Quantitative magnetization transfer (qMT) is a Z-spectrum based imaging technique used to study white matter. The need to acquire many images with unique RF saturation pulses leads to long acquisition times. We aim to shorten qMT imaging times using a sparseSENSE technique that combines parallel imaging and compressed sensing to reduce the amount of acquired data. Retrospectively undersampled data was reconstructed for a range of acceleration factors using wavelet and total variation sparsifying domains. Pool size ratio (F) maps were accelerated by a factor of 4×, and acceleration factors of 8-12× may be possible in future work.