title: A framework for combining a motion atlas with non-motion information to learn clinically useful biomarkers: Application to cardiac resynchronisation therapy response prediction.
abstract: 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.
authors: Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom. Electronic address: devis.1.peressutti@kcl.ac.uk.
authors: Devis
authors: D
authors: Peressutti
authors: Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.
authors: Matthew
authors: M
authors: Sinclair
authors: Biomedical Image Analysis Group, Imperial College London, London, United Kingdom.
authors: Wenjia
authors: W
authors: Bai
authors: Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.
authors: Thomas
authors: T
authors: Jackson
authors: Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.
authors: Jacobus
authors: J
authors: Ruijsink
authors: Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.
authors: David
authors: D
authors: Nordsletten
authors: Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.
authors: Liya
authors: L
authors: Asner
authors: Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.
authors: Myrianthi
authors: M
authors: Hadjicharalambous
authors: Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.
authors: Christopher A
authors: CA
authors: Rinaldi
authors: Biomedical Image Analysis Group, Imperial College London, London, United Kingdom.
authors: Daniel
authors: D
authors: Rueckert
authors: Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom. Electronic address: andrew.king@kcl.ac.uk.
authors: Andrew P
authors: AP
authors: King
mesh_terms: D015415:Biomarkers; D058406:Cardiac Resynchronization Therapy; D004562:Electrocardiography; D006333:Heart Failure; D006801:Humans; D000069550:Machine Learning; D009038:Motion
chemical_list: D015415:Biomarkers
keywords: Cardiac resynchronisation therapy; Multiple kernel learning; Random projections; Spatio-temporal atlas