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

BANDA：通过成像和元数据评估心脏运动异常的贝叶斯深度图谱

• 批准号: EP/S012796/1
• 负责人: Ali Gooya
• 金额: $ 17.25万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FS012796%2F1
• 关键词: BIANDA; Bayesian; Deep; Atlases; Cardiac

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.

心血管疾病是英国第二大杀手，目前该国有超过700万人患有心血管疾病。及早识别有重大风险的个人对于提高患者的生活质量和减轻社会和医疗系统的经济负担至关重要。大量的心血管疾病导致心肌供血不足和运动异常，通过分析患者的动态心脏成像数据可以无创性地诊断。手动评估这些图像是主观的、不可重现的、仅限于左心室，且耗时。统计图谱描述了一大批健康人群的心脏运动的“平均”模式，可能有助于识别个体偏离正常状态的情况。然而，现有地图集与临床实践的整合受到三个关键限制的阻碍：(I)导出的运动统计数据通常与患者的年龄、性别、体重等无关(元数据)，这对于精确诊断是必不可少的；(Ii)这些地图集是非概率的，因此它们的临床可靠性严重受阻；(Iii)它们通常是使用少量数据集(n&lt；1000)得出的，限制了它们的统计能力。为了缓解这些关键的限制，这项建议的目标是，首次开发一个完整的概率图谱，通过整体整合来自大规模心脏成像研究的影像和元数据，准确地评估双室运动异常。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。这些网络提供了对顺序数据(如2D视频)建模的自然机制。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。运动将被建模为整个心脏周期中心脏形状的时空(3D+t)序列，从电影心脏磁共振(CMR)图像中提取。图谱将是一个递归模型，在给定序列的情况下，它将预测心脏下一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，地图集将能够非常准确地量化特定于患者年龄、性别、年龄、种族等的解剖和功能心脏异常(以及显示不确定性的差异)。PI在根据形状编制贝叶斯和非高斯统计地图集方面拥有丰富的经验。然而，以前的工作(I)不是为了分析运动数据而设计的，(Ii)丢弃了患者元数据(如年龄、性别、种族等)，以及(Iii)没有扩展到大人口。因此，该图谱不能在临床上用于研究与各种心血管疾病相关的心脏运动异常。通过将贝叶斯模型与深度神经网络相结合，这一建议将大大有别于PI以前的建议。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)，从而实现可伸缩性。该图谱将来自英国生物库CMR研究，目标是到2022年扫描10万名患者。随着来自英国生物库的数据集的新发布，将继续对地图集进行培训。PI已经为这项研究与临床顾问建立了合作关系，并拥有完全访问CMR数据集的权限。这对该提议的成功至关重要，因为深度神经网络的训练需要获得大量的数据集，这是最近才出现的一种可能性。在这方面，BIANDA是及时和有希望的。

项目成果

A probabilistic deep motion model for unsupervised cardiac shape anomaly assessment.

用于无监督心脏形状异常评估的概率深度运动模型。

• DOI: 10.1016/j.media.2021.102276
• 发表时间: 2022
• 期刊: Medical image analysis
• 影响因子: 10.9
• 作者: Zakeri A