Automatic 3D+t four-chamber CMR quantification of the UK biobank: integrating imaging and non-imaging data priors at scale
Automatic 3D+t four-chamber CMR quantification of the UK biobank: integrating imaging and non-imaging data priors at scale
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英国生物样本库的自动 3D t 四室 CMR 量化：大规模整合成像和非成像数据先验
DOI：
10.1016/j.media.2022.102498
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发表时间：
2022
影响因子：
10.9
通讯作者：
Alejandro F Frangi
中科院分区：
文献类型：
--
作者：
Yan Xia;Xiang Chen;N. Ravikumar;C. Kelly;R. Attar;N. Aung;S. Neubauer;S. Petersen;Alejandro F Frangi
心腔的精确3D建模对于心脏体积和功能的临床评估（包括结构和运动分析）至关重要。此外，为了研究大群体内心脏形态与其他患者信息之间的相关性，有必要自动生成群体内每个受试者的心脏网格模型。在这项研究中，我们引入了MCSI-Net（多线索形状推理网络），其中我们将统计形状模型嵌入卷积神经网络中，并利用队列中的表型和人口统计信息来推断所有四个心腔的3D重建。通过这种方式，我们利用网络的能力来学习电影心脏磁共振（CMR）图像中心腔的外观，并通过使用形状先验约束预测来生成合理的3D心脏形状，该形状先验以从人群子集先验学习的形状变化的统计模式的形式。这反过来又使网络能够推广到整个人群的样本。据我们所知，这是第一个使用这种方法来生成患者特定心脏形状的工作。MCSI-Net能够仅使用一小部分（约23%至46%）可用图像数据生成精确的3D形状，这对社区非常重要，因为它支持CMR扫描采集的加速。来自英国生物库的心脏MR图像被用于训练和验证所提出的方法。我们还介绍了在50个时间框架内分析英国生物银行40，000名受试者的结果，总计200万张图像。我们的模型可以生成更多的全球一致的心脏形状比手动注释在存在切片间运动，并显示出强烈的协议与心脏结构和功能的参考范围跨心室和心房。
Accurate 3D modelling of cardiac chambers is essential for clinical assessment of cardiac volume and function, including structural, and motion analysis. Furthermore, to study the correlation between cardiac morphology and other patient information within a large population, it is necessary to automatically generate cardiac mesh models of each subject within the population. In this study, we introduce MCSI-Net (Multi-Cue Shape Inference Network), where we embed a statistical shape model inside a convolutional neural network and leverage both phenotypic and demographic information from the cohort to infer subject-specific reconstructions of all four cardiac chambers in 3D. In this way, we leverage the ability of the network to learn the appearance of cardiac chambers in cine cardiac magnetic resonance (CMR) images, and generate plausible 3D cardiac shapes, by constraining the prediction using a shape prior, in the form of the statistical modes of shape variation learned a priori from a subset of the population. This, in turn, enables the network to generalise to samples across the entire population. To the best of our knowledge, this is the first work that uses such an approach for patient-specific cardiac shape generation. MCSI-Net is capable of producing accurate 3D shapes using just a fraction (about 23% to 46%) of the available image data, which is of significant importance to the community as it supports the acceleration of CMR scan acquisitions. Cardiac MR images from the UK Biobank were used to train and validate the proposed method. We also present the results from analysing 40,000 subjects of the UK Biobank at 50 time-frames, totalling two million image volumes. Our model can generate more globally consistent heart shape than that of manual annotations in the presence of inter-slice motion and shows strong agreement with the reference ranges for cardiac structure and function across cardiac ventricles and atria.
MULTI-X，最先进的基于云的生物医学研究生态系统
DOI：
10.1109/bibm.2018.8621317
发表时间：
2018
期刊：
--
影响因子：
--
作者：
De Vila M
通讯作者：
De Vila M