We present a framework for combining a cardiac motion atlas with non-motion data. The atlas represents cardiac cycle motion across a number of subjects in a common space based on rich motion descriptors capturing 3D displacement, velocity, strain and strain rate. The non-motion data are derived from a variety of sources such as imaging, electrocardiogram (ECG) and clinical reports. Once in the atlas space, we apply a novel supervised learning approach based on random projections and ensemble learning to learn the relationship between the atlas data and some desired clinical output. We apply our framework to the problem of predicting response to Cardiac Resynchronisation Therapy (CRT). Using a cohort of 34 patients selected for CRT using conventional criteria, results show that the combination of motion and non-motion data enables CRT response to be predicted with 91.2% accuracy (100% sensitivity and 62.5% specificity), which compares favourably with the current state-of-the-art in CRT response prediction.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    A Framework for Combining a Motion Atlas with Non-Motion Information to Learn Clinically Useful Biomarkers:Application to Cardiac Resynchronisation Therapy Response Prediction



    Erscheinungsdatum :

    01.01.2017


    Anmerkungen:

    Peressutti , D , Sinclair , M D M , Bai , W , Jackson , T A , Ruijsink , J , Nordsletten , D , Asner , L , Hadjicharalambous , M , Rinaldi , C A , Rueckert , D & King , A P 2017 , ' 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 , vol. 35 , pp. 669–684 . https://doi.org/10.1016/j.media.2016.10.002



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    DDC:    629



    Echocardiography and resynchronisation in 2007

    Lafitte, S. | British Library Conference Proceedings | 2007


    MOTION PREDICTION DEVICE, MOTION PREDICTION METHOD, AND MOTION PREDICTION PROGRAM

    OKAYAMA KEN | Europäisches Patentamt | 2022

    Freier Zugriff

    Enhancing Motion Prediction by a Cooperative Framework

    Araluce, Javier / Justo, Alberto / Arizala, Asier et al. | IEEE | 2024



    MOTION PREDICTION DEVICE AND MOTION PREDICTION METHOD

    KAWANISHI RAY / SAKAI KATSUHIRO / SUGAIWA TAISUKE et al. | Europäisches Patentamt | 2024

    Freier Zugriff