Quantitative magnetization transfer (qMT) imaging provides quantitative measures of magnetization transfer properties, but the method itself suffers from long acquisition and processing time. Previous research has looked into the application of deep learning to accelerate qMT imaging. Specifically, a network called qMTNet was proposed to accelerate both data acquisition and fitting. In this study, we propose qMTNet+, an improved version of qMTNet, that accomplishes both acceleration tasks as well as generation of missing data with a single residual network. Results showed that qMTNet+ improves the quality of generated MT images and fitted qMT parameters compared to qMTNet.