Objective: To evaluate if color-coded parametric images could help subjective visual analysis and improve interobserver agreement in the evaluation of segmental contraction (SC) in CMR. Background: Routine evaluation of SC in CMR remains mostly based on visual analysis of cine loops and subsequent interobserver variability remains a potential drawback. Materials and methods: Three short axis cine loops were obtained in 33 subjects (18 myocardial infarction, 15 control), and 528 segments were analyzed. From each cine loop a single static parametric image resuming wall motion information was generated using Factor Analysis of Medical Image Sequences. Three readers (R1, R2, R3) scored left ventricular SC in 4 classes in 2 steps: visual assessment of cine loops alone and by combining cine loops with the corresponding parametric image. Reference segmental scores were obtained by consensus. Global contraction indexes were calculated in each step of the analysis. Results: When parametric images were combined with cine loop assessment, interobserver agreement was enhanced for paired readers: R1-R2: kappa = 0.66 (combined analysis) vs. kappa = 0.60 (cine alone); R2-R3: kappa = 0.67 vs. kappa = 0.65; R3-R1: kappa = 0.71 vs. kappa = 0.67 and absolute agreement with consensus was higher for the 3 readers: R1: 91% vs. 85%; R2: 87% vs. 83% and R3: 94% vs. 89%. When considering global wall motion indexes, interobserver agreement was also enhanced: R1 vs. R2 : r = 0.91 vs. 0.85; R2 vs. R3: 0.95 vs. 0.91; R3 vs R1: 0.98 vs. 0.91. Conclusion: Adding a color-coded static parametric image to routine subjective visual assessment of SC reduces interobserver variability.