In radiofrequency ablation (RFA), successful therapy requires accurate, image-guided placement of the ablation device in a location selected by a predictive treatment plan. Current planning methods rely on geometric models of ablations that are not sensitive to underlying physical processes in RFA. Implementing plans based on computational models of RFA with image-guided techniques, however, has not been well characterized. To study the use of computational models of RFA in planning needle placement, this work compared ablations performed with an optically tracked RFA device with corresponding models of the ablations. The calibration of the tracked device allowed the positions of distal features of the device, particularly the tips of the needle electrodes, to be determined to within 1.4 +/- 0.6 mm of uncertainty. Ablations were then performed using the tracked device in a phantom system based on an agarose-albumin mixture. Images of the sliced phantom obtained from the ablation experiments were then compared with the predictions of a bioheat transfer model of RFA, which used the positional data of the tracked device obtained during ablation. The model was demonstrated to predict 90% of imaged pixels classified as being ablated. The discrepancies between model predictions and observations were analyzed and attributed to needle tracking inaccuracy as well as to uncertainties in model parameters. The results suggest the feasibility of using finite element modeling to plan ablations with predictable outcomes when implemented using tracked RFA.