Plaque to Myocardium Ratio (PMR) based on T1-weighted MRI is an important quantitative biomarker to classify high-risk coronary plaque. However, it is calculated based on relative signal intensities, therefore, sensitive to variations in physiological and acquisition conditions. In this work, Bloch simulations were performed to explore the impact of heart rate, echo spacing, flip angle, and the number of readout segments on PMR. A computational model was further proposed to effectively reduce the acquisition-related PMR variations demonstrated in simulations and in vivo studies. The proposed method may potentially improve the precision of PMR and facilitate its comparison in longitudinal studies.