Compressed sensing for phase-contrast computed tomography

Modern X-ray techniques opened the possibility to reconstruct phase contrast (PC) information. This provides significantly improved soft-tissue contrast when compared to conventional computed tomography (CT). While PCCT significantly ameliorates contrast information, radiation dose continues to be an issue when translated to the clinic. Possible dose reduction can be achieved by using more efficient reconstruction algorithms. In this work, dose reduction is achieved by applying a compressed sensing (CS) reconstruction to a highly sparse set of PCCT projections. The applied reconstruction algorithm is based on a non-uniform fast Fourier transform (NUFFT), where sparse sets of projections are reconstructed with a CS algorithm, employing wavelet domain sparsity and finite differences minimization. We evaluated this approach with both phantom and real data. Measured data from a conventional X-ray source were acquired using grating-based interferometry. The resulting reconstructions are compared visually, and quantitatively on the basis of standard deviation within different regions-of-interest. The assessment of phantom and measured data demonstrated the possibility to reconstruct from drastically fewer projections than the Nyquist-theorem demands. The measured standard deviations were comparable or even lower compared to full dose reconstructions. In this initial evaluation of CS-based methods in PCCT, we presented a considerable reduction of necessary projections. Thus, radiation dose can be reduced while maintaining the superior soft-tissue contrast and image quality of PCCT. In the future, approaches such as the presented, will enable 4D PCCT, for instance in cardiac applications.