T2 prime mapping from highly undersampled data using compressed sensing with patch based low rank penalty

摘要

In magnetic resonance (MR) imaging, T2 and T2 star (T2∗) relaxation times represent tissue properties, which can be quantified by specific imaging sequences. Especially, T2 prime (T2') that can be derived from T2 and T2∗ are clinically valuable for delineation of areas with increased oxygen extraction fraction in acute stroke. However, there are limitations in this method because it requires acquisition of many images for the generation of T2 and T2∗ relaxation time maps. In particular, time saving is the most important factor in acquisition of MRI in acute ischemic stroke because therapy should be given to patients as soon as possible. Therefore, to reduce the acquisition time of MR data, we use a compressed sensing algorithm using patch based low rank penalty for the reconstruction of T2 and T2∗ weighted images to obtain the T2 prime map. Our results showed that significant acceleration in T2' image acquisition is possible using the proposed method.