Computational models of the human body are commonly used for injury prediction in automobile safety research. To create these models, the geometry of the human body is typically obtained from segmentation of medical images such as computed tomography (CT) images that have a resolution between 0.2 and 1 mm/pixel. While the accuracy of the geometrical and structural information obtained from these images depend greatly on their resolution, the effect of image resolution on the estimation of the ribs geometrical properties has yet to be established. To do so, each of the thirty-four sections of ribs obtained from a Post Mortem Human Surrogate (PMHS) was imaged using three different CT modalities:​ standard clinical CT (clinCT), high resolution clinical CT (HRclinCT), and microCT. The images were processed to estimate the rib cross-section geometry and mechanical properties, and the results were compared to those obtained from the microCT images by computing the ‘deviation factor’, a metric that quantifies the relative difference between results obtained from clinCT and HRclinCT to those obtained from microCT. Overall, clinCT images gave a deviation greater than 100%, and were therefore deemed inadequate for the purpose of this study. HRclinCT overestimated the rib cross-sectional area by 7.6%, the moments of inertia by about 50%, and the cortical shell area by 40.2%, while underestimating the trabecular area by 14.7%. Next, a parametric analysis was performed to quantify how the variations in the estimate of the geometrical properties affected the rib predicted mechanical response under antero-posterior loading. A variation of up to 45% for the predicted peak force and up to 50% for the predicted stiffness was observed. These results provide a quantitative estimate of the sensitivity of the response of the FE model to the resolution of the images used to generate it. They also suggest that a correction factor could be derived from the comparison between microCT and HRclinCT images to improve the response of the model developed based on HRclinCT images.