Paramagnetic rim lesions (PRL) in multiple sclerosis are chronic inflammatory lesions depicted in susceptibility-based MRI where high PRL lesion burden has been associated with a more aggressive disease course. As their visual detection is subjective and time-consuming, a convolutional neural network (RimNet) has recently been developed and applied to 3T susceptibility contrast MR images. In this work, we evaluate a unimodal RimNet architecture based on either the MP2RAGE uniform contrast or the concurrently obtained T1 map at both 3T and 7T. Results show that prediction improves considerably at 7T, suggesting that 7T MP2RAGE might be helpful for automatically identifying PRL.