Constrained Reconstruction of Sparse Cardiac MR DTI Data

Abstract

Magnetic resonance diffusion tensor imaging (DTI) has emerged as a convenient and reliable alternative to conventional histology for characterizing the fiber structure of the myocardium. The acquisition of full data for different diffusion directions for a large number of slices often takes a long time and results in trade-offs in the number of slices and signal to noise ratios. We propose a constrained reconstruction technique based on a regularization framework to jointly reconstruct sparse sets of cardiac DTI data. Constraints on spatial variation and directional variation were used in the reconstruction. The method was tested on sparse data undersampled in both rectilinear and (simulated) radial fashions and compared to reconstructions from full data. The method provided reasonable reconstructions with half of the data for rectilinear undersampling and similar quality images with a quarter of the data if radial undersampling was used.