Division of Imaging Sciences and Biomedical Engineering, Kings College London, U.K.
Division of Imaging Sciences and Biomedical Engineering, Kings College London, U.K.
Med Image Anal. 2017 Aug;40:96-110. doi: 10.1016/j.media.2017.06.002. Epub 2017 Jun 13.
Cardiac motion atlases provide a space of reference in which the motions of a cohort of subjects can be directly compared. Motion atlases can be used to learn descriptors that are linked to different pathologies and which can subsequently be used for diagnosis. To date, all such atlases have been formed and applied using data from the same modality. In this work we propose a framework to build a multimodal cardiac motion atlas from 3D magnetic resonance (MR) and 3D ultrasound (US) data. Such an atlas will benefit from the complementary motion features derived from the two modalities, and furthermore, it could be applied in clinics to detect cardiovascular disease using US data alone. The processing pipeline for the formation of the multimodal motion atlas initially involves spatial and temporal normalisation of subjects' cardiac geometry and motion. This step was accomplished following a similar pipeline to that proposed for single modality atlas formation. The main novelty of this paper lies in the use of a multi-view algorithm to simultaneously reduce the dimensionality of both the MR and US derived motion data in order to find a common space between both modalities to model their variability. Three different dimensionality reduction algorithms were investigated: principal component analysis, canonical correlation analysis and partial least squares regression (PLS). A leave-one-out cross validation on a multimodal data set of 50 volunteers was employed to quantify the accuracy of the three algorithms. Results show that PLS resulted in the lowest errors, with a reconstruction error of less than 2.3 mm for MR-derived motion data, and less than 2.5 mm for US-derived motion data. In addition, 1000 subjects from the UK Biobank database were used to build a large scale monomodal data set for a systematic validation of the proposed algorithms. Our results demonstrate the feasibility of using US data alone to analyse cardiac function based on a multimodal motion atlas.
心脏运动图谱为参考空间，可在其中直接比较一组对象的运动。运动图谱可用于学习与不同病理相关的描述符，随后可用于诊断。迄今为止，所有此类图谱都是使用来自同一模态的数据形成和应用的。在这项工作中，我们提出了一个从 3D 磁共振（MR）和 3D 超声（US）数据构建多模态心脏运动图谱的框架。这种图谱将受益于两种模态衍生的互补运动特征，此外，它可以在诊所中使用 US 数据来检测心血管疾病。构建多模态运动图谱的处理管道最初涉及对受试者心脏几何形状和运动进行空间和时间归一化。此步骤是按照与单模态图谱形成类似的管道完成的。本文的主要新颖之处在于使用多视图算法同时降低 MR 和 US 衍生运动数据的维度，以找到两种模态之间的公共空间来模拟它们的变异性。研究了三种不同的降维算法：主成分分析、典型相关分析和偏最小二乘回归（PLS）。对 50 名志愿者的多模态数据集进行了留一交叉验证，以量化三种算法的准确性。结果表明，PLS 产生的误差最小，MR 衍生运动数据的重建误差小于 2.3mm，US 衍生运动数据的重建误差小于 2.5mm。此外，使用来自 UK Biobank 数据库的 1000 名受试者构建了一个大规模的单模态数据集，以对所提出的算法进行系统验证。我们的结果证明了仅使用 US 数据基于多模态运动图谱分析心脏功能的可行性。
