Cardiac function in a large animal model of myocardial infarction at 7 T: deep learning based automatic segmentation increases reproducibility.

Cardiac magnetic resonance (CMR) imaging allows precise non-invasive quantification of cardiac function. It requires reliable image segmentation for myocardial tissue. Clinically used software usually offers automatic approaches for this step. These are, however, designed for segmentation of human images obtained at clinical field strengths. They reach their limits when applied to preclinical data and ultrahigh field strength (such as CMR of pigs at 7 T). In our study, eleven animals (seven with myocardial infarction) underwent four CMR scans each. Short-axis cine stacks were acquired and used for functional cardiac analysis. End-systolic and end-diastolic images were labelled manually by two observers and inter- and intra-observer variability were assessed. Aiming to make the functional analysis faster and more reproducible, an established deep learning (DL) model for myocardial segmentation in humans was re-trained using our preclinical 7 T data (n = 772 images and labels). We then tested the model on n = 288 images. Excellent agreement in parameters of cardiac function was found between manual and DL segmentation: For ejection fraction (EF) we achieved a Pearson's r of 0.95, an Intraclass correlation coefficient (ICC) of 0.97, and a Coefficient of variability (CoV) of 6.6%. Dice scores were 0.88 for the left ventricle and 0.84 for the myocardium.

Ultra-high field cardiac MRI in large animals and humans for translational cardiovascular research.

A key step in translational cardiovascular research is the use of large animal models to better understand normal and abnormal physiology, to test drugs or interventions, or to perform studies which would be considered unethical in human subjects. Ultrahigh field magnetic resonance imaging (UHF-MRI) at 7 T field strength is becoming increasingly available for imaging of the heart and, when compared to clinically established field strengths, promises better image quality and image information content, more precise functional analysis, potentially new image contrasts, and as all in-vivo imaging techniques, a reduction of the number of animals per study because of the possibility to scan every animal repeatedly. We present here a solution to the dual use problem of whole-body UHF-MRI systems, which are typically installed in clinical environments, to both UHF-MRI in large animals and humans. Moreover, we provide evidence that in such a research infrastructure UHF-MRI, and ideally combined with a standard small-bore UHF-MRI system, can contribute to a variety of spatial scales in translational cardiovascular research: from cardiac organoids, Zebra fish and rodent hearts to large animal models such as pigs and humans. We present pilot data from serial CINE, late gadolinium enhancement, and susceptibility weighted UHF-MRI in a myocardial infarction model over eight weeks. In 14 pigs which were delivered from a breeding facility in a national SARS-CoV-2 hotspot, we found no infection in the incoming pigs. Human scanning using CINE and phase contrast flow measurements provided good image quality of the left and right ventricle. Agreement of functional analysis between CINE and phase contrast MRI was excellent. MRI in arrested hearts or excised vascular tissue for MRI-based histologic imaging, structural imaging of myofiber and vascular smooth muscle cell architecture using high-resolution diffusion tensor imaging, and UHF-MRI for monitoring free radicals as a surrogate for MRI of reactive oxygen species in studies of oxidative stress are demonstrated. We conclude that UHF-MRI has the potential to become an important precision imaging modality in translational cardiovascular research.