BIANDA: Bayesian Deep Atlases for Cardiac Motion Abnormality Assessment from Imaging and Metadata

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Cardiovascular diseases (CVDs) is the second biggest killer in the UK and currently, more than 7 million people are living with CVD in the country. Early identification of individuals with significant risk is critical to improve the patient quality of life and reduce the financial burden on the social and healthcare systems. A large number of CVDs lead to the shortage of blood supply to the heart muscle and abnormal motion, which can be diagnosed non-invasively by analysing the patient's dynamic cardiac imaging data. Manual assessment of these images is subjective, non-reproducible, limited to the left ventricle, and time-consuming. Statistical atlases, describing the 'average' pattern of the heart motion over a large healthy population, can be potentially useful to identify deviations from normality in individuals. However, the integration of the existing atlases into clinical practice is inhibited by three key limitations: (i) the derived motion statistics are often independent of the patient's age, gender, weight, etc. (metadata) that are essential for precise diagnosis, (ii) Being non-probabilistic, these atlases fail to provide a measure of certainty in the extracted motion abnormalities thus their clinical reliability is seriously hampered, (iii) they are often derived using a small number of data sets (n<1000), limiting their statistical power. To alleviate these key limitations, this proposal aims, for the first time, to develop a full probabilistic atlas to accurately evaluate bi-ventricular motion abnormalities by holistically integrating imaging and metadata from a large population cardiac imaging study. BIANDA will be a novel Bayesian approach extending the recent developments in deep recurrent neural networks (RNNs). These networks provide a natural mechanism to model sequential data such as 2D video. Yet, using RNNs to model the complex dynamics of the heart motion is conceptually new and evidently powerful. The motion will be modelled as the spatiotemporal (3D+t) sequence of the heart shapes across the full cardiac cycle, extracted from cine Cardiac Magnetic Resonance (CMR) images. The atlas will be a recurrent model that, given a sequence, it will predict a probabilistic distribution function (pdf) for the next status of the heart. More importantly, the pdf will be conditioned on the patient's metadata. Thus by measuring the spatial deviations from the expected shape at each phase, the atlas will allow very accurate quantification of anatomical and functional cardiac abnormalities (and variances showing uncertainties) specific to the patient's age, gender, age, ethnicity, etc. The PI has an extensive experience in developing Bayesian and non-Gaussian statistical atlases from shapes. However, the previous work (i) was not designed to analyse motion data, (ii) discarded the patient metadata (such as age, gender, ethnicity, etc.), and (iii) did not scale into large populations. Therefore, the atlas was not clinically deployable to study cardiac motion abnormalities, which are relevant to various CVDs. This proposal will significantly depart from the PI's previous by combining Bayesian models with deep neural networks. The former is required to handle uncertainties; the latter will significantly boost the prediction and computational efficiency (using GPUs), thus scalability. The atlas will be derived from the UK Biobank CMR study aiming to scan n>100,000 patients by 2022. The training of the atlas will be pursued as the new releases of the data sets from the UK Biobank becomes available. The PI has established collaboration with the clinical advisor for this study and has full access to the CMR data sets. This is essential for the success of the proposal as the training of deep neural networks requires access to an ample of data sets, a possibility which has emerged only recently. In this regard, BIANDA is timely and promising.