmentation", Frontiers in Cardiovascular Medicine, 2022. (open access paper). Also, check out the press release here!
  • 12 January 2022: We have a new Blog section on the web site! Click on the Blog link in the menu bar to take you to the MMAG blog. The first post is by Andrew King who discusses the issue of fair AI in medical imaging. Check it out ...