Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland; Laboratoire de Mécanique des Solides (LMS), École Polytechnique/C.N.R.S./Institut Polytechnique de Paris, Palaiseau, France; MΞDISIM team, Inria, Palaiseau, France.
Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.
Med Image Anal. 2022 Nov;82:102598. doi: 10.1016/j.media.2022.102598. Epub 2022 Aug 24.
Cardiovascular magnetic resonance (CMR) imaging is the gold standard for the non-invasive assessment of left-ventricular (LV) function. Prognostic value of deformation metrics extracted directly from regular SSFP CMR images has been shown by numerous studies in the clinical setting, but with some limitations to detect torsion of the myocardium. Tagged CMR introduces trackable features in the myocardium that allow for the assessment of local myocardial deformation, including torsion; it is, however, limited in the quantification of radial strain, which is a decisive metric for assessing the contractility of the heart. In order to improve SSFP-only and tagged-only approaches, we propose to combine the advantages of both image types by fusing global shape motion obtained from SSFP images with the local deformation obtained from tagged images. To this end, tracking is first performed on SSFP images, and subsequently, the resulting motion is utilized to mask and track tagged data. Our implementation is based on a recent finite element-based motion tracking tool with mechanical regularization. Joint SSFP and tagged images registration performance is assessed based on deformation metrics including LV strain and twist using human and in-house porcine datasets. Results show that joint analysis of SSFP and 3DTAG images provides better quantification of LV strain and twist as either data source alone.
心血管磁共振（CMR）成像是评估左心室（LV）功能的非侵入性金标准。大量临床研究表明，直接从常规稳态自由进动（SSFP）CMR 图像中提取的应变指标具有预后价值，但存在一些局限性，无法检测心肌扭转。标记 CMR 在心肌中引入了可跟踪的特征，可用于评估局部心肌变形，包括扭转；然而，它在径向应变的定量方面存在局限性，而径向应变是评估心脏收缩力的决定性指标。为了改进仅 SSFP 和仅标记的方法，我们建议通过融合从 SSFP 图像获得的全局形状运动与从标记图像获得的局部变形来结合这两种图像类型的优势。为此，首先在 SSFP 图像上进行跟踪，然后利用所得运动来掩蔽和跟踪标记数据。我们的实现基于基于最近的基于有限元的运动跟踪工具，具有机械正则化。基于 LV 应变和扭转等变形指标，使用人体和内部猪数据集评估联合 SSFP 和 3DTAG 图像的配准性能。结果表明，与仅使用任一数据源相比，联合分析 SSFP 和 3DTAG 图像可更好地量化 LV 应变和扭转。
