Scientific ReportsBioengineeringPeer-reviewed journal article
Complete spatiotemporal quantification of cardiac motion in mice through enhanced acquisition and super-resolution reconstruction
Tanmay MukherjeeiD, Maziyar KeshavarzianiD, Elizabeth M. FugateiD, Vahid NaeiniiD, Amr DarwishiD, Jacques Ohayon AiD, Kyle J. MyersiD, Dipan J. ShahiD, Diana M. LindquistiD, Sakthivel SadayappaniD, Roderic Ivan PettigrewiD, Reza AvazmohammadiiD
Abstract
The quantification of cardiac motion using cardiac magnetic resonance imaging (CMR) has shown promise as an early-stage marker for cardiovascular diseases. Despite the growing popularity of CMR-based myocardial strain calculations, measures of complete spatiotemporal strains (i.e., three-dimensional strains over the cardiac cycle) remain elusive. Complete spatiotemporal strain calculations are primarily hampered by poor spatial resolution, with the rapid motion of the cardiac wall also challenging the reproducibility of such strains. We hypothesize that a super-resolution reconstruction (SRR) framework that leverages combined image acquisitions at multiple orientations will enhance the reproducibility of complete spatiotemporal strain estimation. Two sets of CMR acquisitions were obtained for five wild-type mice, combining short-axis scans with radial and orthogonal long-axis scans. Super-resolution reconstruction, integrated with tissue classification, was performed to generate full four-dimensional (4D) images. The resulting enhanced and full 4D images enabled complete quantification of the motion in terms of 4D myocardial strains. Additionally, the effects of SRR in improving accurate strain measurements were evaluated using an in-silico heart phantom. The SRR framework revealed near isotropic spatial resolution, high structural similarity, and minimal loss of contrast, which led to overall improvements in strain accuracy. In essence, a comprehensive methodology was generated to quantify complete and reproducible myocardial deformation, aiding in the much-needed standardization of complete spatiotemporal strain calculations.
- organism: mouse
- assay: imaging
- research focus: clustering
- research focus: dimensionality reduction
- approach: deep learning
- Citations
- 10
- FWCI
- 3.58
- Field percentile
- top 6%
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