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

心血管疾病（CVD）是英国的第二大杀手，目前，英国有超过700万人患有心血管疾病。早期识别具有重大风险的个体对于改善患者的生活质量和减轻社会和医疗保健系统的经济负担至关重要。大量的cvd导致心肌供血不足，运动异常，通过分析患者动态心脏影像数据可以无创诊断。人工评估这些图像是主观的，不可重复的，仅限于左心室，耗时。描述大量健康人群心脏运动的“平均”模式的统计地图集可能对识别个体偏离正常状态的偏差有用。然而，现有地图集与临床实践的整合受到三个关键限制的抑制：(i)导出的运动统计数据通常独立于患者的年龄、性别、体重等（元数据），而这些数据对于精确诊断至关重要；（ii）由于是非概率的，这些地图集无法提供提取的运动异常的确定性，因此其临床可靠性受到严重阻碍；（iii）它们通常使用少量数据集（n<1000）导出，限制了它们的统计能力。为了减轻这些关键的限制，本提案旨在首次开发一个完整的概率图谱，通过整体整合来自大型人群心脏成像研究的成像和元数据来准确评估双室运动异常。BIANDA将是一种新的贝叶斯方法，扩展了深度递归神经网络（rnn）的最新发展。这些网络提供了一种自然的机制来对序列数据（如2D视频）进行建模。然而，使用rnn来模拟心脏运动的复杂动力学在概念上是新的，而且显然是强大的。该运动将被建模为从电影心脏磁共振（CMR）图像中提取的心脏形状在整个心脏周期的时空（3D+t）序列。图谱将是一个循环模型，给定一个序列，它将预测心脏下一步状态的概率分布函数（pdf）。更重要的是，pdf将以患者的元数据为条件。因此，通过测量每个阶段与预期形状的空间偏差，图谱将允许非常准确地量化患者年龄、性别、年龄、种族等特定的解剖和功能心脏异常（以及显示不确定性的差异）。PI在从形状开发贝叶斯和非高斯统计地图集方面有丰富的经验。然而，先前的工作(i)没有设计用于分析运动数据，（ii）丢弃了患者元数据（如年龄、性别、种族等），以及（iii）没有扩展到大人群。因此，该图谱不能用于临床研究与各种心血管疾病相关的心脏运动异常。该提案将通过将贝叶斯模型与深度神经网络相结合，大大不同于PI之前的提议。前者需要处理不确定性；后者将显著提高预测和计算效率（使用gpu），从而提高可扩展性。该图谱将来自英国生物银行的CMR研究，目标是到2022年扫描100万名患者。随着英国生物银行新发布的数据集可用，将继续进行地图集的培训。PI已经与该研究的临床顾问建立了合作关系，并且可以完全访问CMR数据集。这对于该提议的成功至关重要，因为深度神经网络的训练需要访问大量的数据集，这是最近才出现的可能性。在这方面，BIANDA是及时和有希望的。

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.