Murine cardiac fibrosis localization using adaptive Bayesian cardiac strain imaging in vivo

Abstract

Abstract An adaptive Bayesian regularized cardiac strain imaging (ABR-CSI) algorithm for in vivo murine myocardial function assessment is presented. We report on 31 BALB/CJ mice (n = 17 females, n = 14 males), randomly stratified into three surgical groups: myocardial infarction (MI, n = 10), ischemia–reperfusion (IR, n = 13) and control (sham, n = 8) imaged pre-surgery (baseline- BL), and 1, 2, 7 and 14 days post-surgery using a high frequency ultrasound imaging system (Vevo 2100). End-systole (ES) radial and longitudinal strain images were used to generate cardiac fibrosis maps using binary thresholding. Percentage fibrotic myocardium (PFM) computed from regional fibrosis maps demonstrated statistically significant differences post-surgery in scar regions. For example, the MI group had significantly higher PFMRadial (%) values in the anterior mid region (p = 0.006) at Day 14 (n = 8, 42.30 ± 14.57) compared to BL (n = 12, 1.32 ± 0.85). A random forest classifier automatically detected fibrotic regions from ground truth Masson’s trichrome stained histopathology whole slide images. Both PFMRadial (r = 0.70) and PFMLongitudinal (r = 0.60) results demonstrated strong, positive correlation with PFMHistopathology (p < 0.001).