The Recurrent Inference Machine (RIM) has been developed as an alternative to the clinically used Compressed Sensing (CS) algorithm, using Deep Learning (DL). A common issue with DL networks is the generalization of the network to features that have not been trained for. In this study we evaluate the robustness of the RIM to white matter lesions in FLuid Attenuated Inversion Recovery (FLAIR) brain MRI data. We are evaluating two pre-trained RIM networks, one trained on T1 brain data and another trained on T2 knee data. This evaluation was done by comparing the two networks to CS in terms of the average relative Signal-to-Noise Ratio (SNR) and Contrast Resolution (CR) that is achieved on 15 datasets acquired from Multiple Sclerosis patients. From these comparisons it shows that the network trained on T2 knee data performs similar to CS in terms of the relative SNR, while having a higher CR. The network trained on T1 brain data has both a lower relative SNR and CR, compared to CS. The data suggest that the RIM trained on T2 knee data is robust to the inclusion of lesions in an area that the network was not trained on.