Complete spatiotemporal quantification of cardiac motion in mice through enhanced acquisition and super-resolution reconstruction.
Tanmay MukherjeeMaziyar KeshavarzianElizabeth M FugateVahid NaeiniAmr DarwishJacques OhayonKyle J MyersDipan J ShahDiana LindquistSakthivel SadayappanRoderic I PettigrewReza AvazmohammadiPublished in: bioRxiv : the preprint server for biology (2024)
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.
Keyphrases
- left ventricular
- escherichia coli
- deep learning
- early stage
- magnetic resonance imaging
- wild type
- computed tomography
- density functional theory
- molecular dynamics
- cardiovascular disease
- heart failure
- molecular dynamics simulations
- contrast enhanced
- high speed
- type diabetes
- convolutional neural network
- optical coherence tomography
- magnetic resonance
- monte carlo
- single cell
- radiation therapy
- molecular docking
- atrial fibrillation
- artificial intelligence
- high resolution
- single molecule
- insulin resistance
- high fat diet induced
- cardiovascular risk factors