Single Anisotropic 3-D MR Image Upsampling via Overcomplete Dictionary Trained From In-Plane High Resolution Slices

In magnetic resonance (MR), hardware limitation, scanning time, and patient comfort often result in the acquisition of anisotropic 3-D MR images. Enhancing image resolution is desired but has been very challenging in medical image processing. Super resolution reconstruction based on sparse representation and overcomplete dictionary has been lately employed to address this problem; however, these methods require extra training sets, which may not be always available. This paper proposes a novel single anisotropic 3-D MR image upsampling method via sparse representation and overcomplete dictionary that is trained from in-plane high resolution slices to upsample in the out-of-plane dimensions. The proposed method, therefore, does not require extra training sets. Abundant experiments, conducted on simulated and clinical brain MR images, show that the proposed method is more accurate than classical interpolation. When compared to a recent upsampling method based on the nonlocal means approach, the proposed method did not show improved results at low upsampling factors with simulated images, but generated comparable results with much better computational efficiency in clinical cases. Therefore, the proposed approach can be efficiently implemented and routinely used to upsample MR images in the out-of-planes views for radiologic assessment and postacquisition processing.