Purpose Safe, sensitive, and non-invasive imaging methods to assess the presence, extent, and turnover of myocardial fibrosis are needed for early stratification of risk in patients who might develop heart failure after myocardial infarction. We describe a non-contrast cardiac magnetic resonance (CMR) approach for sensitive detection of myocardial fibrosis using a canine model of myocardial infarction and reperfusion. Methods Seven dogs had coronary thrombotic occlusion of the left anterior descending coronary arteries followed by fibrinolytic reperfusion. CMR studies were performed at 7 days after reperfusion. A CMR spin-locking T1ρ mapping sequence was used to acquire T1ρ dispersion data with spin-lock frequencies of 0 and 511 Hz. A fibrosis index map was derived on a pixel-by-pixel basis. CMR native T1 mapping, first-pass myocardial perfusion imaging, and post-contrast late gadolinium enhancement imaging were also performed for assessing myocardial ischemia and fibrosis. Hearts were dissected after CMR for histopathological staining and two myocardial tissue segments from the septal regions of adjacent left ventricular slices were qualitatively assessed to grade the extent of myocardial fibrosis. Results Histopathology of 14 myocardial tissue segments from septal regions was graded as grade 1 (fibrosis area, < 20% of a low power field, n = 9), grade 2 (fibrosis area, 20–50% of field, n = 4), or grade 3 (fibrosis area, > 50% of field, n = 1). A dramatic difference in fibrosis index (183%, P < 0.001) was observed by CMR from grade 1 to 2, whereas differences were much smaller for T1ρ (9%, P = 0.14), native T1 (5.5%, P = 0.12), and perfusion (− 21%, P = 0.05). Conclusion A non-contrast CMR index based on T1ρ dispersion contrast was shown in preliminary studies to detect and correlate with the extent of myocardial fibrosis identified histopathologically. A non-contrast approach may have important implications for managing cardiac patients with heart failure, particularly in the presence of impaired renal function. © 2017 Elsevier Inc.