Self-prior image-guided MRI reconstruction with dictionary learning.

Abstract

A novel method, named self-prior image-guided MRI reconstruction with dictionary learning (SPIDLE), is developed to improve the performance of MR imaging with high acceleration rates. "self-prior" means that the prior image is obtained from the target image itself and any extra MRI scans are not needed. The proposed method integrates self-prior image constraint with compressed sensing (CS) and the dictionary learning (DL) technique. The self-prior image is a preliminary result reconstructed using the undersampled k-space measurements of the target image. Therefore, the self-prior image has similar structural features with the target image, and they match each other accurately. CS approach is applied to the residual error of the target image with the self-prior image, because the error image is much sparser than the target image. The split Bregman method is used to solve the proposed approach to promote fast convergence. For multicoil measurements, each coil image is reconstructed individually and the final result is produced as the square root of sum of squares (SOS) of all channel images. The performance of the proposed SPIDLE method was inspected using different undersampling schemes and acceleration rates with various types of invivo MR datasets. Experiments showed that the SPIDLE method is superior to other typical state-of-the-art methods. Specifically, the SPIDLE method produces fewer reconstruction errors, and it is robust to initialization. The proposed SPIDLE method substantially widens the applications of prior image-guided MRI reconstruction, especially for applications that are not suitable to use existing MR scans as prior images. The SPIDLE method obviously improves the reconstruction quality for highly undersampled MRI. It is also promising for reconstruction of dynamic MRI and other imaging modalities, such as CT and CT-MRI multimodality imaging.