Automated cardiac MRI analysis for robust profiling of heart failure models in mice

Heart failure with preserved ejection fraction (HFpEF) is a complex, age-related cardiovascular disease with limited treatment options, partly due to a poor understanding of underlying mechanisms, lack of robust preclinical models and diagnostic tools with limited specificity. Traditional cardiac magnetic resonance imaging (MRI) protocols and analysis in preclinical research are time-consuming, and manual analysis methods are prone to high inter-observer variability (correlation coefficient of 0.79 for left ventricular (LV) ejection fraction between observers). To accelerate and standardize phenotyping, we optimized a comprehensive non-contrast cardiac MRI protocol for high throughput, enabling acquisition of a stack of 12 short-axis slices in approximately seven minutes. This time-efficiency allowed us to add additional sequences, including cine-Arterial Spin Labeling (ASL) for myocardial perfusion mapping and dobutamine stress testing, allowing for a comprehensive cardiac exam. We developed a deep learning approach utilizing 3D Convolutional Neural Networks (CNNs) for fully automated segmentation and quantification of cardiac function. We validated this comprehensive pipeline in two multifactorial mouse models of HFpEF, combining diet-induced obesity (DIO) or high fat diet (HFD) and the hypertensive agent, deoxycorticosterone pivalate (DOCP). Our approach illustrated high technical sensitivity by detecting significant myocardial perfusion reduction in both the DIO (p = 0.02) and DIO + DOCP (p = 0.03) groups compared to control, along with subtle diastolic abnormalities, even in the absence of overt changes in ejection fraction. The CNN demonstrated high accuracy and reproducibility, achieving a mean Dice similarity coefficient greater than 0.9 for segmentation and Intraclass Correlation Coefficients (ICC) exceeding 0.95 for key left ventricular functional parameters (volumes and mass) compared to expert consensus reads. This optimized protocol and automated analysis pipeline provides a valuable tool for preclinical cardiovascular research, enabling efficient and reliable assessment of cardiac remodeling and contributing to a deeper understanding of HFpEF progression.