This paper presents a new method for tomographic reconstruction of volumes from sparse observational data. Application scenarios can be found in astrophysics, plasma physics, or whenever the amount of obtainable measurement is limited. In the extreme only a single view of the phenomenon may be available. Our method uses input image data together with complex, user-definable assumptions about 3D density distributions. The parameter values of the user-defined model are fitted to the input image. This allows for incorporating complex, data-driven assumptions, such as helical symmetry, into the reconstruction process. We present two different sparsity-based reconstruction approaches. For the first method, novel virtual views are generated prior to tomography reconstruction. In the second method, voxel groups of similar target densities are defined and used for group sparsity reconstruction. We evaluate our method on real data of a high-energy plasma experiment and show that the reconstruction is consistent with the available measurement and 3D density assumptions. An additional experiment on simulated data demonstrates possible gains when adding an additional view to the presented reconstruction methods.
Compressed sensing (CS) has been exploited for accelerating data acquisition in magnetic resonance imaging (MRI). MR images can be then reconstructed from significantly fewer measurements, i.e., drastically lower than that required by the Nyquist sampling criterion. However, the compressed sensing method usually produces images with artifacts, particularly at high reduction rates. In this paper, we propose a novel compressed sensing MRI method, called CS-NLTV that exploits curvelet sparsity (CS) and nonlocal total variation (NLTV) regularization. The curvelet transform is optimal sparsifying transform with the excellent directional sensitivity than that of wavelet transform. The NLTV, on the other hand extends the total variation regularizer to a nonlocal variant which can preserve both textures and structures and produce sharper images. We have explored a new approach of combining alternating direction method of multiplier (ADMM), adaptive weighting, and splitting variables technique to solve the formulated optimization problem. The proposed CSNLTV method is evaluated experimentally and compared to the previously reported high performance methods. Results demonstrate a significant improvement of compressed MR image reconstruction on four medical MRI datasets.